fix linking issue with manage_caching_sizes_second_if_negative

(transplanted from 8dd3ae282d
)

.clang-format

@@ -1,17 +0,0 @@
----
-BasedOnStyle: Google
-ColumnLimit:  120
----
-Language:     Cpp
-BasedOnStyle: Google
-ColumnLimit:  120
-StatementMacros:
-  - EIGEN_STATIC_ASSERT
-SortIncludes: false
-AttributeMacros:
-- EIGEN_STRONG_INLINE
-- EIGEN_ALWAYS_INLINE
-- EIGEN_DEVICE_FUNC
-- EIGEN_DONT_INLINE
-- EIGEN_DEPRECATED
-- EIGEN_UNUSED

.git-blame-ignore-revs

@@ -1,4 +0,0 @@
-# First major clang-format MR (https://gitlab.com/libeigen/eigen/-/merge_requests/1429).
-f38e16c193d489c278c189bc06b448a94adb45fb
-# Formatting of tests, examples, benchmarks, et cetera (https://gitlab.com/libeigen/eigen/-/merge_requests/1432).
-46e9cdb7fea25d7f7aef4332b9c3ead3857e213d

.gitlab-ci.yml

@@ -1,31 +0,0 @@
-# This file is part of Eigen, a lightweight C++ template library
-# for linear algebra.
-#
-# Copyright (C) 2023, The Eigen Authors
-#
-# This Source Code Form is subject to the terms of the Mozilla
-# Public License v. 2.0. If a copy of the MPL was not distributed
-# with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-stages:
-  - checkformat
-  - build
-  - test
-
-variables:
-  # CMake build directory.
-  EIGEN_CI_BUILDDIR: .build
-  # Specify the CMake build target.
-  EIGEN_CI_BUILD_TARGET: ""
-  # If a test regex is specified, that will be selected.
-  # Otherwise, we will try a label if specified.
-  EIGEN_CI_TEST_REGEX: ""
-  EIGEN_CI_TEST_LABEL: ""
-
-include:
-  - "/ci/checkformat.gitlab-ci.yml"
-  - "/ci/common.gitlab-ci.yml"
-  - "/ci/build.linux.gitlab-ci.yml"
-  - "/ci/build.windows.gitlab-ci.yml"
-  - "/ci/test.linux.gitlab-ci.yml"
-  - "/ci/test.windows.gitlab-ci.yml"

.gitlab/issue_templates/Bug Report.md

@@ -1,69 +0,0 @@
-<!--
-Please read this!
-
-Before opening a new issue, make sure to search for keywords in the issues
-filtered by "bug::confirmed" or "bug::unconfirmed" and "bugzilla" label:
-
-- https://gitlab.com/libeigen/eigen/-/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=bug%3A%3Aconfirmed
-- https://gitlab.com/libeigen/eigen/-/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=bug%3A%3Aunconfirmed
-- https://gitlab.com/libeigen/eigen/-/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=bugzilla
-
-and verify the issue you're about to submit isn't a duplicate. -->
-
-### Summary
-<!-- Summarize the bug encountered concisely. -->
-
-### Environment
-<!-- Please provide your development environment here -->
-- **Operating System** : Windows/Linux
-- **Architecture** : x64/Arm64/PowerPC ...
-- **Eigen Version** : 3.3.9
-- **Compiler Version** : Gcc7.0
-- **Compile Flags** : -O3 -march=native
-- **Vector Extension** : SSE/AVX/NEON ...
-
-### Minimal Example
-<!-- If possible, please create a minimal example here that exhibits the problematic behavior.
-You can also link to [godbolt](https://godbolt.org). But please note that you need to click 
-the "Share" button in the top right-hand corner of the godbolt page where you reproduce the sample 
-code to get the share link instead of in your browser address bar. 
-
-You can read [the guidelines on stackoverflow](https://stackoverflow.com/help/minimal-reproducible-example)
-on how to create a good minimal example. -->
-
-```cpp
-//show your code here
-```
-
-### Steps to reproduce
-<!-- Describe how one can reproduce the issue - this is very important. Please use an ordered list. -->
-
-1. first step
-2. second step
-3. ... 
-
-### What is the current *bug* behavior?
-<!-- Describe what actually happens. -->
-
-### What is the expected *correct* behavior?
-<!-- Describe what you should see instead. -->
-
-### Relevant logs
-<!-- Add relevant code snippets or program output within blocks marked by " ``` " -->
-
-<!-- OPTIONAL: remove this section if you are not reporting a compilation warning issue.-->
-### Warning Messages
-<!-- Show us the warning messages you got! -->
-
-<!-- OPTIONAL: remove this section if you are not reporting a performance issue. -->
-### Benchmark scripts and results
-<!-- Please share any benchmark scripts - either standalone, or using [Google Benchmark](https://github.com/google/benchmark). -->
-
-### Anything else that might help
-<!-- It will be better to provide us more information to help narrow down the cause. 
-Including but not limited to the following: 
-- lines of code that might help us diagnose the problem. 
-- potential ways to address the issue.
-- last known working/first broken version (release number or commit hash). --> 
-
-- [ ] Have a plan to fix this issue.

.gitlab/issue_templates/Feature Request.md

@@ -1,7 +0,0 @@
-### Describe the feature you would like to be implemented.
-
-### Would such a feature be useful for other users? Why?
-
-### Any hints on how to implement the requested feature?
-
-### Additional resources

.gitlab/merge_request_templates/Merge Request Template.md

@@ -1,26 +0,0 @@
-<!-- 
-Thanks for contributing a merge request! Please name and fully describe your MR as you would for a commit message.
-If the MR fixes an issue, please include "Fixes #issue" in the commit message and the MR description.
-
-In addition, we recommend that first-time contributors read our [contribution guidelines](https://eigen.tuxfamily.org/index.php?title=Contributing_to_Eigen) and [git page](https://eigen.tuxfamily.org/index.php?title=Git), which will help you submit a more standardized MR.
-
-Before submitting the MR, you also need to complete the following checks:
-- Make one PR per feature/bugfix (don't mix multiple changes into one PR). Avoid committing unrelated changes.
-- Rebase before committing
-- For code changes, run the test suite (at least the tests that are likely affected by the change).
-  See our [test guidelines](https://eigen.tuxfamily.org/index.php?title=Tests).
-- If possible, add a test (both for bug-fixes as well as new features)
-- Make sure new features are documented
-
-Note that we are a team of volunteers; we appreciate your patience during the review process.
-
-Again, thanks for contributing! -->
-
-### Reference issue
-<!-- You can link to a specific issue using the gitlab syntax #<issue number>  -->
-
-### What does this implement/fix?
-<!--Please explain your changes.-->
-
-### Additional information
-<!--Any additional information you think is important.-->

.hgeol

@@ -1,11 +1,3 @@
-[patterns]
-*.sh = LF
-*.MINPACK = CRLF
-scripts/*.in = LF
-debug/msvc/*.dat = CRLF
-debug/msvc/*.natvis = CRLF
-unsupported/test/mpreal/*.* = CRLF
-** = native
-
-[repository]
-native = LF
+[patterns]
+**.* = native
+eigen_autoexp_part.dat = CRLF

.hgignore

@@ -1,3 +1,4 @@
+syntax: glob
 qrc_*cxx
 *.orig
 *.pyc
@@ -12,7 +13,7 @@ core
 core.*
 *.bak
 *~
-*.build*
+build*
 *.moc.*
 *.moc
 ui_*
@@ -27,15 +28,5 @@ activity.png
 *.rej
 log
 patch
-*.patch
 a
 a.*
-lapack/testing
-lapack/reference
-.*project
-.settings
-Makefile
-!ci/build.gitlab-ci.yml
-!scripts/buildtests.in
-!Eigen/Core
-!Eigen/src/Core

.krazy

@@ -0,0 +1,3 @@
+SKIP /disabled/
+SKIP /bench/
+SKIP /build/

COPYING.APACHE

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COPYING.BSD

@@ -1,26 +0,0 @@
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COPYING.GPL

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
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+                     END OF TERMS AND CONDITIONS
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+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

COPYING.LGPL

@@ -0,0 +1,165 @@
+                  GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+  This version of the GNU Lesser General Public License incorporates
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+
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+       Library.  A suitable mechanism is one that (a) uses at run time
+       a copy of the Library already present on the user's computer
+       system, and (b) will operate properly with a modified version
+       of the Library that is interface-compatible with the Linked
+       Version. 
+
+   e) Provide Installation Information, but only if you would otherwise
+   be required to provide such information under section 6 of the
+   GNU GPL, and only to the extent that such information is
+   necessary to install and execute a modified version of the
+   Combined Work produced by recombining or relinking the
+   Application with a modified version of the Linked Version. (If
+   you use option 4d0, the Installation Information must accompany
+   the Minimal Corresponding Source and Corresponding Application
+   Code. If you use option 4d1, you must provide the Installation
+   Information in the manner specified by section 6 of the GNU GPL
+   for conveying Corresponding Source.)
+
+  5. Combined Libraries.
+
+  You may place library facilities that are a work based on the
+Library side by side in a single library together with other library
+facilities that are not Applications and are not covered by this
+License, and convey such a combined library under terms of your
+choice, if you do both of the following:
+
+   a) Accompany the combined library with a copy of the same work based
+   on the Library, uncombined with any other library facilities,
+   conveyed under the terms of this License.
+
+   b) Give prominent notice with the combined library that part of it
+   is a work based on the Library, and explaining where to find the
+   accompanying uncombined form of the same work.
+
+  6. Revised Versions of the GNU Lesser General Public License.
+
+  The Free Software Foundation may publish revised and/or new versions
+of the GNU Lesser General Public License from time to time. Such new
+versions will be similar in spirit to the present version, but may
+differ in detail to address new problems or concerns.
+
+  Each version is given a distinguishing version number. If the
+Library as you received it specifies that a certain numbered version
+of the GNU Lesser General Public License "or any later version"
+applies to it, you have the option of following the terms and
+conditions either of that published version or of any later version
+published by the Free Software Foundation. If the Library as you
+received it does not specify a version number of the GNU Lesser
+General Public License, you may choose any version of the GNU Lesser
+General Public License ever published by the Free Software Foundation.
+
+  If the Library as you received it specifies that a proxy can decide
+whether future versions of the GNU Lesser General Public License shall
+apply, that proxy's public statement of acceptance of any version is
+permanent authorization for you to choose that version for the
+Library.

COPYING.MINPACK

@@ -1,51 +0,0 @@
-Minpack Copyright Notice (1999) University of Chicago.  All rights reserved
-
-Redistribution and use in source and binary forms, with or
-without modification, are permitted provided that the
-following conditions are met:
-
-1. Redistributions of source code must retain the above
-copyright notice, this list of conditions and the following
-disclaimer.
-
-2. Redistributions in binary form must reproduce the above
-copyright notice, this list of conditions and the following
-disclaimer in the documentation and/or other materials
-provided with the distribution.
-
-3. The end-user documentation included with the
-redistribution, if any, must include the following
-acknowledgment:
-
-   "This product includes software developed by the
-   University of Chicago, as Operator of Argonne National
-   Laboratory.
-
-Alternately, this acknowledgment may appear in the software
-itself, if and wherever such third-party acknowledgments
-normally appear.
-
-4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
-WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
-UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
-THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
-OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
-OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
-OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
-USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
-THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
-DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
-UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
-BE CORRECTED.
-
-5. LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
-HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
-ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
-INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
-ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
-PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
-SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
-(INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
-EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
-POSSIBILITY OF SUCH LOSS OR DAMAGES.

COPYING.MPL2

@@ -1,373 +0,0 @@
-Mozilla Public License Version 2.0
-==================================
-
-1. Definitions
---------------
-
-1.1. "Contributor"
-    means each individual or legal entity that creates, contributes to
-    the creation of, or owns Covered Software.
-
-1.2. "Contributor Version"
-    means the combination of the Contributions of others (if any) used
-    by a Contributor and that particular Contributor's Contribution.
-
-1.3. "Contribution"
-    means Covered Software of a particular Contributor.
-
-1.4. "Covered Software"
-    means Source Code Form to which the initial Contributor has attached
-    the notice in Exhibit A, the Executable Form of such Source Code
-    Form, and Modifications of such Source Code Form, in each case
-    including portions thereof.
-
-1.5. "Incompatible With Secondary Licenses"
-    means
-
-    (a) that the initial Contributor has attached the notice described
-        in Exhibit B to the Covered Software; or
-
-    (b) that the Covered Software was made available under the terms of
-        version 1.1 or earlier of the License, but not also under the
-        terms of a Secondary License.
-
-1.6. "Executable Form"
-    means any form of the work other than Source Code Form.
-
-1.7. "Larger Work"
-    means a work that combines Covered Software with other material, in 
-    a separate file or files, that is not Covered Software.
-
-1.8. "License"
-    means this document.
-
-1.9. "Licensable"
-    means having the right to grant, to the maximum extent possible,
-    whether at the time of the initial grant or subsequently, any and
-    all of the rights conveyed by this License.
-
-1.10. "Modifications"
-    means any of the following:
-
-    (a) any file in Source Code Form that results from an addition to,
-        deletion from, or modification of the contents of Covered
-        Software; or
-
-    (b) any new file in Source Code Form that contains any Covered
-        Software.
-
-1.11. "Patent Claims" of a Contributor
-    means any patent claim(s), including without limitation, method,
-    process, and apparatus claims, in any patent Licensable by such
-    Contributor that would be infringed, but for the grant of the
-    License, by the making, using, selling, offering for sale, having
-    made, import, or transfer of either its Contributions or its
-    Contributor Version.
-
-1.12. "Secondary License"
-    means either the GNU General Public License, Version 2.0, the GNU
-    Lesser General Public License, Version 2.1, the GNU Affero General
-    Public License, Version 3.0, or any later versions of those
-    licenses.
-
-1.13. "Source Code Form"
-    means the form of the work preferred for making modifications.
-
-1.14. "You" (or "Your")
-    means an individual or a legal entity exercising rights under this
-    License. For legal entities, "You" includes any entity that
-    controls, is controlled by, or is under common control with You. For
-    purposes of this definition, "control" means (a) the power, direct
-    or indirect, to cause the direction or management of such entity,
-    whether by contract or otherwise, or (b) ownership of more than
-    fifty percent (50%) of the outstanding shares or beneficial
-    ownership of such entity.
-
-2. License Grants and Conditions
---------------------------------
-
-2.1. Grants
-
-Each Contributor hereby grants You a world-wide, royalty-free,
-non-exclusive license:
-
-(a) under intellectual property rights (other than patent or trademark)
-    Licensable by such Contributor to use, reproduce, make available,
-    modify, display, perform, distribute, and otherwise exploit its
-    Contributions, either on an unmodified basis, with Modifications, or
-    as part of a Larger Work; and
-
-(b) under Patent Claims of such Contributor to make, use, sell, offer
-    for sale, have made, import, and otherwise transfer either its
-    Contributions or its Contributor Version.
-
-2.2. Effective Date
-
-The licenses granted in Section 2.1 with respect to any Contribution
-become effective for each Contribution on the date the Contributor first
-distributes such Contribution.
-
-2.3. Limitations on Grant Scope
-
-The licenses granted in this Section 2 are the only rights granted under
-this License. No additional rights or licenses will be implied from the
-distribution or licensing of Covered Software under this License.
-Notwithstanding Section 2.1(b) above, no patent license is granted by a
-Contributor:
-
-(a) for any code that a Contributor has removed from Covered Software;
-    or
-
-(b) for infringements caused by: (i) Your and any other third party's
-    modifications of Covered Software, or (ii) the combination of its
-    Contributions with other software (except as part of its Contributor
-    Version); or
-
-(c) under Patent Claims infringed by Covered Software in the absence of
-    its Contributions.
-
-This License does not grant any rights in the trademarks, service marks,
-or logos of any Contributor (except as may be necessary to comply with
-the notice requirements in Section 3.4).
-
-2.4. Subsequent Licenses
-
-No Contributor makes additional grants as a result of Your choice to
-distribute the Covered Software under a subsequent version of this
-License (see Section 10.2) or under the terms of a Secondary License (if
-permitted under the terms of Section 3.3).
-
-2.5. Representation
-
-Each Contributor represents that the Contributor believes its
-Contributions are its original creation(s) or it has sufficient rights
-to grant the rights to its Contributions conveyed by this License.
-
-2.6. Fair Use
-
-This License is not intended to limit any rights You have under
-applicable copyright doctrines of fair use, fair dealing, or other
-equivalents.
-
-2.7. Conditions
-
-Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
-in Section 2.1.
-
-3. Responsibilities
--------------------
-
-3.1. Distribution of Source Form
-
-All distribution of Covered Software in Source Code Form, including any
-Modifications that You create or to which You contribute, must be under
-the terms of this License. You must inform recipients that the Source
-Code Form of the Covered Software is governed by the terms of this
-License, and how they can obtain a copy of this License. You may not
-attempt to alter or restrict the recipients' rights in the Source Code
-Form.
-
-3.2. Distribution of Executable Form
-
-If You distribute Covered Software in Executable Form then:
-
-(a) such Covered Software must also be made available in Source Code
-    Form, as described in Section 3.1, and You must inform recipients of
-    the Executable Form how they can obtain a copy of such Source Code
-    Form by reasonable means in a timely manner, at a charge no more
-    than the cost of distribution to the recipient; and
-
-(b) You may distribute such Executable Form under the terms of this
-    License, or sublicense it under different terms, provided that the
-    license for the Executable Form does not attempt to limit or alter
-    the recipients' rights in the Source Code Form under this License.
-
-3.3. Distribution of a Larger Work
-
-You may create and distribute a Larger Work under terms of Your choice,
-provided that You also comply with the requirements of this License for
-the Covered Software. If the Larger Work is a combination of Covered
-Software with a work governed by one or more Secondary Licenses, and the
-Covered Software is not Incompatible With Secondary Licenses, this
-License permits You to additionally distribute such Covered Software
-under the terms of such Secondary License(s), so that the recipient of
-the Larger Work may, at their option, further distribute the Covered
-Software under the terms of either this License or such Secondary
-License(s).
-
-3.4. Notices
-
-You may not remove or alter the substance of any license notices
-(including copyright notices, patent notices, disclaimers of warranty,
-or limitations of liability) contained within the Source Code Form of
-the Covered Software, except that You may alter any license notices to
-the extent required to remedy known factual inaccuracies.
-
-3.5. Application of Additional Terms
-
-You may choose to offer, and to charge a fee for, warranty, support,
-indemnity or liability obligations to one or more recipients of Covered
-Software. However, You may do so only on Your own behalf, and not on
-behalf of any Contributor. You must make it absolutely clear that any
-such warranty, support, indemnity, or liability obligation is offered by
-You alone, and You hereby agree to indemnify every Contributor for any
-liability incurred by such Contributor as a result of warranty, support,
-indemnity or liability terms You offer. You may include additional
-disclaimers of warranty and limitations of liability specific to any
-jurisdiction.
-
-4. Inability to Comply Due to Statute or Regulation
----------------------------------------------------
-
-If it is impossible for You to comply with any of the terms of this
-License with respect to some or all of the Covered Software due to
-statute, judicial order, or regulation then You must: (a) comply with
-the terms of this License to the maximum extent possible; and (b)
-describe the limitations and the code they affect. Such description must
-be placed in a text file included with all distributions of the Covered
-Software under this License. Except to the extent prohibited by statute
-or regulation, such description must be sufficiently detailed for a
-recipient of ordinary skill to be able to understand it.
-
-5. Termination
---------------
-
-5.1. The rights granted under this License will terminate automatically
-if You fail to comply with any of its terms. However, if You become
-compliant, then the rights granted under this License from a particular
-Contributor are reinstated (a) provisionally, unless and until such
-Contributor explicitly and finally terminates Your grants, and (b) on an
-ongoing basis, if such Contributor fails to notify You of the
-non-compliance by some reasonable means prior to 60 days after You have
-come back into compliance. Moreover, Your grants from a particular
-Contributor are reinstated on an ongoing basis if such Contributor
-notifies You of the non-compliance by some reasonable means, this is the
-first time You have received notice of non-compliance with this License
-from such Contributor, and You become compliant prior to 30 days after
-Your receipt of the notice.
-
-5.2. If You initiate litigation against any entity by asserting a patent
-infringement claim (excluding declaratory judgment actions,
-counter-claims, and cross-claims) alleging that a Contributor Version
-directly or indirectly infringes any patent, then the rights granted to
-You by any and all Contributors for the Covered Software under Section
-2.1 of this License shall terminate.
-
-5.3. In the event of termination under Sections 5.1 or 5.2 above, all
-end user license agreements (excluding distributors and resellers) which
-have been validly granted by You or Your distributors under this License
-prior to termination shall survive termination.
-
-************************************************************************
-*                                                                      *
-*  6. Disclaimer of Warranty                                           *
-*  -------------------------                                           *
-*                                                                      *
-*  Covered Software is provided under this License on an "as is"       *
-*  basis, without warranty of any kind, either expressed, implied, or  *
-*  statutory, including, without limitation, warranties that the       *
-*  Covered Software is free of defects, merchantable, fit for a        *
-*  particular purpose or non-infringing. The entire risk as to the     *
-*  quality and performance of the Covered Software is with You.        *
-*  Should any Covered Software prove defective in any respect, You     *
-*  (not any Contributor) assume the cost of any necessary servicing,   *
-*  repair, or correction. This disclaimer of warranty constitutes an   *
-*  essential part of this License. No use of any Covered Software is   *
-*  authorized under this License except under this disclaimer.         *
-*                                                                      *
-************************************************************************
-
-************************************************************************
-*                                                                      *
-*  7. Limitation of Liability                                          *
-*  --------------------------                                          *
-*                                                                      *
-*  Under no circumstances and under no legal theory, whether tort      *
-*  (including negligence), contract, or otherwise, shall any           *
-*  Contributor, or anyone who distributes Covered Software as          *
-*  permitted above, be liable to You for any direct, indirect,         *
-*  special, incidental, or consequential damages of any character      *
-*  including, without limitation, damages for lost profits, loss of    *
-*  goodwill, work stoppage, computer failure or malfunction, or any    *
-*  and all other commercial damages or losses, even if such party      *
-*  shall have been informed of the possibility of such damages. This   *
-*  limitation of liability shall not apply to liability for death or   *
-*  personal injury resulting from such party's negligence to the       *
-*  extent applicable law prohibits such limitation. Some               *
-*  jurisdictions do not allow the exclusion or limitation of           *
-*  incidental or consequential damages, so this exclusion and          *
-*  limitation may not apply to You.                                    *
-*                                                                      *
-************************************************************************
-
-8. Litigation
--------------
-
-Any litigation relating to this License may be brought only in the
-courts of a jurisdiction where the defendant maintains its principal
-place of business and such litigation shall be governed by laws of that
-jurisdiction, without reference to its conflict-of-law provisions.
-Nothing in this Section shall prevent a party's ability to bring
-cross-claims or counter-claims.
-
-9. Miscellaneous
-----------------
-
-This License represents the complete agreement concerning the subject
-matter hereof. If any provision of this License is held to be
-unenforceable, such provision shall be reformed only to the extent
-necessary to make it enforceable. Any law or regulation which provides
-that the language of a contract shall be construed against the drafter
-shall not be used to construe this License against a Contributor.
-
-10. Versions of the License
----------------------------
-
-10.1. New Versions
-
-Mozilla Foundation is the license steward. Except as provided in Section
-10.3, no one other than the license steward has the right to modify or
-publish new versions of this License. Each version will be given a
-distinguishing version number.
-
-10.2. Effect of New Versions
-
-You may distribute the Covered Software under the terms of the version
-of the License under which You originally received the Covered Software,
-or under the terms of any subsequent version published by the license
-steward.
-
-10.3. Modified Versions
-
-If you create software not governed by this License, and you want to
-create a new license for such software, you may create and use a
-modified version of this License if you rename the license and remove
-any references to the name of the license steward (except to note that
-such modified license differs from this License).
-
-10.4. Distributing Source Code Form that is Incompatible With Secondary
-Licenses
-
-If You choose to distribute Source Code Form that is Incompatible With
-Secondary Licenses under the terms of this version of the License, the
-notice described in Exhibit B of this License must be attached.
-
-Exhibit A - Source Code Form License Notice
--------------------------------------------
-
-  This Source Code Form is subject to the terms of the Mozilla Public
-  License, v. 2.0. If a copy of the MPL was not distributed with this
-  file, You can obtain one at https://mozilla.org/MPL/2.0/.
-
-If it is not possible or desirable to put the notice in a particular
-file, then You may include the notice in a location (such as a LICENSE
-file in a relevant directory) where a recipient would be likely to look
-for such a notice.
-
-You may add additional accurate notices of copyright ownership.
-
-Exhibit B - "Incompatible With Secondary Licenses" Notice
----------------------------------------------------------
-
-  This Source Code Form is "Incompatible With Secondary Licenses", as
-  defined by the Mozilla Public License, v. 2.0.

COPYING.README

@@ -1,6 +0,0 @@
-Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
-  http://www.mozilla.org/MPL/2.0/
-  http://www.mozilla.org/MPL/2.0/FAQ.html
-
-Some files contain third-party code under BSD or other MPL2-compatible licenses,
-whence the other COPYING.* files here.

CTestConfig.cmake

@@ -2,16 +2,12 @@
 ## Then modify the CMakeLists.txt file in the root directory of your
 ## project to incorporate the testing dashboard.
 ## # The following are required to uses Dart and the Cdash dashboard
-##   enable_testing()
-##   include(CTest)
+##   ENABLE_TESTING()
+##   INCLUDE(CTest)
 set(CTEST_PROJECT_NAME "Eigen")
 set(CTEST_NIGHTLY_START_TIME "00:00:00 UTC")
 
 set(CTEST_DROP_METHOD "http")
-set(CTEST_DROP_SITE "my.cdash.org")
-set(CTEST_DROP_LOCATION "/submit.php?project=Eigen")
+set(CTEST_DROP_SITE "eigen.tuxfamily.org")
+set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
 set(CTEST_DROP_SITE_CDASH TRUE)
-#set(CTEST_PROJECT_SUBPROJECTS
-#Official
-#Unsupported
-#)

CTestCustom.cmake.in

@@ -1,4 +1,4 @@
 
-set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "2000")
-set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS   "2000")
-list(APPEND CTEST_CUSTOM_ERROR_EXCEPTION    @EIGEN_CTEST_ERROR_EXCEPTION@)
+## A tribute to Dynamic!
+set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "33331")
+set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "33331")

Eigen/AccelerateSupport

@@ -1,52 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
-#define EIGEN_ACCELERATESUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \ingroup Support_modules
- * \defgroup AccelerateSupport_Module AccelerateSupport module
- *
- * This module provides an interface to the Apple Accelerate library.
- * It provides the seven following main factorization classes:
- * - class AccelerateLLT: a Cholesky (LL^T) factorization.
- * - class AccelerateLDLT: the default LDL^T factorization.
- * - class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
- * - class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
- * - class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
- * - class AccelerateQR: a QR factorization
- * - class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
- *
- * \code
- * #include <Eigen/AccelerateSupport>
- * \endcode
- *
- * In order to use this module, the Accelerate headers must be accessible from
- * the include paths, and your binary must be linked to the Accelerate framework.
- * The Accelerate library is only available on Apple hardware.
- *
- * Note that many of the algorithms can be influenced by the UpLo template
- * argument. All matrices are assumed to be symmetric. For example, the following
- * creates an LDLT factorization where your matrix is symmetric (implicit) and
- * uses the lower triangle:
- *
- * \code
- * AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;
- * \endcode
- */
-
-// IWYU pragma: begin_exports
-#include "src/AccelerateSupport/AccelerateSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_ACCELERATESUPPORT_MODULE_H

Eigen/Array

@@ -0,0 +1,11 @@
+#ifndef EIGEN_ARRAY_MODULE_H
+#define EIGEN_ARRAY_MODULE_H
+
+// include Core first to handle Eigen2 support macros
+#include "Core"
+
+#ifndef EIGEN2_SUPPORT
+  #error The Eigen/Array header does no longer exist in Eigen3. All that functionality has moved to Eigen/Core.
+#endif
+
+#endif // EIGEN_ARRAY_MODULE_H

Eigen/CMakeLists.txt

@@ -0,0 +1,19 @@
+include(RegexUtils)
+test_escape_string_as_regex()
+
+file(GLOB Eigen_directory_files "*")
+
+escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
+
+foreach(f ${Eigen_directory_files})
+  if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src")
+    list(APPEND Eigen_directory_files_to_install ${f})
+  endif()
+endforeach(f ${Eigen_directory_files})
+
+install(FILES
+  ${Eigen_directory_files_to_install}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
+  )
+
+add_subdirectory(src)

Eigen/Cholesky

@@ -1,43 +1,33 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_CHOLESKY_MODULE_H
 #define EIGEN_CHOLESKY_MODULE_H
 
 #include "Core"
-#include "Jacobi"
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
-/** \defgroup Cholesky_Module Cholesky module
- *
- *
- *
- * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
- * Those decompositions are also accessible via the following methods:
- *  - MatrixBase::llt()
- *  - MatrixBase::ldlt()
- *  - SelfAdjointView::llt()
- *  - SelfAdjointView::ldlt()
- *
- * \code
- * #include <Eigen/Cholesky>
- * \endcode
- */
+namespace Eigen {
 
-// IWYU pragma: begin_exports
+/** \defgroup Cholesky_Module Cholesky module
+  *
+  *
+  *
+  * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
+  * Those decompositions are accessible via the following MatrixBase methods:
+  *  - MatrixBase::llt(),
+  *  - MatrixBase::ldlt()
+  *
+  * \code
+  * #include <Eigen/Cholesky>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
 #include "src/Cholesky/LLT.h"
 #include "src/Cholesky/LDLT.h"
-#ifdef EIGEN_USE_LAPACKE
-#include "src/misc/lapacke_helpers.h"
-#include "src/Cholesky/LLT_LAPACKE.h"
-#endif
-// IWYU pragma: end_exports
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_CHOLESKY_MODULE_H
+#endif // EIGEN_CHOLESKY_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/CholmodSupport

@@ -1,48 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
-#define EIGEN_CHOLMODSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <cholmod.h>
-
-/** \ingroup Support_modules
- * \defgroup CholmodSupport_Module CholmodSupport module
- *
- * This module provides an interface to the Cholmod library which is part of the <a
- * href="http://www.suitesparse.com">suitesparse</a> package. It provides the two following main factorization classes:
- * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
- * - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of
- * the underlying factorization method (supernodal or simplicial).
- *
- * For the sake of completeness, this module also propose the two following classes:
- * - class CholmodSimplicialLLT
- * - class CholmodSimplicialLDLT
- * Note that these classes does not bring any particular advantage compared to the built-in
- * SimplicialLLT and SimplicialLDLT factorization classes.
- *
- * \code
- * #include <Eigen/CholmodSupport>
- * \endcode
- *
- * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be
- * linked to the cholmod library and its dependencies. The dependencies depend on how cholmod has been compiled. For a
- * cmake based project, you can use our FindCholmod.cmake module to help you in this task.
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/CholmodSupport/CholmodSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_CHOLMODSUPPORT_MODULE_H

Eigen/Core

@@ -4,72 +4,139 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
-#ifndef EIGEN_CORE_MODULE_H
-#define EIGEN_CORE_MODULE_H
+#ifndef EIGEN_CORE_H
+#define EIGEN_CORE_H
 
-// first thing Eigen does: stop the compiler from reporting useless warnings.
+// first thing Eigen does: stop the compiler from committing suicide
 #include "src/Core/util/DisableStupidWarnings.h"
 
 // then include this file where all our macros are defined. It's really important to do it first because
-// it's where we do all the compiler/OS/arch detections and define most defaults.
+// it's where we do all the alignment settings (platform detection and honoring the user's will if he
+// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
 #include "src/Core/util/Macros.h"
 
-// This detects SSE/AVX/NEON/etc. and configure alignment settings
-#include "src/Core/util/ConfigureVectorization.h"
-
-// We need cuda_runtime.h/hip_runtime.h to ensure that
-// the EIGEN_USING_STD macro works properly on the device side
-#if defined(EIGEN_CUDACC)
-#include <cuda_runtime.h>
-#elif defined(EIGEN_HIPCC)
-#include <hip/hip_runtime.h>
+// if alignment is disabled, then disable vectorization. Note: EIGEN_ALIGN is the proper check, it takes into
+// account both the user's will (EIGEN_DONT_ALIGN) and our own platform checks
+#if !EIGEN_ALIGN
+  #ifndef EIGEN_DONT_VECTORIZE
+    #define EIGEN_DONT_VECTORIZE
+  #endif
 #endif
 
-#ifdef EIGEN_EXCEPTIONS
-#include <new>
+#ifdef _MSC_VER
+  #include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
+  #if (_MSC_VER >= 1500) // 2008 or later
+    // Remember that usage of defined() in a #define is undefined by the standard.
+    // a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
+    #if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || defined(_M_X64)
+      #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
+    #endif
+  #endif
+#else
+  // Remember that usage of defined() in a #define is undefined by the standard
+  #if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
+    #define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
+  #endif
 #endif
 
-// Disable the ipa-cp-clone optimization flag with MinGW 6.x or older (enabled by default with -O3)
-// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
-#if EIGEN_COMP_MINGW && EIGEN_GNUC_STRICT_LESS_THAN(6, 0, 0)
-#pragma GCC optimize("-fno-ipa-cp-clone")
-#endif
+#ifndef EIGEN_DONT_VECTORIZE
 
-// Prevent ICC from specializing std::complex operators that silently fail
-// on device. This allows us to use our own device-compatible specializations
-// instead.
-#if EIGEN_COMP_ICC && defined(EIGEN_GPU_COMPILE_PHASE) && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
-#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
-#endif
-#include <complex>
+  #if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
 
-// this include file manages BLAS and MKL related macros
-// and inclusion of their respective header files
-#include "src/Core/util/MKL_support.h"
+    // Defines symbols for compile-time detection of which instructions are
+    // used.
+    // EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_SSE
+    #define EIGEN_VECTORIZE_SSE2
 
-#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-#define EIGEN_HAS_GPU_FP16
-#endif
+    // Detect sse3/ssse3/sse4:
+    // gcc and icc defines __SSE3__, ...
+    // there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
+    // want to force the use of those instructions with msvc.
+    #ifdef __SSE3__
+      #define EIGEN_VECTORIZE_SSE3
+    #endif
+    #ifdef __SSSE3__
+      #define EIGEN_VECTORIZE_SSSE3
+    #endif
+    #ifdef __SSE4_1__
+      #define EIGEN_VECTORIZE_SSE4_1
+    #endif
+    #ifdef __SSE4_2__
+      #define EIGEN_VECTORIZE_SSE4_2
+    #endif
 
-#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
-#define EIGEN_HAS_GPU_BF16
+    // include files
+
+    // This extern "C" works around a MINGW-w64 compilation issue
+    // https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
+    // In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
+    // However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
+    // with conflicting linkage.  The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
+    // so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
+    // notice that since these are C headers, the extern "C" is theoretically needed anyways.
+    extern "C" {
+      #include <emmintrin.h>
+      #include <xmmintrin.h>
+      #ifdef  EIGEN_VECTORIZE_SSE3
+      #include <pmmintrin.h>
+      #endif
+      #ifdef EIGEN_VECTORIZE_SSSE3
+      #include <tmmintrin.h>
+      #endif
+      #ifdef EIGEN_VECTORIZE_SSE4_1
+      #include <smmintrin.h>
+      #endif
+      #ifdef EIGEN_VECTORIZE_SSE4_2
+      #include <nmmintrin.h>
+      #endif
+    } // end extern "C"
+  #elif defined __ALTIVEC__
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_ALTIVEC
+    #include <altivec.h>
+    // We need to #undef all these ugly tokens defined in <altivec.h>
+    // => use __vector instead of vector
+    #undef bool
+    #undef vector
+    #undef pixel
+  #elif defined  __ARM_NEON__
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_NEON
+    #include <arm_neon.h>
+  #endif
 #endif
 
 #if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
-#define EIGEN_HAS_OPENMP
+  #define EIGEN_HAS_OPENMP
 #endif
 
 #ifdef EIGEN_HAS_OPENMP
-#include <atomic>
 #include <omp.h>
 #endif
 
 // MSVC for windows mobile does not have the errno.h file
-#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
+#if !(defined(_MSC_VER) && defined(_WIN32_WCE))
 #define EIGEN_HAS_ERRNO
 #endif
 
@@ -79,290 +146,187 @@
 #include <cstddef>
 #include <cstdlib>
 #include <cmath>
+#include <complex>
+#include <cassert>
 #include <functional>
-#ifndef EIGEN_NO_IO
-#include <sstream>
 #include <iosfwd>
-#endif
 #include <cstring>
 #include <string>
 #include <limits>
-#include <climits>  // for CHAR_BIT
+#include <climits> // for CHAR_BIT
 // for min/max:
 #include <algorithm>
 
-#include <array>
-#include <vector>
-
-// for std::is_nothrow_move_assignable
-#include <type_traits>
-
-// for std::this_thread::yield().
-#if !defined(EIGEN_USE_BLAS) && (defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL))
-#include <thread>
-#endif
-
 // for outputting debug info
 #ifdef EIGEN_DEBUG_ASSIGN
 #include <iostream>
 #endif
 
 // required for __cpuid, needs to be included after cmath
-// also required for _BitScanReverse on Windows on ARM
-#if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64) && !EIGEN_OS_WINCE
-#include <intrin.h>
+#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64))
+  #include <intrin.h>
 #endif
 
-#if defined(EIGEN_USE_SYCL)
-#undef min
-#undef max
-#undef isnan
-#undef isinf
-#undef isfinite
-#include <CL/sycl.hpp>
-#include <map>
-#include <memory>
-#include <thread>
-#include <utility>
-#ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
-#define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
-#endif
-#ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
-#define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
-#endif
+#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
+  #define EIGEN_EXCEPTIONS
 #endif
 
-#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || \
-    defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API ||  \
-    defined EIGEN2_SUPPORT
-// This will generate an error message:
-#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
+#ifdef EIGEN_EXCEPTIONS
+  #include <new>
 #endif
 
+// defined in bits/termios.h
+#undef B0
+
+/** \brief Namespace containing all symbols from the %Eigen library. */
 namespace Eigen {
 
-// we use size_t frequently and we'll never remember to prepend it with std:: every time just to
+inline static const char *SimdInstructionSetsInUse(void) {
+#if defined(EIGEN_VECTORIZE_SSE4_2)
+  return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_SSE4_1)
+  return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
+#elif defined(EIGEN_VECTORIZE_SSSE3)
+  return "SSE, SSE2, SSE3, SSSE3";
+#elif defined(EIGEN_VECTORIZE_SSE3)
+  return "SSE, SSE2, SSE3";
+#elif defined(EIGEN_VECTORIZE_SSE2)
+  return "SSE, SSE2";
+#elif defined(EIGEN_VECTORIZE_ALTIVEC)
+  return "AltiVec";
+#elif defined(EIGEN_VECTORIZE_NEON)
+  return "ARM NEON";
+#else
+  return "None";
+#endif
+}
+
+#define STAGE10_FULL_EIGEN2_API             10
+#define STAGE20_RESOLVE_API_CONFLICTS       20
+#define STAGE30_FULL_EIGEN3_API             30
+#define STAGE40_FULL_EIGEN3_STRICTNESS      40
+#define STAGE99_NO_EIGEN2_SUPPORT           99
+
+#if   defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
+  #define EIGEN2_SUPPORT
+  #define EIGEN2_SUPPORT_STAGE STAGE40_FULL_EIGEN3_STRICTNESS
+#elif defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
+  #define EIGEN2_SUPPORT
+  #define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
+#elif defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
+  #define EIGEN2_SUPPORT
+  #define EIGEN2_SUPPORT_STAGE STAGE20_RESOLVE_API_CONFLICTS
+#elif defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
+  #define EIGEN2_SUPPORT
+  #define EIGEN2_SUPPORT_STAGE STAGE10_FULL_EIGEN2_API
+#elif defined EIGEN2_SUPPORT
+  // default to stage 3, that's what it's always meant
+  #define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
+  #define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
+#else
+  #define EIGEN2_SUPPORT_STAGE STAGE99_NO_EIGEN2_SUPPORT
+#endif
+
+#ifdef EIGEN2_SUPPORT
+#undef minor
+#endif
+
+// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
 // ensure QNX/QCC support
 using std::size_t;
-// gcc 4.6.0 wants std:: for ptrdiff_t
+// gcc 4.6.0 wants std:: for ptrdiff_t 
 using std::ptrdiff_t;
 
-}  // namespace Eigen
-
 /** \defgroup Core_Module Core module
- * This is the main module of Eigen providing dense matrix and vector support
- * (both fixed and dynamic size) with all the features corresponding to a BLAS library
- * and much more...
- *
- * \code
- * #include <Eigen/Core>
- * \endcode
- */
+  * This is the main module of Eigen providing dense matrix and vector support
+  * (both fixed and dynamic size) with all the features corresponding to a BLAS library
+  * and much more...
+  *
+  * \code
+  * #include <Eigen/Core>
+  * \endcode
+  */
 
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#endif
-
-// IWYU pragma: begin_exports
 #include "src/Core/util/Constants.h"
-#include "src/Core/util/Meta.h"
-#include "src/Core/util/Assert.h"
 #include "src/Core/util/ForwardDeclarations.h"
-#include "src/Core/util/StaticAssert.h"
+#include "src/Core/util/Meta.h"
 #include "src/Core/util/XprHelper.h"
+#include "src/Core/util/StaticAssert.h"
 #include "src/Core/util/Memory.h"
-#include "src/Core/util/IntegralConstant.h"
-#include "src/Core/util/Serializer.h"
-#include "src/Core/util/SymbolicIndex.h"
-#include "src/Core/util/EmulateArray.h"
-#include "src/Core/util/MoreMeta.h"
 
 #include "src/Core/NumTraits.h"
 #include "src/Core/MathFunctions.h"
-#include "src/Core/RandomImpl.h"
 #include "src/Core/GenericPacketMath.h"
-#include "src/Core/MathFunctionsImpl.h"
-#include "src/Core/arch/Default/ConjHelper.h"
-// Generic half float support
-#include "src/Core/arch/Default/Half.h"
-#include "src/Core/arch/Default/BFloat16.h"
-#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
 
-#if defined EIGEN_VECTORIZE_AVX512
-#if defined EIGEN_VECTORIZE_AVX512FP16
-#include "src/Core/arch/AVX512/PacketMathFP16.h"
-#endif
-#include "src/Core/arch/SSE/PacketMath.h"
-#include "src/Core/arch/SSE/TypeCasting.h"
-#include "src/Core/arch/SSE/Complex.h"
-#include "src/Core/arch/AVX/PacketMath.h"
-#include "src/Core/arch/AVX/TypeCasting.h"
-#include "src/Core/arch/AVX/Complex.h"
-#include "src/Core/arch/AVX512/PacketMath.h"
-#include "src/Core/arch/AVX512/TypeCasting.h"
-#include "src/Core/arch/AVX512/Complex.h"
-#include "src/Core/arch/SSE/MathFunctions.h"
-#include "src/Core/arch/AVX/MathFunctions.h"
-#include "src/Core/arch/AVX512/MathFunctions.h"
-#include "src/Core/arch/AVX512/TrsmKernel.h"
-#elif defined EIGEN_VECTORIZE_AVX
-   // Use AVX for floats and doubles, SSE for integers
-#include "src/Core/arch/SSE/PacketMath.h"
-#include "src/Core/arch/SSE/TypeCasting.h"
-#include "src/Core/arch/SSE/Complex.h"
-#include "src/Core/arch/AVX/PacketMath.h"
-#include "src/Core/arch/AVX/TypeCasting.h"
-#include "src/Core/arch/AVX/Complex.h"
-#include "src/Core/arch/SSE/MathFunctions.h"
-#include "src/Core/arch/AVX/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_SSE
-#include "src/Core/arch/SSE/PacketMath.h"
-#include "src/Core/arch/SSE/TypeCasting.h"
-#include "src/Core/arch/SSE/MathFunctions.h"
-#include "src/Core/arch/SSE/Complex.h"
-#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
-#include "src/Core/arch/AltiVec/PacketMath.h"
-#include "src/Core/arch/AltiVec/TypeCasting.h"
-#include "src/Core/arch/AltiVec/MathFunctions.h"
-#include "src/Core/arch/AltiVec/Complex.h"
+#if defined EIGEN_VECTORIZE_SSE
+  #include "src/Core/arch/SSE/PacketMath.h"
+  #include "src/Core/arch/SSE/MathFunctions.h"
+  #include "src/Core/arch/SSE/Complex.h"
+#elif defined EIGEN_VECTORIZE_ALTIVEC
+  #include "src/Core/arch/AltiVec/PacketMath.h"
+  #include "src/Core/arch/AltiVec/Complex.h"
 #elif defined EIGEN_VECTORIZE_NEON
-#include "src/Core/arch/NEON/PacketMath.h"
-#include "src/Core/arch/NEON/TypeCasting.h"
-#include "src/Core/arch/NEON/MathFunctions.h"
-#include "src/Core/arch/NEON/Complex.h"
-#elif defined EIGEN_VECTORIZE_SVE
-#include "src/Core/arch/SVE/PacketMath.h"
-#include "src/Core/arch/SVE/TypeCasting.h"
-#include "src/Core/arch/SVE/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_ZVECTOR
-#include "src/Core/arch/ZVector/PacketMath.h"
-#include "src/Core/arch/ZVector/MathFunctions.h"
-#include "src/Core/arch/ZVector/Complex.h"
-#elif defined EIGEN_VECTORIZE_MSA
-#include "src/Core/arch/MSA/PacketMath.h"
-#include "src/Core/arch/MSA/MathFunctions.h"
-#include "src/Core/arch/MSA/Complex.h"
-#elif defined EIGEN_VECTORIZE_HVX
-#include "src/Core/arch/HVX/PacketMath.h"
-#endif
-
-#if defined EIGEN_VECTORIZE_GPU
-#include "src/Core/arch/GPU/PacketMath.h"
-#include "src/Core/arch/GPU/MathFunctions.h"
-#include "src/Core/arch/GPU/TypeCasting.h"
-#endif
-
-#if defined(EIGEN_USE_SYCL)
-#include "src/Core/arch/SYCL/InteropHeaders.h"
-#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
-#include "src/Core/arch/SYCL/PacketMath.h"
-#include "src/Core/arch/SYCL/MathFunctions.h"
-#include "src/Core/arch/SYCL/TypeCasting.h"
-#endif
+  #include "src/Core/arch/NEON/PacketMath.h"
+  #include "src/Core/arch/NEON/Complex.h"
 #endif
 
 #include "src/Core/arch/Default/Settings.h"
-// This file provides generic implementations valid for scalar as well
-#include "src/Core/arch/Default/GenericPacketMathFunctions.h"
 
-#include "src/Core/functors/TernaryFunctors.h"
-#include "src/Core/functors/BinaryFunctors.h"
-#include "src/Core/functors/UnaryFunctors.h"
-#include "src/Core/functors/NullaryFunctors.h"
-#include "src/Core/functors/StlFunctors.h"
-#include "src/Core/functors/AssignmentFunctors.h"
-
-// Specialized functors for GPU.
-#ifdef EIGEN_GPUCC
-#include "src/Core/arch/GPU/Complex.h"
-#endif
-
-// Specializations of vectorized activation functions for NEON.
-#ifdef EIGEN_VECTORIZE_NEON
-#include "src/Core/arch/NEON/UnaryFunctors.h"
-#endif
-
-#include "src/Core/util/IndexedViewHelper.h"
-#include "src/Core/util/ReshapedHelper.h"
-#include "src/Core/ArithmeticSequence.h"
-#ifndef EIGEN_NO_IO
-#include "src/Core/IO.h"
-#endif
+#include "src/Core/Functors.h"
 #include "src/Core/DenseCoeffsBase.h"
 #include "src/Core/DenseBase.h"
 #include "src/Core/MatrixBase.h"
 #include "src/Core/EigenBase.h"
 
-#include "src/Core/Product.h"
-#include "src/Core/CoreEvaluators.h"
-#include "src/Core/AssignEvaluator.h"
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN  // work around Doxygen bug triggered by Assign.h r814874
-                                 // at least confirmed with Doxygen 1.5.5 and 1.5.6
-#include "src/Core/Assign.h"
+#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
+                                // at least confirmed with Doxygen 1.5.5 and 1.5.6
+  #include "src/Core/Assign.h"
 #endif
 
-#include "src/Core/ArrayBase.h"
 #include "src/Core/util/BlasUtil.h"
 #include "src/Core/DenseStorage.h"
 #include "src/Core/NestByValue.h"
-
-// #include "src/Core/ForceAlignedAccess.h"
-
+#include "src/Core/ForceAlignedAccess.h"
 #include "src/Core/ReturnByValue.h"
 #include "src/Core/NoAlias.h"
 #include "src/Core/PlainObjectBase.h"
 #include "src/Core/Matrix.h"
 #include "src/Core/Array.h"
-#include "src/Core/CwiseTernaryOp.h"
 #include "src/Core/CwiseBinaryOp.h"
 #include "src/Core/CwiseUnaryOp.h"
 #include "src/Core/CwiseNullaryOp.h"
 #include "src/Core/CwiseUnaryView.h"
 #include "src/Core/SelfCwiseBinaryOp.h"
-#include "src/Core/InnerProduct.h"
 #include "src/Core/Dot.h"
 #include "src/Core/StableNorm.h"
-#include "src/Core/Stride.h"
 #include "src/Core/MapBase.h"
+#include "src/Core/Stride.h"
 #include "src/Core/Map.h"
-#include "src/Core/Ref.h"
 #include "src/Core/Block.h"
 #include "src/Core/VectorBlock.h"
-#include "src/Core/IndexedView.h"
-#include "src/Core/Reshaped.h"
 #include "src/Core/Transpose.h"
 #include "src/Core/DiagonalMatrix.h"
 #include "src/Core/Diagonal.h"
 #include "src/Core/DiagonalProduct.h"
-#include "src/Core/SkewSymmetricMatrix3.h"
+#include "src/Core/PermutationMatrix.h"
+#include "src/Core/Transpositions.h"
 #include "src/Core/Redux.h"
 #include "src/Core/Visitor.h"
 #include "src/Core/Fuzzy.h"
+#include "src/Core/IO.h"
 #include "src/Core/Swap.h"
 #include "src/Core/CommaInitializer.h"
-#include "src/Core/GeneralProduct.h"
-#include "src/Core/Solve.h"
-#include "src/Core/Inverse.h"
-#include "src/Core/SolverBase.h"
-#include "src/Core/PermutationMatrix.h"
-#include "src/Core/Transpositions.h"
+#include "src/Core/Flagged.h"
+#include "src/Core/ProductBase.h"
+#include "src/Core/Product.h"
 #include "src/Core/TriangularMatrix.h"
 #include "src/Core/SelfAdjointView.h"
-#include "src/Core/products/GeneralBlockPanelKernel.h"
-#include "src/Core/DeviceWrapper.h"
-#ifdef EIGEN_GEMM_THREADPOOL
-#include "ThreadPool"
-#endif
+#include "src/Core/SolveTriangular.h"
 #include "src/Core/products/Parallelizer.h"
-#include "src/Core/ProductEvaluators.h"
+#include "src/Core/products/CoeffBasedProduct.h"
+#include "src/Core/products/GeneralBlockPanelKernel.h"
 #include "src/Core/products/GeneralMatrixVector.h"
 #include "src/Core/products/GeneralMatrixMatrix.h"
-#include "src/Core/SolveTriangular.h"
 #include "src/Core/products/GeneralMatrixMatrixTriangular.h"
 #include "src/Core/products/SelfadjointMatrixVector.h"
 #include "src/Core/products/SelfadjointMatrixMatrix.h"
@@ -373,46 +337,24 @@ using std::ptrdiff_t;
 #include "src/Core/products/TriangularSolverMatrix.h"
 #include "src/Core/products/TriangularSolverVector.h"
 #include "src/Core/BandMatrix.h"
-#include "src/Core/CoreIterators.h"
-#include "src/Core/ConditionEstimator.h"
-
-#if defined(EIGEN_VECTORIZE_VSX)
-#include "src/Core/arch/AltiVec/MatrixProduct.h"
-#elif defined EIGEN_VECTORIZE_NEON
-#include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
-#endif
-
-#if defined(EIGEN_VECTORIZE_AVX512)
-#include "src/Core/arch/AVX512/GemmKernel.h"
-#endif
 
+#include "src/Core/BooleanRedux.h"
 #include "src/Core/Select.h"
 #include "src/Core/VectorwiseOp.h"
-#include "src/Core/PartialReduxEvaluator.h"
 #include "src/Core/Random.h"
 #include "src/Core/Replicate.h"
 #include "src/Core/Reverse.h"
+#include "src/Core/ArrayBase.h"
 #include "src/Core/ArrayWrapper.h"
-#include "src/Core/StlIterators.h"
 
-#ifdef EIGEN_USE_BLAS
-#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
-#include "src/Core/products/GeneralMatrixVector_BLAS.h"
-#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
-#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
-#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
-#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
-#include "src/Core/products/TriangularMatrixVector_BLAS.h"
-#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
-#endif  // EIGEN_USE_BLAS
-
-#ifdef EIGEN_USE_MKL_VML
-#include "src/Core/Assign_MKL.h"
-#endif
+} // namespace Eigen
 
 #include "src/Core/GlobalFunctions.h"
-// IWYU pragma: end_exports
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_CORE_MODULE_H
+#ifdef EIGEN2_SUPPORT
+#include "Eigen2Support"
+#endif
+
+#endif // EIGEN_CORE_H

Eigen/Eigen

@@ -1,2 +1,2 @@
 #include "Dense"
-#include "Sparse"
+//#include "Sparse"

Eigen/Eigen2Support

@@ -0,0 +1,82 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN2SUPPORT_H
+#define EIGEN2SUPPORT_H
+
+#if (!defined(EIGEN2_SUPPORT)) || (!defined(EIGEN_CORE_H))
+#error Eigen2 support must be enabled by defining EIGEN2_SUPPORT before including any Eigen header
+#endif
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/** \defgroup Eigen2Support_Module Eigen2 support module
+  * This module provides a couple of deprecated functions improving the compatibility with Eigen2.
+  *
+  * To use it, define EIGEN2_SUPPORT before including any Eigen header
+  * \code
+  * #define EIGEN2_SUPPORT
+  * \endcode
+  *
+  */
+
+#include "src/Eigen2Support/Macros.h"
+#include "src/Eigen2Support/Memory.h"
+#include "src/Eigen2Support/Meta.h"
+#include "src/Eigen2Support/Lazy.h"
+#include "src/Eigen2Support/Cwise.h"
+#include "src/Eigen2Support/CwiseOperators.h"
+#include "src/Eigen2Support/TriangularSolver.h"
+#include "src/Eigen2Support/Block.h"
+#include "src/Eigen2Support/VectorBlock.h"
+#include "src/Eigen2Support/Minor.h"
+#include "src/Eigen2Support/MathFunctions.h"
+
+
+} // namespace Eigen
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+// Eigen2 used to include iostream
+#include<iostream>
+
+#define USING_PART_OF_NAMESPACE_EIGEN \
+EIGEN_USING_MATRIX_TYPEDEFS \
+using Eigen::Matrix; \
+using Eigen::MatrixBase; \
+using Eigen::ei_random; \
+using Eigen::ei_real; \
+using Eigen::ei_imag; \
+using Eigen::ei_conj; \
+using Eigen::ei_abs; \
+using Eigen::ei_abs2; \
+using Eigen::ei_sqrt; \
+using Eigen::ei_exp; \
+using Eigen::ei_log; \
+using Eigen::ei_sin; \
+using Eigen::ei_cos;
+
+#endif // EIGEN2SUPPORT_H

Eigen/Eigenvalues

@@ -1,40 +1,31 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_EIGENVALUES_MODULE_H
 #define EIGEN_EIGENVALUES_MODULE_H
 
 #include "Core"
 
+#include "src/Core/util/DisableStupidWarnings.h"
+
 #include "Cholesky"
 #include "Jacobi"
 #include "Householder"
 #include "LU"
-#include "Geometry"
 
-#include "src/Core/util/DisableStupidWarnings.h"
+namespace Eigen {
 
 /** \defgroup Eigenvalues_Module Eigenvalues module
- *
- *
- *
- * This module mainly provides various eigenvalue solvers.
- * This module also provides some MatrixBase methods, including:
- *  - MatrixBase::eigenvalues(),
- *  - MatrixBase::operatorNorm()
- *
- * \code
- * #include <Eigen/Eigenvalues>
- * \endcode
- */
+  *
+  *
+  *
+  * This module mainly provides various eigenvalue solvers.
+  * This module also provides some MatrixBase methods, including:
+  *  - MatrixBase::eigenvalues(),
+  *  - MatrixBase::operatorNorm()
+  *
+  * \code
+  * #include <Eigen/Eigenvalues>
+  * \endcode
+  */
 
-#include "src/misc/RealSvd2x2.h"
-
-// IWYU pragma: begin_exports
 #include "src/Eigenvalues/Tridiagonalization.h"
 #include "src/Eigenvalues/RealSchur.h"
 #include "src/Eigenvalues/EigenSolver.h"
@@ -43,21 +34,11 @@
 #include "src/Eigenvalues/HessenbergDecomposition.h"
 #include "src/Eigenvalues/ComplexSchur.h"
 #include "src/Eigenvalues/ComplexEigenSolver.h"
-#include "src/Eigenvalues/RealQZ.h"
-#include "src/Eigenvalues/GeneralizedEigenSolver.h"
 #include "src/Eigenvalues/MatrixBaseEigenvalues.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/Eigenvalues/RealSchur_LAPACKE.h"
-#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
-#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
-#endif
-// IWYU pragma: end_exports
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_EIGENVALUES_MODULE_H
+#endif // EIGEN_EIGENVALUES_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/Geometry

@@ -1,61 +1,67 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_GEOMETRY_MODULE_H
 #define EIGEN_GEOMETRY_MODULE_H
 
 #include "Core"
 
+#include "src/Core/util/DisableStupidWarnings.h"
+
 #include "SVD"
 #include "LU"
 #include <limits>
 
-#include "src/Core/util/DisableStupidWarnings.h"
+#ifndef M_PI
+#define M_PI 3.14159265358979323846
+#endif
+
+namespace Eigen {
 
 /** \defgroup Geometry_Module Geometry module
- *
- * This module provides support for:
- *  - fixed-size homogeneous transformations
- *  - translation, scaling, 2D and 3D rotations
- *  - \link Quaternion quaternions \endlink
- *  - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
- *  - orthogonal vector generation (\ref MatrixBase::unitOrthogonal)
- *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes
- * \endlink
- *  - \link AlignedBox axis aligned bounding boxes \endlink
- *  - \link umeyama least-square transformation fitting \endlink
- *
- * \code
- * #include <Eigen/Geometry>
- * \endcode
- */
+  *
+  *
+  *
+  * This module provides support for:
+  *  - fixed-size homogeneous transformations
+  *  - translation, scaling, 2D and 3D rotations
+  *  - quaternions
+  *  - \ref MatrixBase::cross() "cross product"
+  *  - \ref MatrixBase::unitOrthogonal() "orthognal vector generation"
+  *  - some linear components: parametrized-lines and hyperplanes
+  *
+  * \code
+  * #include <Eigen/Geometry>
+  * \endcode
+  */
 
-// IWYU pragma: begin_exports
 #include "src/Geometry/OrthoMethods.h"
 #include "src/Geometry/EulerAngles.h"
-#include "src/Geometry/Homogeneous.h"
-#include "src/Geometry/RotationBase.h"
-#include "src/Geometry/Rotation2D.h"
-#include "src/Geometry/Quaternion.h"
-#include "src/Geometry/AngleAxis.h"
-#include "src/Geometry/Transform.h"
-#include "src/Geometry/Translation.h"
-#include "src/Geometry/Scaling.h"
-#include "src/Geometry/Hyperplane.h"
-#include "src/Geometry/ParametrizedLine.h"
-#include "src/Geometry/AlignedBox.h"
-#include "src/Geometry/Umeyama.h"
 
-// Use the SSE optimized version whenever possible.
-#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
-#include "src/Geometry/arch/Geometry_SIMD.h"
+#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
+  #include "src/Geometry/Homogeneous.h"
+  #include "src/Geometry/RotationBase.h"
+  #include "src/Geometry/Rotation2D.h"
+  #include "src/Geometry/Quaternion.h"
+  #include "src/Geometry/AngleAxis.h"
+  #include "src/Geometry/Transform.h"
+  #include "src/Geometry/Translation.h"
+  #include "src/Geometry/Scaling.h"
+  #include "src/Geometry/Hyperplane.h"
+  #include "src/Geometry/ParametrizedLine.h"
+  #include "src/Geometry/AlignedBox.h"
+  #include "src/Geometry/Umeyama.h"
+
+  #if defined EIGEN_VECTORIZE_SSE
+    #include "src/Geometry/arch/Geometry_SSE.h"
+  #endif
 #endif
-// IWYU pragma: end_exports
+
+#ifdef EIGEN2_SUPPORT
+#include "src/Eigen2Support/Geometry/All.h"
+#endif
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_GEOMETRY_MODULE_H
+#endif // EIGEN_GEOMETRY_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */
+

Eigen/Householder

@@ -1,10 +1,3 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_HOUSEHOLDER_MODULE_H
 #define EIGEN_HOUSEHOLDER_MODULE_H
 
@@ -12,20 +5,23 @@
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
+namespace Eigen {
+
 /** \defgroup Householder_Module Householder module
- * This module provides Householder transformations.
- *
- * \code
- * #include <Eigen/Householder>
- * \endcode
- */
+  * This module provides Householder transformations.
+  *
+  * \code
+  * #include <Eigen/Householder>
+  * \endcode
+  */
 
-// IWYU pragma: begin_exports
 #include "src/Householder/Householder.h"
 #include "src/Householder/HouseholderSequence.h"
 #include "src/Householder/BlockHouseholder.h"
-// IWYU pragma: end_exports
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_HOUSEHOLDER_MODULE_H
+#endif // EIGEN_HOUSEHOLDER_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/IterativeLinearSolvers

@@ -1,52 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
-#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/**
-  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
-  *
-  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a
-  squared matrix, usually very large and sparse.
-  * Those solvers are accessible via the following classes:
-  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
-  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
-  *  - BiCGSTAB for general square matrices.
-  *
-  * These iterative solvers are associated with some preconditioners:
-  *  - IdentityPreconditioner - not really useful
-  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
-  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
-  *
-  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport,
-  UmfPackSupport, SuperLUSupport, AccelerateSupport.
-  *
-    \code
-    #include <Eigen/IterativeLinearSolvers>
-    \endcode
-  */
-
-// IWYU pragma: begin_exports
-#include "src/IterativeLinearSolvers/SolveWithGuess.h"
-#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
-#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
-#include "src/IterativeLinearSolvers/ConjugateGradient.h"
-#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
-#include "src/IterativeLinearSolvers/BiCGSTAB.h"
-#include "src/IterativeLinearSolvers/IncompleteLUT.h"
-#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H

Eigen/Jacobi

@@ -1,10 +1,3 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_JACOBI_MODULE_H
 #define EIGEN_JACOBI_MODULE_H
 
@@ -12,22 +5,26 @@
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
+namespace Eigen {
+
 /** \defgroup Jacobi_Module Jacobi module
- * This module provides Jacobi and Givens rotations.
- *
- * \code
- * #include <Eigen/Jacobi>
- * \endcode
- *
- * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
- *  - MatrixBase::applyOnTheLeft()
- *  - MatrixBase::applyOnTheRight().
- */
+  * This module provides Jacobi and Givens rotations.
+  *
+  * \code
+  * #include <Eigen/Jacobi>
+  * \endcode
+  *
+  * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
+  *  - MatrixBase::applyOnTheLeft()
+  *  - MatrixBase::applyOnTheRight().
+  */
 
-// IWYU pragma: begin_exports
 #include "src/Jacobi/Jacobi.h"
-// IWYU pragma: end_exports
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_JACOBI_MODULE_H
+#endif // EIGEN_JACOBI_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */
+

Eigen/KLUSupport

@@ -1,43 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_KLUSUPPORT_MODULE_H
-#define EIGEN_KLUSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <btf.h>
-#include <klu.h>
-}
-
-/** \ingroup Support_modules
- * \defgroup KLUSupport_Module KLUSupport module
- *
- * This module provides an interface to the KLU library which is part of the <a
- * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
- * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
- *
- * \code
- * #include <Eigen/KLUSupport>
- * \endcode
- *
- * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must
- * be linked to the klu library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
- * cmake based project, you can use our FindKLU.cmake module to help you in this task.
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/KLUSupport/KLUSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_KLUSUPPORT_MODULE_H

Eigen/LU

@@ -1,10 +1,3 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_LU_MODULE_H
 #define EIGEN_LU_MODULE_H
 
@@ -12,35 +5,38 @@
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
-/** \defgroup LU_Module LU module
- * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
- * This module defines the following MatrixBase methods:
- *  - MatrixBase::inverse()
- *  - MatrixBase::determinant()
- *
- * \code
- * #include <Eigen/LU>
- * \endcode
- */
+namespace Eigen {
 
+/** \defgroup LU_Module LU module
+  * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
+  * This module defines the following MatrixBase methods:
+  *  - MatrixBase::inverse()
+  *  - MatrixBase::determinant()
+  *
+  * \code
+  * #include <Eigen/LU>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
 #include "src/misc/Kernel.h"
 #include "src/misc/Image.h"
-
-// IWYU pragma: begin_exports
 #include "src/LU/FullPivLU.h"
 #include "src/LU/PartialPivLU.h"
-#ifdef EIGEN_USE_LAPACKE
-#include "src/misc/lapacke_helpers.h"
-#include "src/LU/PartialPivLU_LAPACKE.h"
-#endif
 #include "src/LU/Determinant.h"
-#include "src/LU/InverseImpl.h"
+#include "src/LU/Inverse.h"
 
-#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
-#include "src/LU/arch/InverseSize4.h"
+#if defined EIGEN_VECTORIZE_SSE
+  #include "src/LU/arch/Inverse_SSE.h"
 #endif
-// IWYU pragma: end_exports
+
+#ifdef EIGEN2_SUPPORT
+  #include "src/Eigen2Support/LU.h"
+#endif
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_LU_MODULE_H
+#endif // EIGEN_LU_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/LeastSquares

@@ -0,0 +1,36 @@
+#ifndef EIGEN_REGRESSION_MODULE_H
+#define EIGEN_REGRESSION_MODULE_H
+
+#ifndef EIGEN2_SUPPORT
+#error LeastSquares is only available in Eigen2 support mode (define EIGEN2_SUPPORT)
+#endif
+
+// exclude from normal eigen3-only documentation
+#ifdef EIGEN2_SUPPORT
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include "Eigenvalues"
+#include "Geometry"
+
+namespace Eigen {
+
+/** \defgroup LeastSquares_Module LeastSquares module
+  * This module provides linear regression and related features.
+  *
+  * \code
+  * #include <Eigen/LeastSquares>
+  * \endcode
+  */
+
+#include "src/Eigen2Support/LeastSquares.h"
+
+} // namespace Eigen
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN2_SUPPORT
+
+#endif // EIGEN_REGRESSION_MODULE_H

Eigen/MetisSupport

@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_METISSUPPORT_MODULE_H
-#define EIGEN_METISSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <metis.h>
-}
-
-/** \ingroup Support_modules
- * \defgroup MetisSupport_Module MetisSupport module
- *
- * \code
- * #include <Eigen/MetisSupport>
- * \endcode
- * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
- * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
- */
-
-// IWYU pragma: begin_exports
-#include "src/MetisSupport/MetisSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_METISSUPPORT_MODULE_H

Eigen/OrderingMethods

@@ -1,73 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
-#define EIGEN_ORDERINGMETHODS_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/**
- * \defgroup OrderingMethods_Module OrderingMethods module
- *
- * This module is currently for internal use only
- *
- * It defines various built-in and external ordering methods for sparse matrices.
- * They are typically used to reduce the number of elements during
- * the sparse matrix decomposition (LLT, LU, QR).
- * Precisely, in a preprocessing step, a permutation matrix P is computed using
- * those ordering methods and applied to the columns of the matrix.
- * Using for instance the sparse Cholesky decomposition, it is expected that
- * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
- *
- *
- * Usage :
- * \code
- * #include <Eigen/OrderingMethods>
- * \endcode
- *
- * A simple usage is as a template parameter in the sparse decomposition classes :
- *
- * \code
- * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
- * \endcode
- *
- * \code
- * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
- * \endcode
- *
- * It is possible as well to call directly a particular ordering method for your own purpose,
- * \code
- * AMDOrdering<int> ordering;
- * PermutationMatrix<Dynamic, Dynamic, int> perm;
- * SparseMatrix<double> A;
- * //Fill the matrix ...
- *
- * ordering(A, perm); // Call AMD
- * \endcode
- *
- * \note Some of these methods (like AMD or METIS), need the sparsity pattern
- * of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
- * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
- * If your matrix is already symmetric (at least in structure), you can avoid that
- * by calling the method with a SelfAdjointView type.
- *
- * \code
- *  // Call the ordering on the pattern of the lower triangular matrix A
- * ordering(A.selfadjointView<Lower>(), perm);
- * \endcode
- */
-
-// IWYU pragma: begin_exports
-#include "src/OrderingMethods/Amd.h"
-#include "src/OrderingMethods/Ordering.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_ORDERINGMETHODS_MODULE_H

Eigen/PaStiXSupport

@@ -1,51 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
-#define EIGEN_PASTIXSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <pastix_nompi.h>
-#include <pastix.h>
-}
-
-#ifdef complex
-#undef complex
-#endif
-
-/** \ingroup Support_modules
- * \defgroup PaStiXSupport_Module PaStiXSupport module
- *
- * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
- * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
- * It provides the two following main factorization classes:
- * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
- * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
- * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
- *
- * \code
- * #include <Eigen/PaStiXSupport>
- * \endcode
- *
- * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be
- * linked to the PaSTiX library and its dependencies. This wrapper resuires PaStiX version 5.x compiled without MPI
- * support. The dependencies depend on how PaSTiX has been compiled. For a cmake based project, you can use our
- * FindPaSTiX.cmake module to help you in this task.
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/PaStiXSupport/PaStiXSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_PASTIXSUPPORT_MODULE_H

Eigen/PardisoSupport

@@ -1,38 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
-#define EIGEN_PARDISOSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <mkl_pardiso.h>
-
-/** \ingroup Support_modules
- * \defgroup PardisoSupport_Module PardisoSupport module
- *
- * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
- *
- * \code
- * #include <Eigen/PardisoSupport>
- * \endcode
- *
- * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be
- * linked to the MKL library and its dependencies. See this \ref TopicUsingIntelMKL "page" for more information on
- * MKL-Eigen integration.
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/PardisoSupport/PardisoSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_PARDISOSUPPORT_MODULE_H

Eigen/QR

@@ -1,48 +1,45 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_QR_MODULE_H
 #define EIGEN_QR_MODULE_H
 
 #include "Core"
 
+#include "src/Core/util/DisableStupidWarnings.h"
+
 #include "Cholesky"
 #include "Jacobi"
 #include "Householder"
 
-#include "src/Core/util/DisableStupidWarnings.h"
+namespace Eigen {
 
 /** \defgroup QR_Module QR module
- *
- *
- *
- * This module provides various QR decompositions
- * This module also provides some MatrixBase methods, including:
- *  - MatrixBase::householderQr()
- *  - MatrixBase::colPivHouseholderQr()
- *  - MatrixBase::fullPivHouseholderQr()
- *
- * \code
- * #include <Eigen/QR>
- * \endcode
- */
+  *
+  *
+  *
+  * This module provides various QR decompositions
+  * This module also provides some MatrixBase methods, including:
+  *  - MatrixBase::qr(),
+  *
+  * \code
+  * #include <Eigen/QR>
+  * \endcode
+  */
 
-// IWYU pragma: begin_exports
+#include "src/misc/Solve.h"
 #include "src/QR/HouseholderQR.h"
 #include "src/QR/FullPivHouseholderQR.h"
 #include "src/QR/ColPivHouseholderQR.h"
-#include "src/QR/CompleteOrthogonalDecomposition.h"
-#ifdef EIGEN_USE_LAPACKE
-#include "src/misc/lapacke_helpers.h"
-#include "src/QR/HouseholderQR_LAPACKE.h"
-#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
+
+#ifdef EIGEN2_SUPPORT
+#include "src/Eigen2Support/QR.h"
 #endif
-// IWYU pragma: end_exports
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_QR_MODULE_H
+#ifdef EIGEN2_SUPPORT
+#include "Eigenvalues"
+#endif
+
+#endif // EIGEN_QR_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/QtAlignedMalloc

@@ -1,9 +1,3 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
 #ifndef EIGEN_QTMALLOC_MODULE_H
 #define EIGEN_QTMALLOC_MODULE_H
@@ -14,13 +8,20 @@
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
-void *qMalloc(std::size_t size) { return Eigen::internal::aligned_malloc(size); }
+void *qMalloc(size_t size)
+{
+  return Eigen::internal::aligned_malloc(size);
+}
 
-void qFree(void *ptr) { Eigen::internal::aligned_free(ptr); }
+void qFree(void *ptr)
+{
+  Eigen::internal::aligned_free(ptr);
+}
 
-void *qRealloc(void *ptr, std::size_t size) {
-  void *newPtr = Eigen::internal::aligned_malloc(size);
-  std::memcpy(newPtr, ptr, size);
+void *qRealloc(void *ptr, size_t size)
+{
+  void* newPtr = Eigen::internal::aligned_malloc(size);
+  memcpy(newPtr, ptr, size);
   Eigen::internal::aligned_free(ptr);
   return newPtr;
 }
@@ -29,4 +30,5 @@ void *qRealloc(void *ptr, std::size_t size) {
 
 #endif
 
-#endif  // EIGEN_QTMALLOC_MODULE_H
+#endif // EIGEN_QTMALLOC_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/SPQRSupport

@@ -1,41 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPQRSUPPORT_MODULE_H
-#define EIGEN_SPQRSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include "SuiteSparseQR.hpp"
-
-/** \ingroup Support_modules
- * \defgroup SPQRSupport_Module SuiteSparseQR module
- *
- * This module provides an interface to the SPQR library, which is part of the <a
- * href="http://www.suitesparse.com">suitesparse</a> package.
- *
- * \code
- * #include <Eigen/SPQRSupport>
- * \endcode
- *
- * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be
- * linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...). For a cmake based project, you can use
- * our FindSPQR.cmake and FindCholmod.Cmake modules
- *
- */
-
-#include "CholmodSupport"
-
-// IWYU pragma: begin_exports
-#include "src/SPQRSupport/SuiteSparseQRSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif

Eigen/SVD

@@ -1,10 +1,3 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_SVD_MODULE_H
 #define EIGEN_SVD_MODULE_H
 
@@ -14,43 +7,32 @@
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
-/** \defgroup SVD_Module SVD module
- *
- *
- *
- * This module provides SVD decomposition for matrices (both real and complex).
- * Two decomposition algorithms are provided:
- *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very
- * slow for larger ones.
- *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast
- * for large problems. These decompositions are accessible via the respective classes and following MatrixBase methods:
- *  - MatrixBase::jacobiSvd()
- *  - MatrixBase::bdcSvd()
- *
- * \code
- * #include <Eigen/SVD>
- * \endcode
- */
+namespace Eigen {
 
-// IWYU pragma: begin_exports
-#include "src/misc/RealSvd2x2.h"
-#include "src/SVD/UpperBidiagonalization.h"
-#include "src/SVD/SVDBase.h"
+/** \defgroup SVD_Module SVD module
+  *
+  *
+  *
+  * This module provides SVD decomposition for matrices (both real and complex).
+  * This decomposition is accessible via the following MatrixBase method:
+  *  - MatrixBase::jacobiSvd()
+  *
+  * \code
+  * #include <Eigen/SVD>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
 #include "src/SVD/JacobiSVD.h"
-#include "src/SVD/BDCSVD.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
+#include "src/SVD/UpperBidiagonalization.h"
+
+#ifdef EIGEN2_SUPPORT
+#include "src/Eigen2Support/SVD.h"
 #endif
-#ifndef EIGEN_USE_LAPACKE_STRICT
-#include "src/SVD/JacobiSVD_LAPACKE.h"
-#endif
-#include "src/SVD/BDCSVD_LAPACKE.h"
-#endif
-// IWYU pragma: end_exports
+
+} // namespace Eigen
 
 #include "src/Core/util/ReenableStupidWarnings.h"
 
-#endif  // EIGEN_SVD_MODULE_H
+#endif // EIGEN_SVD_MODULE_H
+/* vim: set filetype=cpp et sw=2 ts=2 ai: */

Eigen/Sparse

@@ -1,33 +1,69 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
 #ifndef EIGEN_SPARSE_MODULE_H
 #define EIGEN_SPARSE_MODULE_H
 
-/** \defgroup Sparse_Module Sparse meta-module
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+
+#ifdef EIGEN2_SUPPORT
+#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
+#endif
+
+#ifndef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
+#error The sparse module API is not stable yet. To use it anyway, please define the EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET preprocessor token.
+#endif
+
+namespace Eigen {
+
+/** \defgroup Sparse_Module Sparse module
   *
-  * Meta-module including all related modules:
-  * - \ref SparseCore_Module
-  * - \ref OrderingMethods_Module
-  * - \ref SparseCholesky_Module
-  * - \ref SparseLU_Module
-  * - \ref SparseQR_Module
-  * - \ref IterativeLinearSolvers_Module
   *
-    \code
-    #include <Eigen/Sparse>
-    \endcode
+  *
+  * See the \ref TutorialSparse "Sparse tutorial"
+  *
+  * \code
+  * #include <Eigen/Sparse>
+  * \endcode
   */
 
-#include "SparseCore"
-#include "OrderingMethods"
-#include "SparseCholesky"
-#include "SparseLU"
-#include "SparseQR"
-#include "IterativeLinearSolvers"
+/** The type used to identify a general sparse storage. */
+struct Sparse {};
+
+#include "src/Sparse/SparseUtil.h"
+#include "src/Sparse/SparseMatrixBase.h"
+#include "src/Sparse/CompressedStorage.h"
+#include "src/Sparse/AmbiVector.h"
+#include "src/Sparse/SparseMatrix.h"
+#include "src/Sparse/DynamicSparseMatrix.h"
+#include "src/Sparse/MappedSparseMatrix.h"
+#include "src/Sparse/SparseVector.h"
+#include "src/Sparse/CoreIterators.h"
+#include "src/Sparse/SparseBlock.h"
+#include "src/Sparse/SparseTranspose.h"
+#include "src/Sparse/SparseCwiseUnaryOp.h"
+#include "src/Sparse/SparseCwiseBinaryOp.h"
+#include "src/Sparse/SparseDot.h"
+#include "src/Sparse/SparseAssign.h"
+#include "src/Sparse/SparseRedux.h"
+#include "src/Sparse/SparseFuzzy.h"
+#include "src/Sparse/SparseProduct.h"
+#include "src/Sparse/SparseSparseProduct.h"
+#include "src/Sparse/SparseDenseProduct.h"
+#include "src/Sparse/SparseDiagonalProduct.h"
+#include "src/Sparse/SparseTriangularView.h"
+#include "src/Sparse/SparseSelfAdjointView.h"
+#include "src/Sparse/TriangularSolver.h"
+#include "src/Sparse/SparseView.h"
+
+} // namespace Eigen
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSE_MODULE_H
 
-#endif  // EIGEN_SPARSE_MODULE_H

Eigen/SparseCholesky

@@ -1,40 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
-#define EIGEN_SPARSECHOLESKY_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/**
- * \defgroup SparseCholesky_Module SparseCholesky module
- *
- * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian)
- * matrices. Those decompositions are accessible via the following classes:
- *  - SimplicialLLt,
- *  - SimplicialLDLt
- *
- * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
- *
- * \code
- * #include <Eigen/SparseCholesky>
- * \endcode
- */
-
-// IWYU pragma: begin_exports
-#include "src/SparseCholesky/SimplicialCholesky.h"
-#include "src/SparseCholesky/SimplicialCholesky_impl.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_SPARSECHOLESKY_MODULE_H

Eigen/SparseCore

@@ -1,70 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSECORE_MODULE_H
-#define EIGEN_SPARSECORE_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <vector>
-#include <map>
-#include <cstdlib>
-#include <cstring>
-#include <algorithm>
-#include <numeric>
-
-/**
- * \defgroup SparseCore_Module SparseCore module
- *
- * This module provides a sparse matrix representation, and basic associated matrix manipulations
- * and operations.
- *
- * See the \ref TutorialSparse "Sparse tutorial"
- *
- * \code
- * #include <Eigen/SparseCore>
- * \endcode
- *
- * This module depends on: Core.
- */
-
-// IWYU pragma: begin_exports
-#include "src/SparseCore/SparseUtil.h"
-#include "src/SparseCore/SparseMatrixBase.h"
-#include "src/SparseCore/SparseAssign.h"
-#include "src/SparseCore/CompressedStorage.h"
-#include "src/SparseCore/AmbiVector.h"
-#include "src/SparseCore/SparseCompressedBase.h"
-#include "src/SparseCore/SparseMatrix.h"
-#include "src/SparseCore/SparseMap.h"
-#include "src/SparseCore/SparseVector.h"
-#include "src/SparseCore/SparseRef.h"
-#include "src/SparseCore/SparseCwiseUnaryOp.h"
-#include "src/SparseCore/SparseCwiseBinaryOp.h"
-#include "src/SparseCore/SparseTranspose.h"
-#include "src/SparseCore/SparseBlock.h"
-#include "src/SparseCore/SparseDot.h"
-#include "src/SparseCore/SparseRedux.h"
-#include "src/SparseCore/SparseView.h"
-#include "src/SparseCore/SparseDiagonalProduct.h"
-#include "src/SparseCore/ConservativeSparseSparseProduct.h"
-#include "src/SparseCore/SparseSparseProductWithPruning.h"
-#include "src/SparseCore/SparseProduct.h"
-#include "src/SparseCore/SparseDenseProduct.h"
-#include "src/SparseCore/SparseSelfAdjointView.h"
-#include "src/SparseCore/SparseTriangularView.h"
-#include "src/SparseCore/TriangularSolver.h"
-#include "src/SparseCore/SparsePermutation.h"
-#include "src/SparseCore/SparseFuzzy.h"
-#include "src/SparseCore/SparseSolverBase.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_SPARSECORE_MODULE_H

Eigen/SparseLU

@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_MODULE_H
-#define EIGEN_SPARSELU_MODULE_H
-
-#include "SparseCore"
-
-/**
- * \defgroup SparseLU_Module SparseLU module
- * This module defines a supernodal factorization of general sparse matrices.
- * The code is fully optimized for supernode-panel updates with specialized kernels.
- * Please, see the documentation of the SparseLU class for more details.
- */
-
-// Ordering interface
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-// IWYU pragma: begin_exports
-#include "src/SparseLU/SparseLU_Structs.h"
-#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
-#include "src/SparseLU/SparseLUImpl.h"
-#include "src/SparseCore/SparseColEtree.h"
-#include "src/SparseLU/SparseLU_Memory.h"
-#include "src/SparseLU/SparseLU_heap_relax_snode.h"
-#include "src/SparseLU/SparseLU_relax_snode.h"
-#include "src/SparseLU/SparseLU_pivotL.h"
-#include "src/SparseLU/SparseLU_panel_dfs.h"
-#include "src/SparseLU/SparseLU_kernel_bmod.h"
-#include "src/SparseLU/SparseLU_panel_bmod.h"
-#include "src/SparseLU/SparseLU_column_dfs.h"
-#include "src/SparseLU/SparseLU_column_bmod.h"
-#include "src/SparseLU/SparseLU_copy_to_ucol.h"
-#include "src/SparseLU/SparseLU_pruneL.h"
-#include "src/SparseLU/SparseLU_Utils.h"
-#include "src/SparseLU/SparseLU.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_SPARSELU_MODULE_H

Eigen/SparseQR

@@ -1,38 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEQR_MODULE_H
-#define EIGEN_SPARSEQR_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup SparseQR_Module SparseQR module
- * \brief Provides QR decomposition for sparse matrices
- *
- * This module provides a simplicial version of the left-looking Sparse QR decomposition.
- * The columns of the input matrix should be reordered to limit the fill-in during the
- * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
- * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
- * of built-in and external ordering methods.
- *
- * \code
- * #include <Eigen/SparseQR>
- * \endcode
- *
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/SparseCore/SparseColEtree.h"
-#include "src/SparseQR/SparseQR.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif

Eigen/StdDeque

@@ -4,9 +4,24 @@
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_STDDEQUE_MODULE_H
 #define EIGEN_STDDEQUE_MODULE_H
@@ -14,17 +29,14 @@
 #include "Core"
 #include <deque>
 
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
-    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
 
 #define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
 
 #else
 
-// IWYU pragma: begin_exports
 #include "src/StlSupport/StdDeque.h"
-// IWYU pragma: end_exports
 
 #endif
 
-#endif  // EIGEN_STDDEQUE_MODULE_H
+#endif // EIGEN_STDDEQUE_MODULE_H

Eigen/StdList

@@ -3,9 +3,24 @@
 //
 // Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_STDLIST_MODULE_H
 #define EIGEN_STDLIST_MODULE_H
@@ -13,17 +28,14 @@
 #include "Core"
 #include <list>
 
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
-    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */    
 
 #define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
 
 #else
 
-// IWYU pragma: begin_exports
 #include "src/StlSupport/StdList.h"
-// IWYU pragma: end_exports
 
 #endif
 
-#endif  // EIGEN_STDLIST_MODULE_H
+#endif // EIGEN_STDLIST_MODULE_H

Eigen/StdVector

@@ -4,9 +4,24 @@
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_STDVECTOR_MODULE_H
 #define EIGEN_STDVECTOR_MODULE_H
@@ -14,17 +29,14 @@
 #include "Core"
 #include <vector>
 
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
-    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
 
 #define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
 
 #else
 
-// IWYU pragma: begin_exports
 #include "src/StlSupport/StdVector.h"
-// IWYU pragma: end_exports
 
 #endif
 
-#endif  // EIGEN_STDVECTOR_MODULE_H
+#endif // EIGEN_STDVECTOR_MODULE_H

Eigen/SuperLUSupport

@@ -1,70 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
-#define EIGEN_SUPERLUSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#ifdef EMPTY
-#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
-#endif
-
-typedef int int_t;
-#include <slu_Cnames.h>
-#include <supermatrix.h>
-#include <slu_util.h>
-
-// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
-// so we remove it in favor of a SUPERLU_EMPTY token.
-// If EMPTY was already defined then we don't undef it.
-
-#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
-#undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
-#elif defined(EMPTY)
-#undef EMPTY
-#endif
-
-#define SUPERLU_EMPTY (-1)
-
-namespace Eigen {
-struct SluMatrix;
-}
-
-/** \ingroup Support_modules
- * \defgroup SuperLUSupport_Module SuperLUSupport module
- *
- * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
- * It provides the following factorization class:
- * - class SuperLU: a supernodal sequential LU factorization.
- * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative
- * methods).
- *
- * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
- *
- * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined
- * because it is too polluting.
- *
- * \code
- * #include <Eigen/SuperLUSupport>
- * \endcode
- *
- * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be
- * linked to the superlu library and its dependencies. The dependencies depend on how superlu has been compiled. For a
- * cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/SuperLUSupport/SuperLUSupport.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_SUPERLUSUPPORT_MODULE_H

Eigen/ThreadPool

@@ -1,79 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_THREADPOOL_MODULE_H
-#define EIGEN_THREADPOOL_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup ThreadPool_Module ThreadPool Module
- *
- * This module provides 2 threadpool implementations
- *  - a simple reference implementation
- *  - a faster non blocking implementation
- *
- * \code
- * #include <Eigen/ThreadPool>
- * \endcode
- */
-
-#include <cstddef>
-#include <cstring>
-#include <time.h>
-
-#include <vector>
-#include <atomic>
-#include <condition_variable>
-#include <deque>
-#include <mutex>
-#include <thread>
-#include <functional>
-#include <memory>
-#include <utility>
-
-// There are non-parenthesized calls to "max" in the  <unordered_map> header,
-// which trigger a check in test/main.h causing compilation to fail.
-// We work around the check here by removing the check for max in
-// the case where we have to emulate thread_local.
-#ifdef max
-#undef max
-#endif
-#include <unordered_map>
-
-#include "src/Core/util/Meta.h"
-#include "src/Core/util/MaxSizeVector.h"
-
-#ifndef EIGEN_MUTEX
-#define EIGEN_MUTEX std::mutex
-#endif
-#ifndef EIGEN_MUTEX_LOCK
-#define EIGEN_MUTEX_LOCK std::unique_lock<std::mutex>
-#endif
-#ifndef EIGEN_CONDVAR
-#define EIGEN_CONDVAR std::condition_variable
-#endif
-
-// IWYU pragma: begin_exports
-#include "src/ThreadPool/ThreadLocal.h"
-#include "src/ThreadPool/ThreadYield.h"
-#include "src/ThreadPool/ThreadCancel.h"
-#include "src/ThreadPool/EventCount.h"
-#include "src/ThreadPool/RunQueue.h"
-#include "src/ThreadPool/ThreadPoolInterface.h"
-#include "src/ThreadPool/ThreadEnvironment.h"
-#include "src/ThreadPool/Barrier.h"
-#include "src/ThreadPool/NonBlockingThreadPool.h"
-#include "src/ThreadPool/CoreThreadPoolDevice.h"
-// IWYU pragma: end_exports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_CXX11_THREADPOOL_MODULE_H

Eigen/UmfPackSupport

@@ -1,42 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
-#define EIGEN_UMFPACKSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <umfpack.h>
-}
-
-/** \ingroup Support_modules
- * \defgroup UmfPackSupport_Module UmfPackSupport module
- *
- * This module provides an interface to the UmfPack library which is part of the <a
- * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
- * - class UmfPackLU: a multifrontal sequential LU factorization.
- *
- * \code
- * #include <Eigen/UmfPackSupport>
- * \endcode
- *
- * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be
- * linked to the umfpack library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
- * cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
- *
- */
-
-// IWYU pragma: begin_exports
-#include "src/UmfPackSupport/UmfPackSupport.h"
-// IWYU pragma: endexports
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_UMFPACKSUPPORT_MODULE_H

Eigen/src/AccelerateSupport/AccelerateSupport.h

@@ -1,423 +0,0 @@
-#ifndef EIGEN_ACCELERATESUPPORT_H
-#define EIGEN_ACCELERATESUPPORT_H
-
-#include <Accelerate/Accelerate.h>
-
-#include <Eigen/Sparse>
-
-namespace Eigen {
-
-template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
-class AccelerateImpl;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateLLT
- * \brief A direct Cholesky (LLT) factorization and solver based on Accelerate
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateLLT
- */
-template <typename MatrixType, int UpLo = Lower>
-using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateLDLT
- * \brief The default Cholesky (LDLT) factorization and solver based on Accelerate
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLT
- */
-template <typename MatrixType, int UpLo = Lower>
-using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateLDLTUnpivoted
- * \brief A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTUnpivoted
- */
-template <typename MatrixType, int UpLo = Lower>
-using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateLDLTSBK
- * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static
- * pivoting
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTSBK
- */
-template <typename MatrixType, int UpLo = Lower>
-using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateLDLTTPP
- * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTTPP
- */
-template <typename MatrixType, int UpLo = Lower>
-using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateQR
- * \brief A QR factorization and solver based on Accelerate
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateQR
- */
-template <typename MatrixType>
-using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;
-
-/** \ingroup AccelerateSupport_Module
- * \class AccelerateCholeskyAtA
- * \brief A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R)
- *
- * \warning Only single and double precision real scalar types are supported by Accelerate
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- *
- * \sa \ref TutorialSparseSolverConcept, class AccelerateCholeskyAtA
- */
-template <typename MatrixType>
-using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
-
-namespace internal {
-template <typename T>
-struct AccelFactorizationDeleter {
-  void operator()(T* sym) {
-    if (sym) {
-      SparseCleanup(*sym);
-      delete sym;
-      sym = nullptr;
-    }
-  }
-};
-
-template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
-struct SparseTypesTraitBase {
-  typedef DenseVecT AccelDenseVector;
-  typedef DenseMatT AccelDenseMatrix;
-  typedef SparseMatT AccelSparseMatrix;
-
-  typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
-  typedef NumFactT NumericFactorization;
-
-  typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
-  typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
-};
-
-template <typename Scalar>
-struct SparseTypesTrait {};
-
-template <>
-struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
-                                                       SparseOpaqueFactorization_Double> {};
-
-template <>
-struct SparseTypesTrait<float>
-    : SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
-};
-
-}  // end namespace internal
-
-template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
-class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
- protected:
-  using Base = SparseSolverBase<AccelerateImpl>;
-  using Base::derived;
-  using Base::m_isInitialized;
-
- public:
-  using Base::_solve_impl;
-
-  typedef MatrixType_ MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
-  enum { UpLo = UpLo_ };
-
-  using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
-  using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
-  using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
-  using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
-  using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
-  using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
-  using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
-
-  AccelerateImpl() {
-    m_isInitialized = false;
-
-    auto check_flag_set = [](int value, int flag) { return ((value & flag) == flag); };
-
-    if (check_flag_set(UpLo_, Symmetric)) {
-      m_sparseKind = SparseSymmetric;
-      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
-    } else if (check_flag_set(UpLo_, UnitLower)) {
-      m_sparseKind = SparseUnitTriangular;
-      m_triType = SparseLowerTriangle;
-    } else if (check_flag_set(UpLo_, UnitUpper)) {
-      m_sparseKind = SparseUnitTriangular;
-      m_triType = SparseUpperTriangle;
-    } else if (check_flag_set(UpLo_, StrictlyLower)) {
-      m_sparseKind = SparseTriangular;
-      m_triType = SparseLowerTriangle;
-    } else if (check_flag_set(UpLo_, StrictlyUpper)) {
-      m_sparseKind = SparseTriangular;
-      m_triType = SparseUpperTriangle;
-    } else if (check_flag_set(UpLo_, Lower)) {
-      m_sparseKind = SparseTriangular;
-      m_triType = SparseLowerTriangle;
-    } else if (check_flag_set(UpLo_, Upper)) {
-      m_sparseKind = SparseTriangular;
-      m_triType = SparseUpperTriangle;
-    } else {
-      m_sparseKind = SparseOrdinary;
-      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
-    }
-
-    m_order = SparseOrderDefault;
-  }
-
-  explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
-
-  ~AccelerateImpl() {}
-
-  inline Index cols() const { return m_nCols; }
-  inline Index rows() const { return m_nRows; }
-
-  ComputationInfo info() const {
-    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-    return m_info;
-  }
-
-  void analyzePattern(const MatrixType& matrix);
-
-  void factorize(const MatrixType& matrix);
-
-  void compute(const MatrixType& matrix);
-
-  template <typename Rhs, typename Dest>
-  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
-
-  /** Sets the ordering algorithm to use. */
-  void setOrder(SparseOrder_t order) { m_order = order; }
-
- private:
-  template <typename T>
-  void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
-    const Index nColumnsStarts = a.cols() + 1;
-
-    columnStarts.resize(nColumnsStarts);
-
-    for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
-
-    SparseAttributes_t attributes{};
-    attributes.transpose = false;
-    attributes.triangle = m_triType;
-    attributes.kind = m_sparseKind;
-
-    SparseMatrixStructure structure{};
-    structure.attributes = attributes;
-    structure.rowCount = static_cast<int>(a.rows());
-    structure.columnCount = static_cast<int>(a.cols());
-    structure.blockSize = 1;
-    structure.columnStarts = columnStarts.data();
-    structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
-
-    A.structure = structure;
-    A.data = const_cast<T*>(a.valuePtr());
-  }
-
-  void doAnalysis(AccelSparseMatrix& A) {
-    m_numericFactorization.reset(nullptr);
-
-    SparseSymbolicFactorOptions opts{};
-    opts.control = SparseDefaultControl;
-    opts.orderMethod = m_order;
-    opts.order = nullptr;
-    opts.ignoreRowsAndColumns = nullptr;
-    opts.malloc = malloc;
-    opts.free = free;
-    opts.reportError = nullptr;
-
-    m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
-
-    SparseStatus_t status = m_symbolicFactorization->status;
-
-    updateInfoStatus(status);
-
-    if (status != SparseStatusOK) m_symbolicFactorization.reset(nullptr);
-  }
-
-  void doFactorization(AccelSparseMatrix& A) {
-    SparseStatus_t status = SparseStatusReleased;
-
-    if (m_symbolicFactorization) {
-      m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
-
-      status = m_numericFactorization->status;
-
-      if (status != SparseStatusOK) m_numericFactorization.reset(nullptr);
-    }
-
-    updateInfoStatus(status);
-  }
-
- protected:
-  void updateInfoStatus(SparseStatus_t status) const {
-    switch (status) {
-      case SparseStatusOK:
-        m_info = Success;
-        break;
-      case SparseFactorizationFailed:
-      case SparseMatrixIsSingular:
-        m_info = NumericalIssue;
-        break;
-      case SparseInternalError:
-      case SparseParameterError:
-      case SparseStatusReleased:
-      default:
-        m_info = InvalidInput;
-        break;
-    }
-  }
-
-  mutable ComputationInfo m_info;
-  Index m_nRows, m_nCols;
-  std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
-  std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
-  SparseKind_t m_sparseKind;
-  SparseTriangle_t m_triType;
-  SparseOrder_t m_order;
-};
-
-/** Computes the symbolic and numeric decomposition of matrix \a a */
-template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
-void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
-  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
-
-  m_nRows = a.rows();
-  m_nCols = a.cols();
-
-  AccelSparseMatrix A{};
-  std::vector<long> columnStarts;
-
-  buildAccelSparseMatrix(a, A, columnStarts);
-
-  doAnalysis(A);
-
-  if (m_symbolicFactorization) doFactorization(A);
-
-  m_isInitialized = true;
-}
-
-/** Performs a symbolic decomposition on the sparsity pattern of matrix \a a.
- *
- * This function is particularly useful when solving for several problems having the same structure.
- *
- * \sa factorize()
- */
-template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
-void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
-  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
-
-  m_nRows = a.rows();
-  m_nCols = a.cols();
-
-  AccelSparseMatrix A{};
-  std::vector<long> columnStarts;
-
-  buildAccelSparseMatrix(a, A, columnStarts);
-
-  doAnalysis(A);
-
-  m_isInitialized = true;
-}
-
-/** Performs a numeric decomposition of matrix \a a.
- *
- * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
- * performed.
- *
- * \sa analyzePattern()
- */
-template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
-void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
-  eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
-  eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
-
-  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
-
-  AccelSparseMatrix A{};
-  std::vector<long> columnStarts;
-
-  buildAccelSparseMatrix(a, A, columnStarts);
-
-  doFactorization(A);
-}
-
-template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
-template <typename Rhs, typename Dest>
-void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
-                                                                              MatrixBase<Dest>& x) const {
-  if (!m_numericFactorization) {
-    m_info = InvalidInput;
-    return;
-  }
-
-  eigen_assert(m_nRows == b.rows());
-  eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
-
-  SparseStatus_t status = SparseStatusOK;
-
-  Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
-  Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
-
-  AccelDenseMatrix xmat{};
-  xmat.attributes = SparseAttributes_t();
-  xmat.columnCount = static_cast<int>(x.cols());
-  xmat.rowCount = static_cast<int>(x.rows());
-  xmat.columnStride = xmat.rowCount;
-  xmat.data = x_ptr;
-
-  AccelDenseMatrix bmat{};
-  bmat.attributes = SparseAttributes_t();
-  bmat.columnCount = static_cast<int>(b.cols());
-  bmat.rowCount = static_cast<int>(b.rows());
-  bmat.columnStride = bmat.rowCount;
-  bmat.data = b_ptr;
-
-  SparseSolve(*m_numericFactorization, bmat, xmat);
-
-  updateInfoStatus(status);
-}
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_ACCELERATESUPPORT_H

Eigen/src/AccelerateSupport/InternalHeaderCheck.h

@@ -1,3 +0,0 @@
-#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
-#error "Please include Eigen/AccelerateSupport instead of including headers inside the src directory directly."
-#endif

Eigen/src/CMakeLists.txt

@@ -0,0 +1,7 @@
+file(GLOB Eigen_src_subdirectories "*")
+escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
+foreach(f ${Eigen_src_subdirectories})
+  if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" )
+    add_subdirectory(${f})
+  endif()
+endforeach()

Eigen/src/Cholesky/CMakeLists.txt

@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Cholesky_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Cholesky_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
+  )

Eigen/src/Cholesky/InternalHeaderCheck.h

@@ -1,3 +0,0 @@
-#ifndef EIGEN_CHOLESKY_MODULE_H
-#error "Please include Eigen/Cholesky instead of including headers inside the src directory directly."
-#endif

Eigen/src/Cholesky/LDLT.h

@@ -1,326 +1,298 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_LDLT_H
 #define EIGEN_LDLT_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 namespace internal {
-template <typename MatrixType_, int UpLo_>
-struct traits<LDLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
+template<typename MatrixType, int UpLo> struct LDLT_Traits;
+}
 
-template <typename MatrixType, int UpLo>
-struct LDLT_Traits;
+/** \ingroup cholesky_Module
+  *
+  * \class LDLT
+  *
+  * \brief Robust Cholesky decomposition of a matrix with pivoting
+  *
+  * \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
+  *
+  * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
+  * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
+  * is lower triangular with a unit diagonal and D is a diagonal matrix.
+  *
+  * The decomposition uses pivoting to ensure stability, so that L will have
+  * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
+  * on D also stabilizes the computation.
+  *
+  * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
+	* decomposition to determine whether a system of equations has a solution.
+  *
+  * \sa MatrixBase::ldlt(), class LLT
+  */
+ /* THIS PART OF THE DOX IS CURRENTLY DISABLED BECAUSE INACCURATE BECAUSE OF BUG IN THE DECOMPOSITION CODE
+  * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
+  * the strict lower part does not have to store correct values.
+  */
+template<typename _MatrixType, int _UpLo> class LDLT
+{
+  public:
+    typedef _MatrixType MatrixType;
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+      UpLo = _UpLo
+    };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
 
-// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
-enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
-}  // namespace internal
+    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
+    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
 
-/** \ingroup Cholesky_Module
- *
- * \class LDLT
- *
- * \brief Robust Cholesky decomposition of a matrix with pivoting
- *
- * \tparam MatrixType_ the type of the matrix of which to compute the LDL^T Cholesky decomposition
- * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
- *             The other triangular part won't be read.
- *
- * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
- * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
- * is lower triangular with a unit diagonal and D is a diagonal matrix.
- *
- * The decomposition uses pivoting to ensure stability, so that D will have
- * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
- * on D also stabilizes the computation.
- *
- * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
- * decomposition to determine whether a system of equations has a solution.
- *
- * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
- *
- * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
- */
-template <typename MatrixType_, int UpLo_>
-class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
- public:
-  typedef MatrixType_ MatrixType;
-  typedef SolverBase<LDLT> Base;
-  friend class SolverBase<LDLT>;
+    typedef internal::LDLT_Traits<MatrixType,UpLo> Traits;
 
-  EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
-  enum {
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    UpLo = UpLo_
-  };
-  typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
+    /** \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via LDLT::compute(const MatrixType&).
+      */
+    LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {}
 
-  typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
-  typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
-
-  typedef internal::LDLT_Traits<MatrixType, UpLo> Traits;
-
-  /** \brief Default Constructor.
-   *
-   * The default constructor is useful in cases in which the user intends to
-   * perform decompositions via LDLT::compute(const MatrixType&).
-   */
-  LDLT() : m_matrix(), m_transpositions(), m_sign(internal::ZeroSign), m_isInitialized(false) {}
-
-  /** \brief Default Constructor with memory preallocation
-   *
-   * Like the default constructor but with preallocation of the internal data
-   * according to the specified problem \a size.
-   * \sa LDLT()
-   */
-  explicit LDLT(Index size)
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa LDLT()
+      */
+    LDLT(Index size)
       : m_matrix(size, size),
         m_transpositions(size),
         m_temporary(size),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false) {}
+        m_isInitialized(false)
+    {}
 
-  /** \brief Constructor with decomposition
-   *
-   * This calculates the decomposition for the input \a matrix.
-   *
-   * \sa LDLT(Index size)
-   */
-  template <typename InputType>
-  explicit LDLT(const EigenBase<InputType>& matrix)
+    LDLT(const MatrixType& matrix)
       : m_matrix(matrix.rows(), matrix.cols()),
         m_transpositions(matrix.rows()),
         m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false) {
-    compute(matrix.derived());
-  }
+        m_isInitialized(false)
+    {
+      compute(matrix);
+    }
 
-  /** \brief Constructs a LDLT factorization from a given matrix
-   *
-   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
-   * MatrixType is a Eigen::Ref.
-   *
-   * \sa LDLT(const EigenBase&)
-   */
-  template <typename InputType>
-  explicit LDLT(EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_transpositions(matrix.rows()),
-        m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false) {
-    compute(matrix.derived());
-  }
+    /** \returns a view of the upper triangular matrix U */
+    inline typename Traits::MatrixU matrixU() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return Traits::getU(m_matrix);
+    }
 
-  /** Clear any existing decomposition
-   * \sa rankUpdate(w,sigma)
-   */
-  void setZero() { m_isInitialized = false; }
+    /** \returns a view of the lower triangular matrix L */
+    inline typename Traits::MatrixL matrixL() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return Traits::getL(m_matrix);
+    }
 
-  /** \returns a view of the upper triangular matrix U */
-  inline typename Traits::MatrixU matrixU() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return Traits::getU(m_matrix);
-  }
+    /** \returns the permutation matrix P as a transposition sequence.
+      */
+    inline const TranspositionType& transpositionsP() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_transpositions;
+    }
 
-  /** \returns a view of the lower triangular matrix L */
-  inline typename Traits::MatrixL matrixL() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return Traits::getL(m_matrix);
-  }
+    /** \returns the coefficients of the diagonal matrix D */
+    inline Diagonal<const MatrixType> vectorD(void) const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_matrix.diagonal();
+    }
 
-  /** \returns the permutation matrix P as a transposition sequence.
-   */
-  inline const TranspositionType& transpositionsP() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_transpositions;
-  }
+    /** \returns true if the matrix is positive (semidefinite) */
+    inline bool isPositive(void) const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_sign == 1;
+    }
+    
+    #ifdef EIGEN2_SUPPORT
+    inline bool isPositiveDefinite() const
+    {
+      return isPositive();
+    }
+    #endif
 
-  /** \returns the coefficients of the diagonal matrix D */
-  inline Diagonal<const MatrixType> vectorD() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_matrix.diagonal();
-  }
+    /** \returns true if the matrix is negative (semidefinite) */
+    inline bool isNegative(void) const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_sign == -1;
+    }
 
-  /** \returns true if the matrix is positive (semidefinite) */
-  inline bool isPositive() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
-  }
+    /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
+      *
+      * \note_about_checking_solutions
+      *
+      * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
+      * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$, 
+      * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
+      * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
+      * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
+      * computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
+      *
+      * \sa MatrixBase::ldlt()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<LDLT, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      eigen_assert(m_matrix.rows()==b.rows()
+                && "LDLT::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<LDLT, Rhs>(*this, b.derived());
+    }
 
-  /** \returns true if the matrix is negative (semidefinite) */
-  inline bool isNegative(void) const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
-  }
+    #ifdef EIGEN2_SUPPORT
+    template<typename OtherDerived, typename ResultType>
+    bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
+    {
+      *result = this->solve(b);
+      return true;
+    }
+    #endif
 
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
-   *
-   * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
-   *
-   * \note_about_checking_solutions
-   *
-   * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
-   * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
-   * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
-   * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
-   * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
-   * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
-   *
-   * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
-   */
-  template <typename Rhs>
-  inline const Solve<LDLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
-#endif
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
 
-  template <typename Derived>
-  bool solveInPlace(MatrixBase<Derived>& bAndX) const;
+    LDLT& compute(const MatrixType& matrix);
 
-  template <typename InputType>
-  LDLT& compute(const EigenBase<InputType>& matrix);
+    /** \returns the internal LDLT decomposition matrix
+      *
+      * TODO: document the storage layout
+      */
+    inline const MatrixType& matrixLDLT() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_matrix;
+    }
 
-  /** \returns an estimate of the reciprocal condition number of the matrix of
-   *  which \c *this is the LDLT decomposition.
-   */
-  RealScalar rcond() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return internal::rcond_estimate_helper(m_l1_norm, *this);
-  }
+    MatrixType reconstructedMatrix() const;
 
-  template <typename Derived>
-  LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha = 1);
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
 
-  /** \returns the internal LDLT decomposition matrix
-   *
-   * TODO: document the storage layout
-   */
-  inline const MatrixType& matrixLDLT() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_matrix;
-  }
+  protected:
 
-  MatrixType reconstructedMatrix() const;
-
-  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
-   * is self-adjoint.
-   *
-   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
-   * \code x = decomposition.adjoint().solve(b) \endcode
-   */
-  const LDLT& adjoint() const { return *this; }
-
-  EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-  EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-  /** \brief Reports whether previous computation was successful.
-   *
-   * \returns \c Success if computation was successful,
-   *          \c NumericalIssue if the factorization failed because of a zero pivot.
-   */
-  ComputationInfo info() const {
-    eigen_assert(m_isInitialized && "LDLT is not initialized.");
-    return m_info;
-  }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  template <typename RhsType, typename DstType>
-  void _solve_impl(const RhsType& rhs, DstType& dst) const;
-
-  template <bool Conjugate, typename RhsType, typename DstType>
-  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
-#endif
-
- protected:
-  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-
-  /** \internal
-   * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
-   * The strict upper part is used during the decomposition, the strict lower
-   * part correspond to the coefficients of L (its diagonal is equal to 1 and
-   * is not stored), and the diagonal entries correspond to D.
-   */
-  MatrixType m_matrix;
-  RealScalar m_l1_norm;
-  TranspositionType m_transpositions;
-  TmpMatrixType m_temporary;
-  internal::SignMatrix m_sign;
-  bool m_isInitialized;
-  ComputationInfo m_info;
+    /** \internal
+      * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
+      * The strict upper part is used during the decomposition, the strict lower
+      * part correspond to the coefficients of L (its diagonal is equal to 1 and
+      * is not stored), and the diagonal entries correspond to D.
+      */
+    MatrixType m_matrix;
+    TranspositionType m_transpositions;
+    TmpMatrixType m_temporary;
+    int m_sign;
+    bool m_isInitialized;
 };
 
 namespace internal {
 
-template <int UpLo>
-struct ldlt_inplace;
+template<int UpLo> struct ldlt_inplace;
 
-template <>
-struct ldlt_inplace<Lower> {
-  template <typename MatrixType, typename TranspositionType, typename Workspace>
-  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) {
-    using std::abs;
+template<> struct ldlt_inplace<Lower>
+{
+  template<typename MatrixType, typename TranspositionType, typename Workspace>
+  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
+  {
     typedef typename MatrixType::Scalar Scalar;
     typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename TranspositionType::StorageIndex IndexType;
-    eigen_assert(mat.rows() == mat.cols());
+    typedef typename MatrixType::Index Index;
+    eigen_assert(mat.rows()==mat.cols());
     const Index size = mat.rows();
-    bool found_zero_pivot = false;
-    bool ret = true;
 
-    if (size <= 1) {
+    if (size <= 1)
+    {
       transpositions.setIdentity();
-      if (size == 0)
-        sign = ZeroSign;
-      else if (numext::real(mat.coeff(0, 0)) > static_cast<RealScalar>(0))
-        sign = PositiveSemiDef;
-      else if (numext::real(mat.coeff(0, 0)) < static_cast<RealScalar>(0))
-        sign = NegativeSemiDef;
-      else
-        sign = ZeroSign;
+      if(sign)
+        *sign = real(mat.coeff(0,0))>0 ? 1:-1;
       return true;
     }
 
-    for (Index k = 0; k < size; ++k) {
+    RealScalar cutoff = 0, biggest_in_corner;
+
+    for (Index k = 0; k < size; ++k)
+    {
       // Find largest diagonal element
       Index index_of_biggest_in_corner;
-      mat.diagonal().tail(size - k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
+      biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
       index_of_biggest_in_corner += k;
 
-      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
-      if (k != index_of_biggest_in_corner) {
+      if(k == 0)
+      {
+        // The biggest overall is the point of reference to which further diagonals
+        // are compared; if any diagonal is negligible compared
+        // to the largest overall, the algorithm bails.
+        cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
+
+        if(sign)
+          *sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
+      }
+
+      // Finish early if the matrix is not full rank.
+      if(biggest_in_corner < cutoff)
+      {
+        for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
+        break;
+      }
+
+      transpositions.coeffRef(k) = index_of_biggest_in_corner;
+      if(k != index_of_biggest_in_corner)
+      {
         // apply the transposition while taking care to consider only
         // the lower triangular part
-        Index s = size - index_of_biggest_in_corner - 1;  // trailing size after the biggest element
+        Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
         mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
         mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
-        std::swap(mat.coeffRef(k, k), mat.coeffRef(index_of_biggest_in_corner, index_of_biggest_in_corner));
-        for (Index i = k + 1; i < index_of_biggest_in_corner; ++i) {
-          Scalar tmp = mat.coeffRef(i, k);
-          mat.coeffRef(i, k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner, i));
-          mat.coeffRef(index_of_biggest_in_corner, i) = numext::conj(tmp);
+        std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
+        for(int i=k+1;i<index_of_biggest_in_corner;++i)
+        {
+          Scalar tmp = mat.coeffRef(i,k);
+          mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
+          mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
         }
-        if (NumTraits<Scalar>::IsComplex)
-          mat.coeffRef(index_of_biggest_in_corner, k) = numext::conj(mat.coeff(index_of_biggest_in_corner, k));
+        if(NumTraits<Scalar>::IsComplex)
+          mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
       }
 
       // partition the matrix:
@@ -328,273 +300,136 @@ struct ldlt_inplace<Lower> {
       // lu  = A10 | A11 |  -
       //       A20 | A21 | A22
       Index rs = size - k - 1;
-      Block<MatrixType, Dynamic, 1> A21(mat, k + 1, k, rs, 1);
-      Block<MatrixType, 1, Dynamic> A10(mat, k, 0, 1, k);
-      Block<MatrixType, Dynamic, Dynamic> A20(mat, k + 1, 0, rs, k);
+      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
+      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
+      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
 
-      if (k > 0) {
-        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
-        mat.coeffRef(k, k) -= (A10 * temp.head(k)).value();
-        if (rs > 0) A21.noalias() -= A20 * temp.head(k);
-      }
-
-      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
-      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
-      // we should only make sure that we do not introduce INF or NaN values.
-      // Remark that LAPACK also uses 0 as the cutoff value.
-      RealScalar realAkk = numext::real(mat.coeffRef(k, k));
-      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
-
-      if (k == 0 && !pivot_is_valid) {
-        // The entire diagonal is zero, there is nothing more to do
-        // except filling the transpositions, and checking whether the matrix is zero.
-        sign = ZeroSign;
-        for (Index j = 0; j < size; ++j) {
-          transpositions.coeffRef(j) = IndexType(j);
-          ret = ret && (mat.col(j).tail(size - j - 1).array() == Scalar(0)).all();
-        }
-        return ret;
-      }
-
-      if ((rs > 0) && pivot_is_valid)
-        A21 /= realAkk;
-      else if (rs > 0)
-        ret = ret && (A21.array() == Scalar(0)).all();
-
-      if (found_zero_pivot && pivot_is_valid)
-        ret = false;  // factorization failed
-      else if (!pivot_is_valid)
-        found_zero_pivot = true;
-
-      if (sign == PositiveSemiDef) {
-        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
-      } else if (sign == NegativeSemiDef) {
-        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
-      } else if (sign == ZeroSign) {
-        if (realAkk > static_cast<RealScalar>(0))
-          sign = PositiveSemiDef;
-        else if (realAkk < static_cast<RealScalar>(0))
-          sign = NegativeSemiDef;
+      if(k>0)
+      {
+        temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint();
+        mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
+        if(rs>0)
+          A21.noalias() -= A20 * temp.head(k);
       }
+      if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
+        A21 /= mat.coeffRef(k,k);
     }
 
-    return ret;
-  }
-
-  // Reference for the algorithm: Davis and Hager, "Multiple Rank
-  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
-  // Trivial rearrangements of their computations (Timothy E. Holy)
-  // allow their algorithm to work for rank-1 updates even if the
-  // original matrix is not of full rank.
-  // Here only rank-1 updates are implemented, to reduce the
-  // requirement for intermediate storage and improve accuracy
-  template <typename MatrixType, typename WDerived>
-  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w,
-                            const typename MatrixType::RealScalar& sigma = 1) {
-    using numext::isfinite;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-
-    const Index size = mat.rows();
-    eigen_assert(mat.cols() == size && w.size() == size);
-
-    RealScalar alpha = 1;
-
-    // Apply the update
-    for (Index j = 0; j < size; j++) {
-      // Check for termination due to an original decomposition of low-rank
-      if (!(isfinite)(alpha)) break;
-
-      // Update the diagonal terms
-      RealScalar dj = numext::real(mat.coeff(j, j));
-      Scalar wj = w.coeff(j);
-      RealScalar swj2 = sigma * numext::abs2(wj);
-      RealScalar gamma = dj * alpha + swj2;
-
-      mat.coeffRef(j, j) += swj2 / alpha;
-      alpha += swj2 / dj;
-
-      // Update the terms of L
-      Index rs = size - j - 1;
-      w.tail(rs) -= wj * mat.col(j).tail(rs);
-      if (!numext::is_exactly_zero(gamma)) mat.col(j).tail(rs) += (sigma * numext::conj(wj) / gamma) * w.tail(rs);
-    }
     return true;
   }
-
-  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
-  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w,
-                     const typename MatrixType::RealScalar& sigma = 1) {
-    // Apply the permutation to the input w
-    tmp = transpositions * w;
-
-    return ldlt_inplace<Lower>::updateInPlace(mat, tmp, sigma);
-  }
 };
 
-template <>
-struct ldlt_inplace<Upper> {
-  template <typename MatrixType, typename TranspositionType, typename Workspace>
-  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp,
-                                            SignMatrix& sign) {
+template<> struct ldlt_inplace<Upper>
+{
+  template<typename MatrixType, typename TranspositionType, typename Workspace>
+  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
+  {
     Transpose<MatrixType> matt(mat);
     return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
   }
-
-  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
-  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w,
-                                         const typename MatrixType::RealScalar& sigma = 1) {
-    Transpose<MatrixType> matt(mat);
-    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
-  }
 };
 
-template <typename MatrixType>
-struct LDLT_Traits<MatrixType, Lower> {
-  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
+{
+  typedef TriangularView<MatrixType, UnitLower> MatrixL;
+  typedef TriangularView<typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
+  inline static MatrixL getL(const MatrixType& m) { return m; }
+  inline static MatrixU getU(const MatrixType& m) { return m.adjoint(); }
 };
 
-template <typename MatrixType>
-struct LDLT_Traits<MatrixType, Upper> {
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
-  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
+{
+  typedef TriangularView<typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
+  typedef TriangularView<MatrixType, UnitUpper> MatrixU;
+  inline static MatrixL getL(const MatrixType& m) { return m.adjoint(); }
+  inline static MatrixU getU(const MatrixType& m) { return m; }
 };
 
-}  // end namespace internal
+} // end namespace internal
 
 /** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
- */
-template <typename MatrixType, int UpLo_>
-template <typename InputType>
-LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
-  eigen_assert(a.rows() == a.cols());
+  */
+template<typename MatrixType, int _UpLo>
+LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
+{
+  eigen_assert(a.rows()==a.cols());
   const Index size = a.rows();
 
-  m_matrix = a.derived();
-
-  // Compute matrix L1 norm = max abs column sum.
-  m_l1_norm = RealScalar(0);
-  // TODO move this code to SelfAdjointView
-  for (Index col = 0; col < size; ++col) {
-    RealScalar abs_col_sum;
-    if (UpLo_ == Lower)
-      abs_col_sum =
-          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
-    else
-      abs_col_sum =
-          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
-    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
-  }
+  m_matrix = a;
 
   m_transpositions.resize(size);
   m_isInitialized = false;
   m_temporary.resize(size);
-  m_sign = internal::ZeroSign;
 
-  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success
-                                                                                                    : NumericalIssue;
+  internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, &m_sign);
 
   m_isInitialized = true;
   return *this;
 }
 
-/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
- * \param w a vector to be incorporated into the decomposition.
- * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column
- * vectors. Optional; default value is +1. \sa setZero()
- */
-template <typename MatrixType, int UpLo_>
-template <typename Derived>
-LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::rankUpdate(
-    const MatrixBase<Derived>& w, const typename LDLT<MatrixType, UpLo_>::RealScalar& sigma) {
-  typedef typename TranspositionType::StorageIndex IndexType;
-  const Index size = w.rows();
-  if (m_isInitialized) {
-    eigen_assert(m_matrix.rows() == size);
-  } else {
-    m_matrix.resize(size, size);
-    m_matrix.setZero();
-    m_transpositions.resize(size);
-    for (Index i = 0; i < size; i++) m_transpositions.coeffRef(i) = IndexType(i);
-    m_temporary.resize(size);
-    m_sign = sigma >= 0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
-    m_isInitialized = true;
+namespace internal {
+template<typename _MatrixType, int _UpLo, typename Rhs>
+struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs>
+  : solve_retval_base<LDLT<_MatrixType,_UpLo>, Rhs>
+{
+  typedef LDLT<_MatrixType,_UpLo> LDLTType;
+  EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    eigen_assert(rhs().rows() == dec().matrixLDLT().rows());
+    // dst = P b
+    dst = dec().transpositionsP() * rhs();
+
+    // dst = L^-1 (P b)
+    dec().matrixL().solveInPlace(dst);
+
+    // dst = D^-1 (L^-1 P b)
+    // more precisely, use pseudo-inverse of D (see bug 241)
+    using std::abs;
+    using std::max;
+    typedef typename LDLTType::MatrixType MatrixType;
+    typedef typename LDLTType::Scalar Scalar;
+    typedef typename LDLTType::RealScalar RealScalar;
+    const Diagonal<const MatrixType> vectorD = dec().vectorD();
+    RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(),
+				 RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS
+    for (Index i = 0; i < vectorD.size(); ++i) {
+      if(abs(vectorD(i)) > tolerance)
+	dst.row(i) /= vectorD(i);
+      else
+	dst.row(i).setZero();
+    }
+
+    // dst = L^-T (D^-1 L^-1 P b)
+    dec().matrixU().solveInPlace(dst);
+
+    // dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
+    dst = dec().transpositionsP().transpose() * dst;
   }
-
-  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
-
-  return *this;
+};
 }
 
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template <typename MatrixType_, int UpLo_>
-template <typename RhsType, typename DstType>
-void LDLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
-  _solve_impl_transposed<true>(rhs, dst);
-}
-
-template <typename MatrixType_, int UpLo_>
-template <bool Conjugate, typename RhsType, typename DstType>
-void LDLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
-  // dst = P b
-  dst = m_transpositions * rhs;
-
-  // dst = L^-1 (P b)
-  // dst = L^-*T (P b)
-  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-
-  // dst = D^-* (L^-1 P b)
-  // dst = D^-1 (L^-*T P b)
-  // more precisely, use pseudo-inverse of D (see bug 241)
-  using std::abs;
-  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
-  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
-  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
-  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1)
-  // / NumTraits<RealScalar>::highest()); However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the
-  // highest diagonal element is not well justified and leads to numerical issues in some cases. Moreover, Lapack's
-  // xSYTRS routines use 0 for the tolerance. Using numeric_limits::min() gives us more robustness to denormals.
-  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
-  for (Index i = 0; i < vecD.size(); ++i) {
-    if (abs(vecD(i)) > tolerance)
-      dst.row(i) /= vecD(i);
-    else
-      dst.row(i).setZero();
-  }
-
-  // dst = L^-* (D^-* L^-1 P b)
-  // dst = L^-T (D^-1 L^-*T P b)
-  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
-
-  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
-  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
-  dst = m_transpositions.transpose() * dst;
-}
-#endif
-
 /** \internal use x = ldlt_object.solve(x);
- *
- * This is the \em in-place version of solve().
- *
- * \param bAndX represents both the right-hand side matrix b and result x.
- *
- * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
- *
- * This version avoids a copy when the right hand side matrix b is not
- * needed anymore.
- *
- * \sa LDLT::solve(), MatrixBase::ldlt()
- */
-template <typename MatrixType, int UpLo_>
-template <typename Derived>
-bool LDLT<MatrixType, UpLo_>::solveInPlace(MatrixBase<Derived>& bAndX) const {
+  *
+  * This is the \em in-place version of solve().
+  *
+  * \param bAndX represents both the right-hand side matrix b and result x.
+  *
+  * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
+  *
+  * This version avoids a copy when the right hand side matrix b is not
+  * needed anymore.
+  *
+  * \sa LDLT::solve(), MatrixBase::ldlt()
+  */
+template<typename MatrixType,int _UpLo>
+template<typename Derived>
+bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
   eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  eigen_assert(m_matrix.rows() == bAndX.rows());
+  const Index size = m_matrix.rows();
+  eigen_assert(size == bAndX.rows());
 
   bAndX = this->solve(bAndX);
 
@@ -604,11 +439,12 @@ bool LDLT<MatrixType, UpLo_>::solveInPlace(MatrixBase<Derived>& bAndX) const {
 /** \returns the matrix represented by the decomposition,
  * i.e., it returns the product: P^T L D L^* P.
  * This function is provided for debug purpose. */
-template <typename MatrixType, int UpLo_>
-MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+template<typename MatrixType, int _UpLo>
+MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
+{
   eigen_assert(m_isInitialized && "LDLT is not initialized.");
   const Index size = m_matrix.rows();
-  MatrixType res(size, size);
+  MatrixType res(size,size);
 
   // P
   res.setIdentity();
@@ -616,7 +452,7 @@ MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
   // L^* P
   res = matrixU() * res;
   // D(L^*P)
-  res = vectorD().real().asDiagonal() * res;
+  res = vectorD().asDiagonal() * res;
   // L(DL^*P)
   res = matrixL() * res;
   // P^T (LDL^*P)
@@ -626,24 +462,23 @@ MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
 }
 
 /** \cholesky_module
- * \returns the Cholesky decomposition with full pivoting without square root of \c *this
- * \sa MatrixBase::ldlt()
- */
-template <typename MatrixType, unsigned int UpLo>
+  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+  */
+template<typename MatrixType, unsigned int UpLo>
 inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-SelfAdjointView<MatrixType, UpLo>::ldlt() const {
-  return LDLT<PlainObject, UpLo>(m_matrix);
+SelfAdjointView<MatrixType, UpLo>::ldlt() const
+{
+  return LDLT<PlainObject,UpLo>(m_matrix);
 }
 
 /** \cholesky_module
- * \returns the Cholesky decomposition with full pivoting without square root of \c *this
- * \sa SelfAdjointView::ldlt()
- */
-template <typename Derived>
-inline const LDLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::ldlt() const {
+  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+  */
+template<typename Derived>
+inline const LDLT<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::ldlt() const
+{
   return LDLT<PlainObject>(derived());
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_LDLT_H
+#endif // EIGEN_LDLT_H

Eigen/src/Cholesky/LLT.h

@@ -3,418 +3,309 @@
 //
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_LLT_H
 #define EIGEN_LLT_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename MatrixType_, int UpLo_>
-struct traits<LLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-template <typename MatrixType, int UpLo>
-struct LLT_Traits;
-}  // namespace internal
-
-/** \ingroup Cholesky_Module
- *
- * \class LLT
- *
- * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
- *
- * \tparam MatrixType_ the type of the matrix of which we are computing the LL^T Cholesky decomposition
- * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
- *               The other triangular part won't be read.
- *
- * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
- * matrix A such that A = LL^* = U^*U, where L is lower triangular.
- *
- * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
- * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
- * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
- * situations like generalised eigen problems with hermitian matrices.
- *
- * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive
- * definite matrices, use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine
- * whether a system of equations has a solution.
- *
- * Example: \include LLT_example.cpp
- * Output: \verbinclude LLT_example.out
- *
- * \b Performance: for best performance, it is recommended to use a column-major storage format
- * with the Lower triangular part (the default), or, equivalently, a row-major storage format
- * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
- * step, and rank-updates can be up to 3 times slower.
- *
- * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
- *
- * Note that during the decomposition, only the lower (or upper, as defined by UpLo_) triangular part of A is
- * considered. Therefore, the strict lower part does not have to store correct values.
- *
- * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
- */
-template <typename MatrixType_, int UpLo_>
-class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
- public:
-  typedef MatrixType_ MatrixType;
-  typedef SolverBase<LLT> Base;
-  friend class SolverBase<LLT>;
-
-  EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
-  enum { MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
-
-  enum { PacketSize = internal::packet_traits<Scalar>::size, AlignmentMask = int(PacketSize) - 1, UpLo = UpLo_ };
-
-  typedef internal::LLT_Traits<MatrixType, UpLo> Traits;
-
-  /**
-   * \brief Default Constructor.
-   *
-   * The default constructor is useful in cases in which the user intends to
-   * perform decompositions via LLT::compute(const MatrixType&).
-   */
-  LLT() : m_matrix(), m_isInitialized(false) {}
-
-  /** \brief Default Constructor with memory preallocation
-   *
-   * Like the default constructor but with preallocation of the internal data
-   * according to the specified problem \a size.
-   * \sa LLT()
-   */
-  explicit LLT(Index size) : m_matrix(size, size), m_isInitialized(false) {}
-
-  template <typename InputType>
-  explicit LLT(const EigenBase<InputType>& matrix) : m_matrix(matrix.rows(), matrix.cols()), m_isInitialized(false) {
-    compute(matrix.derived());
-  }
-
-  /** \brief Constructs a LLT factorization from a given matrix
-   *
-   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
-   * \c MatrixType is a Eigen::Ref.
-   *
-   * \sa LLT(const EigenBase&)
-   */
-  template <typename InputType>
-  explicit LLT(EigenBase<InputType>& matrix) : m_matrix(matrix.derived()), m_isInitialized(false) {
-    compute(matrix.derived());
-  }
-
-  /** \returns a view of the upper triangular matrix U */
-  inline typename Traits::MatrixU matrixU() const {
-    eigen_assert(m_isInitialized && "LLT is not initialized.");
-    return Traits::getU(m_matrix);
-  }
-
-  /** \returns a view of the lower triangular matrix L */
-  inline typename Traits::MatrixL matrixL() const {
-    eigen_assert(m_isInitialized && "LLT is not initialized.");
-    return Traits::getL(m_matrix);
-  }
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
-   *
-   * Since this LLT class assumes anyway that the matrix A is invertible, the solution
-   * theoretically exists and is unique regardless of b.
-   *
-   * Example: \include LLT_solve.cpp
-   * Output: \verbinclude LLT_solve.out
-   *
-   * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
-   */
-  template <typename Rhs>
-  inline const Solve<LLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
-#endif
-
-  template <typename Derived>
-  void solveInPlace(const MatrixBase<Derived>& bAndX) const;
-
-  template <typename InputType>
-  LLT& compute(const EigenBase<InputType>& matrix);
-
-  /** \returns an estimate of the reciprocal condition number of the matrix of
-   *  which \c *this is the Cholesky decomposition.
-   */
-  RealScalar rcond() const {
-    eigen_assert(m_isInitialized && "LLT is not initialized.");
-    eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
-    return internal::rcond_estimate_helper(m_l1_norm, *this);
-  }
-
-  /** \returns the LLT decomposition matrix
-   *
-   * TODO: document the storage layout
-   */
-  inline const MatrixType& matrixLLT() const {
-    eigen_assert(m_isInitialized && "LLT is not initialized.");
-    return m_matrix;
-  }
-
-  MatrixType reconstructedMatrix() const;
-
-  /** \brief Reports whether previous computation was successful.
-   *
-   * \returns \c Success if computation was successful,
-   *          \c NumericalIssue if the matrix.appears not to be positive definite.
-   */
-  ComputationInfo info() const {
-    eigen_assert(m_isInitialized && "LLT is not initialized.");
-    return m_info;
-  }
-
-  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
-   * is self-adjoint.
-   *
-   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
-   * \code x = decomposition.adjoint().solve(b) \endcode
-   */
-  const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; }
-
-  inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-  inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-  template <typename VectorType>
-  LLT& rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  template <typename RhsType, typename DstType>
-  void _solve_impl(const RhsType& rhs, DstType& dst) const;
-
-  template <bool Conjugate, typename RhsType, typename DstType>
-  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
-#endif
-
- protected:
-  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-
-  /** \internal
-   * Used to compute and store L
-   * The strict upper part is not used and even not initialized.
-   */
-  MatrixType m_matrix;
-  RealScalar m_l1_norm;
-  bool m_isInitialized;
-  ComputationInfo m_info;
-};
-
-namespace internal {
-
-template <typename Scalar, int UpLo>
-struct llt_inplace;
-
-template <typename MatrixType, typename VectorType>
-static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec,
-                                   const typename MatrixType::RealScalar& sigma) {
-  using std::sqrt;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::ColXpr ColXpr;
-  typedef internal::remove_all_t<ColXpr> ColXprCleaned;
-  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
-  typedef Matrix<Scalar, Dynamic, 1> TempVectorType;
-  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
-
-  Index n = mat.cols();
-  eigen_assert(mat.rows() == n && vec.size() == n);
-
-  TempVectorType temp;
-
-  if (sigma > 0) {
-    // This version is based on Givens rotations.
-    // It is faster than the other one below, but only works for updates,
-    // i.e., for sigma > 0
-    temp = sqrt(sigma) * vec;
-
-    for (Index i = 0; i < n; ++i) {
-      JacobiRotation<Scalar> g;
-      g.makeGivens(mat(i, i), -temp(i), &mat(i, i));
-
-      Index rs = n - i - 1;
-      if (rs > 0) {
-        ColXprSegment x(mat.col(i).tail(rs));
-        TempVecSegment y(temp.tail(rs));
-        apply_rotation_in_the_plane(x, y, g);
-      }
-    }
-  } else {
-    temp = vec;
-    RealScalar beta = 1;
-    for (Index j = 0; j < n; ++j) {
-      RealScalar Ljj = numext::real(mat.coeff(j, j));
-      RealScalar dj = numext::abs2(Ljj);
-      Scalar wj = temp.coeff(j);
-      RealScalar swj2 = sigma * numext::abs2(wj);
-      RealScalar gamma = dj * beta + swj2;
-
-      RealScalar x = dj + swj2 / beta;
-      if (x <= RealScalar(0)) return j;
-      RealScalar nLjj = sqrt(x);
-      mat.coeffRef(j, j) = nLjj;
-      beta += swj2 / dj;
-
-      // Update the terms of L
-      Index rs = n - j - 1;
-      if (rs) {
-        temp.tail(rs) -= (wj / Ljj) * mat.col(j).tail(rs);
-        if (!numext::is_exactly_zero(gamma))
-          mat.col(j).tail(rs) =
-              (nLjj / Ljj) * mat.col(j).tail(rs) + (nLjj * sigma * numext::conj(wj) / gamma) * temp.tail(rs);
-      }
-    }
-  }
-  return -1;
+namespace internal{
+template<typename MatrixType, int UpLo> struct LLT_Traits;
 }
 
-template <typename Scalar>
-struct llt_inplace<Scalar, Lower> {
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template <typename MatrixType>
-  static Index unblocked(MatrixType& mat) {
-    using std::sqrt;
+/** \ingroup cholesky_Module
+  *
+  * \class LLT
+  *
+  * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
+  *
+  * \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
+  *
+  * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
+  * matrix A such that A = LL^* = U^*U, where L is lower triangular.
+  *
+  * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
+  * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
+  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
+  * situations like generalised eigen problems with hermitian matrices.
+  *
+  * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
+  * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
+  * has a solution.
+  *
+  * \sa MatrixBase::llt(), class LDLT
+  */
+ /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
+  * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
+  * the strict lower part does not have to store correct values.
+  */
+template<typename _MatrixType, int _UpLo> class LLT
+{
+  public:
+    typedef _MatrixType MatrixType;
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+    typedef typename MatrixType::Index Index;
 
-    eigen_assert(mat.rows() == mat.cols());
+    enum {
+      PacketSize = internal::packet_traits<Scalar>::size,
+      AlignmentMask = int(PacketSize)-1,
+      UpLo = _UpLo
+    };
+
+    typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
+
+    /**
+      * \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via LLT::compute(const MatrixType&).
+      */
+    LLT() : m_matrix(), m_isInitialized(false) {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa LLT()
+      */
+    LLT(Index size) : m_matrix(size, size),
+                    m_isInitialized(false) {}
+
+    LLT(const MatrixType& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()),
+        m_isInitialized(false)
+    {
+      compute(matrix);
+    }
+
+    /** \returns a view of the upper triangular matrix U */
+    inline typename Traits::MatrixU matrixU() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return Traits::getU(m_matrix);
+    }
+
+    /** \returns a view of the lower triangular matrix L */
+    inline typename Traits::MatrixL matrixL() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return Traits::getL(m_matrix);
+    }
+
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * Since this LLT class assumes anyway that the matrix A is invertible, the solution
+      * theoretically exists and is unique regardless of b.
+      *
+      * Example: \include LLT_solve.cpp
+      * Output: \verbinclude LLT_solve.out
+      *
+      * \sa solveInPlace(), MatrixBase::llt()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<LLT, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      eigen_assert(m_matrix.rows()==b.rows()
+                && "LLT::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<LLT, Rhs>(*this, b.derived());
+    }
+
+    #ifdef EIGEN2_SUPPORT
+    template<typename OtherDerived, typename ResultType>
+    bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
+    {
+      *result = this->solve(b);
+      return true;
+    }
+    
+    bool isPositiveDefinite() const { return true; }
+    #endif
+
+    template<typename Derived>
+    void solveInPlace(MatrixBase<Derived> &bAndX) const;
+
+    LLT& compute(const MatrixType& matrix);
+
+    /** \returns the LLT decomposition matrix
+      *
+      * TODO: document the storage layout
+      */
+    inline const MatrixType& matrixLLT() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return m_matrix;
+    }
+
+    MatrixType reconstructedMatrix() const;
+
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return m_info;
+    }
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+  protected:
+    /** \internal
+      * Used to compute and store L
+      * The strict upper part is not used and even not initialized.
+      */
+    MatrixType m_matrix;
+    bool m_isInitialized;
+    ComputationInfo m_info;
+};
+
+namespace internal {
+
+template<int UpLo> struct llt_inplace;
+
+template<> struct llt_inplace<Lower>
+{
+  template<typename MatrixType>
+  static typename MatrixType::Index unblocked(MatrixType& mat)
+  {
+    typedef typename MatrixType::Index Index;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    
+    eigen_assert(mat.rows()==mat.cols());
     const Index size = mat.rows();
-    for (Index k = 0; k < size; ++k) {
-      Index rs = size - k - 1;  // remaining size
+    for(Index k = 0; k < size; ++k)
+    {
+      Index rs = size-k-1; // remaining size
 
-      Block<MatrixType, Dynamic, 1> A21(mat, k + 1, k, rs, 1);
-      Block<MatrixType, 1, Dynamic> A10(mat, k, 0, 1, k);
-      Block<MatrixType, Dynamic, Dynamic> A20(mat, k + 1, 0, rs, k);
+      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
+      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
+      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
 
-      RealScalar x = numext::real(mat.coeff(k, k));
-      if (k > 0) x -= A10.squaredNorm();
-      if (x <= RealScalar(0)) return k;
-      mat.coeffRef(k, k) = x = sqrt(x);
-      if (k > 0 && rs > 0) A21.noalias() -= A20 * A10.adjoint();
-      if (rs > 0) A21 /= x;
+      RealScalar x = real(mat.coeff(k,k));
+      if (k>0) x -= A10.squaredNorm();
+      if (x<=RealScalar(0))
+        return k;
+      mat.coeffRef(k,k) = x = sqrt(x);
+      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
+      if (rs>0) A21 *= RealScalar(1)/x;
     }
     return -1;
   }
 
-  template <typename MatrixType>
-  static Index blocked(MatrixType& m) {
-    eigen_assert(m.rows() == m.cols());
+  template<typename MatrixType>
+  static typename MatrixType::Index blocked(MatrixType& m)
+  {
+    typedef typename MatrixType::Index Index;
+    eigen_assert(m.rows()==m.cols());
     Index size = m.rows();
-    if (size < 32) return unblocked(m);
+    if(size<32)
+      return unblocked(m);
 
-    Index blockSize = size / 8;
-    blockSize = (blockSize / 16) * 16;
-    blockSize = (std::min)((std::max)(blockSize, Index(8)), Index(128));
+    Index blockSize = size/8;
+    blockSize = (blockSize/16)*16;
+    blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
 
-    for (Index k = 0; k < size; k += blockSize) {
+    for (Index k=0; k<size; k+=blockSize)
+    {
       // partition the matrix:
       //       A00 |  -  |  -
       // lu  = A10 | A11 |  -
       //       A20 | A21 | A22
-      Index bs = (std::min)(blockSize, size - k);
+      Index bs = (std::min)(blockSize, size-k);
       Index rs = size - k - bs;
-      Block<MatrixType, Dynamic, Dynamic> A11(m, k, k, bs, bs);
-      Block<MatrixType, Dynamic, Dynamic> A21(m, k + bs, k, rs, bs);
-      Block<MatrixType, Dynamic, Dynamic> A22(m, k + bs, k + bs, rs, rs);
+      Block<MatrixType,Dynamic,Dynamic> A11(m,k,   k,   bs,bs);
+      Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k,   rs,bs);
+      Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
 
       Index ret;
-      if ((ret = unblocked(A11)) >= 0) return k + ret;
-      if (rs > 0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
-      if (rs > 0)
-        A22.template selfadjointView<Lower>().rankUpdate(A21,
-                                                         typename NumTraits<RealScalar>::Literal(-1));  // bottleneck
+      if((ret=unblocked(A11))>=0) return k+ret;
+      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
+      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,-1); // bottleneck
     }
     return -1;
   }
-
-  template <typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
-    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
-  }
 };
 
-template <typename Scalar>
-struct llt_inplace<Scalar, Upper> {
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  template <typename MatrixType>
-  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat) {
+template<> struct llt_inplace<Upper>
+{
+  template<typename MatrixType>
+  static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat)
+  {
     Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::unblocked(matt);
+    return llt_inplace<Lower>::unblocked(matt);
   }
-  template <typename MatrixType>
-  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat) {
+  template<typename MatrixType>
+  static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat)
+  {
     Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::blocked(matt);
-  }
-  template <typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
+    return llt_inplace<Lower>::blocked(matt);
   }
 };
 
-template <typename MatrixType>
-struct LLT_Traits<MatrixType, Lower> {
-  typedef const TriangularView<const MatrixType, Lower> MatrixL;
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
-  static bool inplace_decomposition(MatrixType& m) {
-    return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m) == -1;
-  }
+template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
+{
+  typedef TriangularView<MatrixType, Lower> MatrixL;
+  typedef TriangularView<typename MatrixType::AdjointReturnType, Upper> MatrixU;
+  inline static MatrixL getL(const MatrixType& m) { return m; }
+  inline static MatrixU getU(const MatrixType& m) { return m.adjoint(); }
+  static bool inplace_decomposition(MatrixType& m)
+  { return llt_inplace<Lower>::blocked(m)==-1; }
 };
 
-template <typename MatrixType>
-struct LLT_Traits<MatrixType, Upper> {
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
-  typedef const TriangularView<const MatrixType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
-  static bool inplace_decomposition(MatrixType& m) {
-    return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m) == -1;
-  }
+template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
+{
+  typedef TriangularView<typename MatrixType::AdjointReturnType, Lower> MatrixL;
+  typedef TriangularView<MatrixType, Upper> MatrixU;
+  inline static MatrixL getL(const MatrixType& m) { return m.adjoint(); }
+  inline static MatrixU getU(const MatrixType& m) { return m; }
+  static bool inplace_decomposition(MatrixType& m)
+  { return llt_inplace<Upper>::blocked(m)==-1; }
 };
 
-}  // end namespace internal
+} // end namespace internal
 
 /** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
- *
- * \returns a reference to *this
- *
- * Example: \include TutorialLinAlgComputeTwice.cpp
- * Output: \verbinclude TutorialLinAlgComputeTwice.out
- */
-template <typename MatrixType, int UpLo_>
-template <typename InputType>
-LLT<MatrixType, UpLo_>& LLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
-  eigen_assert(a.rows() == a.cols());
+  *
+  *
+  * \returns a reference to *this
+  */
+template<typename MatrixType, int _UpLo>
+LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
+{
+  assert(a.rows()==a.cols());
   const Index size = a.rows();
   m_matrix.resize(size, size);
-  if (!internal::is_same_dense(m_matrix, a.derived())) m_matrix = a.derived();
-
-  // Compute matrix L1 norm = max abs column sum.
-  m_l1_norm = RealScalar(0);
-  // TODO move this code to SelfAdjointView
-  for (Index col = 0; col < size; ++col) {
-    RealScalar abs_col_sum;
-    if (UpLo_ == Lower)
-      abs_col_sum =
-          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
-    else
-      abs_col_sum =
-          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
-    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
-  }
+  m_matrix = a;
 
   m_isInitialized = true;
   bool ok = Traits::inplace_decomposition(m_matrix);
@@ -423,60 +314,41 @@ LLT<MatrixType, UpLo_>& LLT<MatrixType, UpLo_>::compute(const EigenBase<InputTyp
   return *this;
 }
 
-/** Performs a rank one update (or dowdate) of the current decomposition.
- * If A = LL^* before the rank one update,
- * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
- * of same dimension.
- */
-template <typename MatrixType_, int UpLo_>
-template <typename VectorType>
-LLT<MatrixType_, UpLo_>& LLT<MatrixType_, UpLo_>::rankUpdate(const VectorType& v, const RealScalar& sigma) {
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
-  eigen_assert(v.size() == m_matrix.cols());
-  eigen_assert(m_isInitialized);
-  if (internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix, v, sigma) >= 0)
-    m_info = NumericalIssue;
-  else
-    m_info = Success;
+namespace internal {
+template<typename _MatrixType, int UpLo, typename Rhs>
+struct solve_retval<LLT<_MatrixType, UpLo>, Rhs>
+  : solve_retval_base<LLT<_MatrixType, UpLo>, Rhs>
+{
+  typedef LLT<_MatrixType,UpLo> LLTType;
+  EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs)
 
-  return *this;
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dst = rhs();
+    dec().solveInPlace(dst);
+  }
+};
 }
 
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template <typename MatrixType_, int UpLo_>
-template <typename RhsType, typename DstType>
-void LLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
-  _solve_impl_transposed<true>(rhs, dst);
-}
-
-template <typename MatrixType_, int UpLo_>
-template <bool Conjugate, typename RhsType, typename DstType>
-void LLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
-  dst = rhs;
-
-  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-  matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
-}
-#endif
-
 /** \internal use x = llt_object.solve(x);
- *
- * This is the \em in-place version of solve().
- *
- * \param bAndX represents both the right-hand side matrix b and result x.
- *
- * This version avoids a copy when the right hand side matrix b is not needed anymore.
- *
- * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
- * This function will const_cast it, so constness isn't honored here.
- *
- * \sa LLT::solve(), MatrixBase::llt()
- */
-template <typename MatrixType, int UpLo_>
-template <typename Derived>
-void LLT<MatrixType, UpLo_>::solveInPlace(const MatrixBase<Derived>& bAndX) const {
+  * 
+  * This is the \em in-place version of solve().
+  *
+  * \param bAndX represents both the right-hand side matrix b and result x.
+  *
+  * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
+  *
+  * This version avoids a copy when the right hand side matrix b is not
+  * needed anymore.
+  *
+  * \sa LLT::solve(), MatrixBase::llt()
+  */
+template<typename MatrixType, int _UpLo>
+template<typename Derived>
+void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
   eigen_assert(m_isInitialized && "LLT is not initialized.");
-  eigen_assert(m_matrix.rows() == bAndX.rows());
+  eigen_assert(m_matrix.rows()==bAndX.rows());
   matrixL().solveInPlace(bAndX);
   matrixU().solveInPlace(bAndX);
 }
@@ -484,31 +356,31 @@ void LLT<MatrixType, UpLo_>::solveInPlace(const MatrixBase<Derived>& bAndX) cons
 /** \returns the matrix represented by the decomposition,
  * i.e., it returns the product: L L^*.
  * This function is provided for debug purpose. */
-template <typename MatrixType, int UpLo_>
-MatrixType LLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+template<typename MatrixType, int _UpLo>
+MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
+{
   eigen_assert(m_isInitialized && "LLT is not initialized.");
   return matrixL() * matrixL().adjoint().toDenseMatrix();
 }
 
 /** \cholesky_module
- * \returns the LLT decomposition of \c *this
- * \sa SelfAdjointView::llt()
- */
-template <typename Derived>
-inline const LLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::llt() const {
+  * \returns the LLT decomposition of \c *this
+  */
+template<typename Derived>
+inline const LLT<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::llt() const
+{
   return LLT<PlainObject>(derived());
 }
 
 /** \cholesky_module
- * \returns the LLT decomposition of \c *this
- * \sa SelfAdjointView::llt()
- */
-template <typename MatrixType, unsigned int UpLo>
-inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::llt()
-    const {
-  return LLT<PlainObject, UpLo>(m_matrix);
+  * \returns the LLT decomposition of \c *this
+  */
+template<typename MatrixType, unsigned int UpLo>
+inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
+SelfAdjointView<MatrixType, UpLo>::llt() const
+{
+  return LLT<PlainObject,UpLo>(m_matrix);
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_LLT_H
+#endif // EIGEN_LLT_H

Eigen/src/Cholesky/LLT_LAPACKE.h

@@ -1,124 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *     LLt decomposition based on LAPACKE_?potrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_LLT_LAPACKE_H
-#define EIGEN_LLT_LAPACKE_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-
-namespace lapacke_helpers {
-// -------------------------------------------------------------------------------------------------------------------
-//        Dispatch for rank update handling upper and lower parts
-// -------------------------------------------------------------------------------------------------------------------
-
-template <UpLoType Mode>
-struct rank_update {};
-
-template <>
-struct rank_update<Lower> {
-  template <typename MatrixType, typename VectorType>
-  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
-    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
-  }
-};
-
-template <>
-struct rank_update<Upper> {
-  template <typename MatrixType, typename VectorType>
-  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
-    Transpose<MatrixType> matt(mat);
-    return Eigen::internal::llt_rank_update_lower(matt, vec.conjugate(), sigma);
-  }
-};
-
-// -------------------------------------------------------------------------------------------------------------------
-//        Generic lapacke llt implementation that hands of to the dispatches
-// -------------------------------------------------------------------------------------------------------------------
-
-template <typename Scalar, UpLoType Mode>
-struct lapacke_llt {
-  EIGEN_STATIC_ASSERT(((Mode == Lower) || (Mode == Upper)), MODE_MUST_BE_UPPER_OR_LOWER)
-  template <typename MatrixType>
-  static Index blocked(MatrixType &m) {
-    eigen_assert(m.rows() == m.cols());
-    if (m.rows() == 0) {
-      return -1;
-    }
-    /* Set up parameters for ?potrf */
-    lapack_int size = to_lapack(m.rows());
-    lapack_int matrix_order = lapack_storage_of(m);
-    constexpr char uplo = Mode == Upper ? 'U' : 'L';
-    Scalar *a = &(m.coeffRef(0, 0));
-    lapack_int lda = to_lapack(m.outerStride());
-
-    lapack_int info = potrf(matrix_order, uplo, size, to_lapack(a), lda);
-    info = (info == 0) ? -1 : info > 0 ? info - 1 : size;
-    return info;
-  }
-
-  template <typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
-    return rank_update<Mode>::run(mat, vec, sigma);
-  }
-};
-}  // namespace lapacke_helpers
-// end namespace lapacke_helpers
-
-/*
- * Here, we just put the generic implementation from lapacke_llt into a full specialization of the llt_inplace
- * type. By being a full specialization, the versions defined here thus get precedence over the generic implementation
- * in LLT.h for double, float and complex double, complex float types.
- */
-
-#define EIGEN_LAPACKE_LLT(EIGTYPE)                                                             \
-  template <>                                                                                  \
-  struct llt_inplace<EIGTYPE, Lower> : public lapacke_helpers::lapacke_llt<EIGTYPE, Lower> {}; \
-  template <>                                                                                  \
-  struct llt_inplace<EIGTYPE, Upper> : public lapacke_helpers::lapacke_llt<EIGTYPE, Upper> {};
-
-EIGEN_LAPACKE_LLT(double)
-EIGEN_LAPACKE_LLT(float)
-EIGEN_LAPACKE_LLT(std::complex<double>)
-EIGEN_LAPACKE_LLT(std::complex<float>)
-
-#undef EIGEN_LAPACKE_LLT
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_LLT_LAPACKE_H

Eigen/src/CholmodSupport/CholmodSupport.h

@@ -1,738 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLMODSUPPORT_H
-#define EIGEN_CHOLMODSUPPORT_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Scalar>
-struct cholmod_configure_matrix;
-
-template <>
-struct cholmod_configure_matrix<double> {
-  template <typename CholmodType>
-  static void run(CholmodType& mat) {
-    mat.xtype = CHOLMOD_REAL;
-    mat.dtype = CHOLMOD_DOUBLE;
-  }
-};
-
-template <>
-struct cholmod_configure_matrix<std::complex<double> > {
-  template <typename CholmodType>
-  static void run(CholmodType& mat) {
-    mat.xtype = CHOLMOD_COMPLEX;
-    mat.dtype = CHOLMOD_DOUBLE;
-  }
-};
-
-// Other scalar types are not yet supported by Cholmod
-// template<> struct cholmod_configure_matrix<float> {
-//   template<typename CholmodType>
-//   static void run(CholmodType& mat) {
-//     mat.xtype = CHOLMOD_REAL;
-//     mat.dtype = CHOLMOD_SINGLE;
-//   }
-// };
-//
-// template<> struct cholmod_configure_matrix<std::complex<float> > {
-//   template<typename CholmodType>
-//   static void run(CholmodType& mat) {
-//     mat.xtype = CHOLMOD_COMPLEX;
-//     mat.dtype = CHOLMOD_SINGLE;
-//   }
-// };
-
-}  // namespace internal
-
-/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
- * Note that the data are shared.
- */
-template <typename Scalar_, int Options_, typename StorageIndex_>
-cholmod_sparse viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, StorageIndex_> > mat) {
-  cholmod_sparse res;
-  res.nzmax = mat.nonZeros();
-  res.nrow = mat.rows();
-  res.ncol = mat.cols();
-  res.p = mat.outerIndexPtr();
-  res.i = mat.innerIndexPtr();
-  res.x = mat.valuePtr();
-  res.z = 0;
-  res.sorted = 1;
-  if (mat.isCompressed()) {
-    res.packed = 1;
-    res.nz = 0;
-  } else {
-    res.packed = 0;
-    res.nz = mat.innerNonZeroPtr();
-  }
-
-  res.dtype = 0;
-  res.stype = -1;
-
-  if (internal::is_same<StorageIndex_, int>::value) {
-    res.itype = CHOLMOD_INT;
-  } else if (internal::is_same<StorageIndex_, SuiteSparse_long>::value) {
-    res.itype = CHOLMOD_LONG;
-  } else {
-    eigen_assert(false && "Index type not supported yet");
-  }
-
-  // setup res.xtype
-  internal::cholmod_configure_matrix<Scalar_>::run(res);
-
-  res.stype = 0;
-
-  return res;
-}
-
-template <typename Scalar_, int Options_, typename Index_>
-const cholmod_sparse viewAsCholmod(const SparseMatrix<Scalar_, Options_, Index_>& mat) {
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
-  return res;
-}
-
-template <typename Scalar_, int Options_, typename Index_>
-const cholmod_sparse viewAsCholmod(const SparseVector<Scalar_, Options_, Index_>& mat) {
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
-  return res;
-}
-
-/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
- * The data are not copied but shared. */
-template <typename Scalar_, int Options_, typename Index_, unsigned int UpLo>
-cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<Scalar_, Options_, Index_>, UpLo>& mat) {
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.matrix().const_cast_derived()));
-
-  if (UpLo == Upper) res.stype = 1;
-  if (UpLo == Lower) res.stype = -1;
-  // swap stype for rowmajor matrices (only works for real matrices)
-  EIGEN_STATIC_ASSERT((Options_ & RowMajorBit) == 0 || NumTraits<Scalar_>::IsComplex == 0,
-                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  if (Options_ & RowMajorBit) res.stype *= -1;
-
-  return res;
-}
-
-/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
- * The data are not copied but shared. */
-template <typename Derived>
-cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat) {
-  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags & RowMajorBit) == 0,
-                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  typedef typename Derived::Scalar Scalar;
-
-  cholmod_dense res;
-  res.nrow = mat.rows();
-  res.ncol = mat.cols();
-  res.nzmax = res.nrow * res.ncol;
-  res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
-  res.x = (void*)(mat.derived().data());
-  res.z = 0;
-
-  internal::cholmod_configure_matrix<Scalar>::run(res);
-
-  return res;
-}
-
-/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
- * The data are not copied but shared. */
-template <typename Scalar, typename StorageIndex>
-Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_sparse& cm) {
-  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
-      cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol], static_cast<StorageIndex*>(cm.p),
-      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
-}
-
-/** Returns a view of the Cholmod sparse matrix factor \a cm as an Eigen sparse matrix.
- * The data are not copied but shared. */
-template <typename Scalar, typename StorageIndex>
-Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_factor& cm) {
-  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
-      cm.n, cm.n, static_cast<StorageIndex*>(cm.p)[cm.n], static_cast<StorageIndex*>(cm.p),
-      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
-}
-
-namespace internal {
-
-// template specializations for int and long that call the correct cholmod method
-
-#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name)                        \
-  template <typename StorageIndex_>                                 \
-  inline ret cm_##name(cholmod_common& Common) {                    \
-    return cholmod_##name(&Common);                                 \
-  }                                                                 \
-  template <>                                                       \
-  inline ret cm_##name<SuiteSparse_long>(cholmod_common & Common) { \
-    return cholmod_l_##name(&Common);                               \
-  }
-
-#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1)                         \
-  template <typename StorageIndex_>                                          \
-  inline ret cm_##name(t1& a1, cholmod_common& Common) {                     \
-    return cholmod_##name(&a1, &Common);                                     \
-  }                                                                          \
-  template <>                                                                \
-  inline ret cm_##name<SuiteSparse_long>(t1 & a1, cholmod_common & Common) { \
-    return cholmod_l_##name(&a1, &Common);                                   \
-  }
-
-EIGEN_CHOLMOD_SPECIALIZE0(int, start)
-EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
-
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense, cholmod_dense*, X)
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
-
-EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
-EIGEN_CHOLMOD_SPECIALIZE1(cholmod_sparse*, factor_to_sparse, cholmod_factor, L)
-
-template <typename StorageIndex_>
-inline cholmod_dense* cm_solve(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
-  return cholmod_solve(sys, &L, &B, &Common);
-}
-template <>
-inline cholmod_dense* cm_solve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
-  return cholmod_l_solve(sys, &L, &B, &Common);
-}
-
-template <typename StorageIndex_>
-inline cholmod_sparse* cm_spsolve(int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common& Common) {
-  return cholmod_spsolve(sys, &L, &B, &Common);
-}
-template <>
-inline cholmod_sparse* cm_spsolve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_sparse& B,
-                                                    cholmod_common& Common) {
-  return cholmod_l_spsolve(sys, &L, &B, &Common);
-}
-
-template <typename StorageIndex_>
-inline int cm_factorize_p(cholmod_sparse* A, double beta[2], StorageIndex_* fset, std::size_t fsize, cholmod_factor* L,
-                          cholmod_common& Common) {
-  return cholmod_factorize_p(A, beta, fset, fsize, L, &Common);
-}
-template <>
-inline int cm_factorize_p<SuiteSparse_long>(cholmod_sparse* A, double beta[2], SuiteSparse_long* fset,
-                                            std::size_t fsize, cholmod_factor* L, cholmod_common& Common) {
-  return cholmod_l_factorize_p(A, beta, fset, fsize, L, &Common);
-}
-
-#undef EIGEN_CHOLMOD_SPECIALIZE0
-#undef EIGEN_CHOLMOD_SPECIALIZE1
-
-}  // namespace internal
-
-enum CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt };
-
-/** \ingroup CholmodSupport_Module
- * \class CholmodBase
- * \brief The base class for the direct Cholesky factorization of Cholmod
- * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
- */
-template <typename MatrixType_, int UpLo_, typename Derived>
-class CholmodBase : public SparseSolverBase<Derived> {
- protected:
-  typedef SparseSolverBase<Derived> Base;
-  using Base::derived;
-  using Base::m_isInitialized;
-
- public:
-  typedef MatrixType_ MatrixType;
-  enum { UpLo = UpLo_ };
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef MatrixType CholMatrixType;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
-
- public:
-  CholmodBase() : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
-    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
-    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
-    internal::cm_start<StorageIndex>(m_cholmod);
-  }
-
-  explicit CholmodBase(const MatrixType& matrix)
-      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
-    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
-    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
-    internal::cm_start<StorageIndex>(m_cholmod);
-    compute(matrix);
-  }
-
-  ~CholmodBase() {
-    if (m_cholmodFactor) internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
-    internal::cm_finish<StorageIndex>(m_cholmod);
-  }
-
-  inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
-  inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
-
-  /** \brief Reports whether previous computation was successful.
-   *
-   * \returns \c Success if computation was successful,
-   *          \c NumericalIssue if the matrix.appears to be negative.
-   */
-  ComputationInfo info() const {
-    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-    return m_info;
-  }
-
-  /** Computes the sparse Cholesky decomposition of \a matrix */
-  Derived& compute(const MatrixType& matrix) {
-    analyzePattern(matrix);
-    factorize(matrix);
-    return derived();
-  }
-
-  /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
-   *
-   * This function is particularly useful when solving for several problems having the same structure.
-   *
-   * \sa factorize()
-   */
-  void analyzePattern(const MatrixType& matrix) {
-    if (m_cholmodFactor) {
-      internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
-      m_cholmodFactor = 0;
-    }
-    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
-    m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
-
-    this->m_isInitialized = true;
-    this->m_info = Success;
-    m_analysisIsOk = true;
-    m_factorizationIsOk = false;
-  }
-
-  /** Performs a numeric decomposition of \a matrix
-   *
-   * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
-   * performed.
-   *
-   * \sa analyzePattern()
-   */
-  void factorize(const MatrixType& matrix) {
-    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
-    internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
-
-    // If the factorization failed, either the input matrix was zero (so m_cholmodFactor == nullptr), or minor is the
-    // column at which it failed. On success minor == n.
-    this->m_info =
-        (m_cholmodFactor != nullptr && m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
-    m_factorizationIsOk = true;
-  }
-
-  /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
-   *  See the Cholmod user guide for details. */
-  cholmod_common& cholmod() { return m_cholmod; }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  /** \internal */
-  template <typename Rhs, typename Dest>
-  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
-    eigen_assert(m_factorizationIsOk &&
-                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
-                 "symbolic()/numeric()");
-    const Index size = m_cholmodFactor->n;
-    EIGEN_UNUSED_VARIABLE(size);
-    eigen_assert(size == b.rows());
-
-    // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
-    Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor> > b_ref(b.derived());
-
-    cholmod_dense b_cd = viewAsCholmod(b_ref);
-    cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
-    if (!x_cd) {
-      this->m_info = NumericalIssue;
-      return;
-    }
-    // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
-    // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
-    dest = Matrix<Scalar, Dest::RowsAtCompileTime, Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),
-                                                                                 b.rows(), b.cols());
-    internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
-  }
-
-  /** \internal */
-  template <typename RhsDerived, typename DestDerived>
-  void _solve_impl(const SparseMatrixBase<RhsDerived>& b, SparseMatrixBase<DestDerived>& dest) const {
-    eigen_assert(m_factorizationIsOk &&
-                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
-                 "symbolic()/numeric()");
-    const Index size = m_cholmodFactor->n;
-    EIGEN_UNUSED_VARIABLE(size);
-    eigen_assert(size == b.rows());
-
-    // note: cs stands for Cholmod Sparse
-    Ref<SparseMatrix<typename RhsDerived::Scalar, ColMajor, typename RhsDerived::StorageIndex> > b_ref(
-        b.const_cast_derived());
-    cholmod_sparse b_cs = viewAsCholmod(b_ref);
-    cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
-    if (!x_cs) {
-      this->m_info = NumericalIssue;
-      return;
-    }
-    // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
-    // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's
-    // sparse solver)
-    dest.derived() = viewAsEigen<typename DestDerived::Scalar, typename DestDerived::StorageIndex>(*x_cs);
-    internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
-  }
-#endif  // EIGEN_PARSED_BY_DOXYGEN
-
-  /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
-   *
-   * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
-   * \c d_ii = \a offset + \c d_ii
-   *
-   * The default is \a offset=0.
-   *
-   * \returns a reference to \c *this.
-   */
-  Derived& setShift(const RealScalar& offset) {
-    m_shiftOffset[0] = double(offset);
-    return derived();
-  }
-
-  /** \returns the determinant of the underlying matrix from the current factorization */
-  Scalar determinant() const {
-    using std::exp;
-    return exp(logDeterminant());
-  }
-
-  /** \returns the log determinant of the underlying matrix from the current factorization */
-  Scalar logDeterminant() const {
-    using numext::real;
-    using std::log;
-    eigen_assert(m_factorizationIsOk &&
-                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
-                 "symbolic()/numeric()");
-
-    RealScalar logDet = 0;
-    Scalar* x = static_cast<Scalar*>(m_cholmodFactor->x);
-    if (m_cholmodFactor->is_super) {
-      // Supernodal factorization stored as a packed list of dense column-major blocks,
-      // as described by the following structure:
-
-      // super[k] == index of the first column of the j-th super node
-      StorageIndex* super = static_cast<StorageIndex*>(m_cholmodFactor->super);
-      // pi[k] == offset to the description of row indices
-      StorageIndex* pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
-      // px[k] == offset to the respective dense block
-      StorageIndex* px = static_cast<StorageIndex*>(m_cholmodFactor->px);
-
-      Index nb_super_nodes = m_cholmodFactor->nsuper;
-      for (Index k = 0; k < nb_super_nodes; ++k) {
-        StorageIndex ncols = super[k + 1] - super[k];
-        StorageIndex nrows = pi[k + 1] - pi[k];
-
-        Map<const Array<Scalar, 1, Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows + 1));
-        logDet += sk.real().log().sum();
-      }
-    } else {
-      // Simplicial factorization stored as standard CSC matrix.
-      StorageIndex* p = static_cast<StorageIndex*>(m_cholmodFactor->p);
-      Index size = m_cholmodFactor->n;
-      for (Index k = 0; k < size; ++k) logDet += log(real(x[p[k]]));
-    }
-    if (m_cholmodFactor->is_ll) logDet *= 2.0;
-    return logDet;
-  }
-
-  template <typename Stream>
-  void dumpMemory(Stream& /*s*/) {}
-
- protected:
-  mutable cholmod_common m_cholmod;
-  cholmod_factor* m_cholmodFactor;
-  double m_shiftOffset[2];
-  mutable ComputationInfo m_info;
-  int m_factorizationIsOk;
-  int m_analysisIsOk;
-};
-
-/** \ingroup CholmodSupport_Module
- * \class CholmodSimplicialLLT
- * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
- *
- * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
- * using the Cholmod library.
- * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical
- * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
- * either dense or sparse.
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
- *               or Upper. Default is Lower.
- *
- * \implsparsesolverconcept
- *
- * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
- * compressed.
- *
- * \warning Only double precision real and complex scalar types are supported by Cholmod.
- *
- * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
- */
-template <typename MatrixType_, int UpLo_ = Lower>
-class CholmodSimplicialLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT<MatrixType_, UpLo_> > {
-  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT> Base;
-  using Base::m_cholmod;
-
- public:
-  typedef MatrixType_ MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef TriangularView<const MatrixType, Eigen::Lower> MatrixL;
-  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::Upper> MatrixU;
-
-  CholmodSimplicialLLT() : Base() { init(); }
-
-  CholmodSimplicialLLT(const MatrixType& matrix) : Base() {
-    init();
-    this->compute(matrix);
-  }
-
-  ~CholmodSimplicialLLT() {}
-
-  /** \returns an expression of the factor L */
-  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
-
-  /** \returns an expression of the factor U (= L^*) */
-  inline MatrixU matrixU() const { return matrixL().adjoint(); }
-
- protected:
-  void init() {
-    m_cholmod.final_asis = 0;
-    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-    m_cholmod.final_ll = 1;
-  }
-};
-
-/** \ingroup CholmodSupport_Module
- * \class CholmodSimplicialLDLT
- * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
- *
- * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
- * using the Cholmod library.
- * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical
- * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
- * either dense or sparse.
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
- *               or Upper. Default is Lower.
- *
- * \implsparsesolverconcept
- *
- * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
- * compressed.
- *
- * \warning Only double precision real and complex scalar types are supported by Cholmod.
- *
- * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
- */
-template <typename MatrixType_, int UpLo_ = Lower>
-class CholmodSimplicialLDLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT<MatrixType_, UpLo_> > {
-  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT> Base;
-  using Base::m_cholmod;
-
- public:
-  typedef MatrixType_ MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef Matrix<Scalar, Dynamic, 1> VectorType;
-  typedef TriangularView<const MatrixType, Eigen::UnitLower> MatrixL;
-  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
-
-  CholmodSimplicialLDLT() : Base() { init(); }
-
-  CholmodSimplicialLDLT(const MatrixType& matrix) : Base() {
-    init();
-    this->compute(matrix);
-  }
-
-  ~CholmodSimplicialLDLT() {}
-
-  /** \returns a vector expression of the diagonal D */
-  inline VectorType vectorD() const {
-    auto cholmodL = viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor);
-
-    VectorType D{cholmodL.rows()};
-
-    for (Index k = 0; k < cholmodL.outerSize(); ++k) {
-      typename decltype(cholmodL)::InnerIterator it{cholmodL, k};
-      D(k) = it.value();
-    }
-
-    return D;
-  }
-
-  /** \returns an expression of the factor L */
-  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
-
-  /** \returns an expression of the factor U (= L^*) */
-  inline MatrixU matrixU() const { return matrixL().adjoint(); }
-
- protected:
-  void init() {
-    m_cholmod.final_asis = 1;
-    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-  }
-};
-
-/** \ingroup CholmodSupport_Module
- * \class CholmodSupernodalLLT
- * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
- *
- * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
- * using the Cholmod library.
- * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
- * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
- * X and B can be either dense or sparse.
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
- *               or Upper. Default is Lower.
- *
- * \implsparsesolverconcept
- *
- * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
- * compressed.
- *
- * \warning Only double precision real and complex scalar types are supported by Cholmod.
- *
- * \sa \ref TutorialSparseSolverConcept
- */
-template <typename MatrixType_, int UpLo_ = Lower>
-class CholmodSupernodalLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT<MatrixType_, UpLo_> > {
-  typedef CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT> Base;
-  using Base::m_cholmod;
-
- public:
-  typedef MatrixType_ MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-
-  CholmodSupernodalLLT() : Base() { init(); }
-
-  CholmodSupernodalLLT(const MatrixType& matrix) : Base() {
-    init();
-    this->compute(matrix);
-  }
-
-  ~CholmodSupernodalLLT() {}
-
-  /** \returns an expression of the factor L */
-  inline MatrixType matrixL() const {
-    // Convert Cholmod factor's supernodal storage format to Eigen's CSC storage format
-    cholmod_sparse* cholmodL = internal::cm_factor_to_sparse(*Base::m_cholmodFactor, m_cholmod);
-    MatrixType L = viewAsEigen<Scalar, StorageIndex>(*cholmodL);
-    internal::cm_free_sparse<StorageIndex>(cholmodL, m_cholmod);
-
-    return L;
-  }
-
-  /** \returns an expression of the factor U (= L^*) */
-  inline MatrixType matrixU() const { return matrixL().adjoint(); }
-
- protected:
-  void init() {
-    m_cholmod.final_asis = 1;
-    m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
-  }
-};
-
-/** \ingroup CholmodSupport_Module
- * \class CholmodDecomposition
- * \brief A general Cholesky factorization and solver based on Cholmod
- *
- * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
- * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
- * X and B can be either dense or sparse.
- *
- * This variant permits to change the underlying Cholesky method at runtime.
- * On the other hand, it does not provide access to the result of the factorization.
- * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
- *
- * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
- * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
- *               or Upper. Default is Lower.
- *
- * \implsparsesolverconcept
- *
- * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
- * compressed.
- *
- * \warning Only double precision real and complex scalar types are supported by Cholmod.
- *
- * \sa \ref TutorialSparseSolverConcept
- */
-template <typename MatrixType_, int UpLo_ = Lower>
-class CholmodDecomposition : public CholmodBase<MatrixType_, UpLo_, CholmodDecomposition<MatrixType_, UpLo_> > {
-  typedef CholmodBase<MatrixType_, UpLo_, CholmodDecomposition> Base;
-  using Base::m_cholmod;
-
- public:
-  typedef MatrixType_ MatrixType;
-
-  CholmodDecomposition() : Base() { init(); }
-
-  CholmodDecomposition(const MatrixType& matrix) : Base() {
-    init();
-    this->compute(matrix);
-  }
-
-  ~CholmodDecomposition() {}
-
-  void setMode(CholmodMode mode) {
-    switch (mode) {
-      case CholmodAuto:
-        m_cholmod.final_asis = 1;
-        m_cholmod.supernodal = CHOLMOD_AUTO;
-        break;
-      case CholmodSimplicialLLt:
-        m_cholmod.final_asis = 0;
-        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-        m_cholmod.final_ll = 1;
-        break;
-      case CholmodSupernodalLLt:
-        m_cholmod.final_asis = 1;
-        m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
-        break;
-      case CholmodLDLt:
-        m_cholmod.final_asis = 1;
-        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-        break;
-      default:
-        break;
-    }
-  }
-
- protected:
-  void init() {
-    m_cholmod.final_asis = 1;
-    m_cholmod.supernodal = CHOLMOD_AUTO;
-  }
-};
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CHOLMODSUPPORT_H

Eigen/src/CholmodSupport/InternalHeaderCheck.h

@@ -1,3 +0,0 @@
-#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
-#error "Please include Eigen/CholmodSupport instead of including headers inside the src directory directly."
-#endif

Eigen/src/Core/ArithmeticSequence.h

@@ -1,239 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
-#define EIGEN_ARITHMETIC_SEQUENCE_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-
-// Helper to cleanup the type of the increment:
-template <typename T>
-struct cleanup_seq_incr {
-  typedef typename cleanup_index_type<T, DynamicIndex>::type type;
-};
-
-}  // namespace internal
-
-//--------------------------------------------------------------------------------
-// seq(first,last,incr) and seqN(first,size,incr)
-//--------------------------------------------------------------------------------
-
-template <typename FirstType = Index, typename SizeType = Index, typename IncrType = internal::FixedInt<1> >
-class ArithmeticSequence;
-
-template <typename FirstType, typename SizeType, typename IncrType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                   typename internal::cleanup_index_type<SizeType>::type,
-                   typename internal::cleanup_seq_incr<IncrType>::type>
-seqN(FirstType first, SizeType size, IncrType incr);
-
-/** \class ArithmeticSequence
- * \ingroup Core_Module
- *
- * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
- * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
- * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
- *
- * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
- * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
- * only way it is used.
- *
- * \tparam FirstType type of the first element, usually an Index,
- *                   but internally it can be a symbolic expression
- * \tparam SizeType type representing the size of the sequence, usually an Index
- *                  or a compile time integral constant. Internally, it can also be a symbolic expression
- * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is
- * compile-time 1)
- *
- * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
- */
-template <typename FirstType, typename SizeType, typename IncrType>
-class ArithmeticSequence {
- public:
-  constexpr ArithmeticSequence() = default;
-  constexpr ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
-  constexpr ArithmeticSequence(FirstType first, SizeType size, IncrType incr)
-      : m_first(first), m_size(size), m_incr(incr) {}
-
-  enum {
-    // SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
-    IncrAtCompileTime = internal::get_fixed_value<IncrType, DynamicIndex>::value
-  };
-
-  /** \returns the size, i.e., number of elements, of the sequence */
-  constexpr Index size() const { return m_size; }
-
-  /** \returns the first element \f$ a_0 \f$ in the sequence */
-  constexpr Index first() const { return m_first; }
-
-  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
-  constexpr Index operator[](Index i) const { return m_first + i * m_incr; }
-
-  constexpr const FirstType& firstObject() const { return m_first; }
-  constexpr const SizeType& sizeObject() const { return m_size; }
-  constexpr const IncrType& incrObject() const { return m_incr; }
-
- protected:
-  FirstType m_first;
-  SizeType m_size;
-  IncrType m_incr;
-
- public:
-  constexpr auto reverse() const -> decltype(Eigen::seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr)) {
-    return seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr);
-  }
-};
-
-/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
- *
- * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
-template <typename FirstType, typename SizeType, typename IncrType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                   typename internal::cleanup_index_type<SizeType>::type,
-                   typename internal::cleanup_seq_incr<IncrType>::type>
-seqN(FirstType first, SizeType size, IncrType incr) {
-  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                            typename internal::cleanup_index_type<SizeType>::type,
-                            typename internal::cleanup_seq_incr<IncrType>::type>(first, size, incr);
-}
-
-/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
- *
- * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
-template <typename FirstType, typename SizeType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                   typename internal::cleanup_index_type<SizeType>::type>
-seqN(FirstType first, SizeType size) {
-  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                            typename internal::cleanup_index_type<SizeType>::type>(first, size);
-}
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a
- * incr
- *
- * It is essentially an alias to:
- * \code
- * seqN(f, (l-f+incr)/incr, incr);
- * \endcode
- *
- * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
- */
-template <typename FirstType, typename LastType, typename IncrType>
-auto seq(FirstType f, LastType l, IncrType incr);
-
-/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
- *
- * It is essentially an alias to:
- * \code
- * seqN(f,l-f+1);
- * \endcode
- *
- * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
- */
-template <typename FirstType, typename LastType>
-auto seq(FirstType f, LastType l);
-
-#else  // EIGEN_PARSED_BY_DOXYGEN
-
-template <typename FirstType, typename LastType>
-auto seq(FirstType f, LastType l)
-    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                     (typename internal::cleanup_index_type<LastType>::type(l) -
-                      typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()))) {
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (typename internal::cleanup_index_type<LastType>::type(l) -
-               typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()));
-}
-
-template <typename FirstType, typename LastType, typename IncrType>
-auto seq(FirstType f, LastType l, IncrType incr)
-    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                     (typename internal::cleanup_index_type<LastType>::type(l) -
-                      typename internal::cleanup_index_type<FirstType>::type(f) +
-                      typename internal::cleanup_seq_incr<IncrType>::type(incr)) /
-                         typename internal::cleanup_seq_incr<IncrType>::type(incr),
-                     typename internal::cleanup_seq_incr<IncrType>::type(incr))) {
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (typename internal::cleanup_index_type<LastType>::type(l) -
-               typename internal::cleanup_index_type<FirstType>::type(f) + CleanedIncrType(incr)) /
-                  CleanedIncrType(incr),
-              CleanedIncrType(incr));
-}
-
-#endif  // EIGEN_PARSED_BY_DOXYGEN
-
-namespace placeholders {
-
-/** \cpp11
- * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
- *
- * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
- *
- * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
-template <typename SizeType, typename IncrType>
-auto lastN(SizeType size, IncrType incr)
-    -> decltype(seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr)) {
-  return seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr);
-}
-
-/** \cpp11
- * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
- *
- *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
- *
- * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
-template <typename SizeType>
-auto lastN(SizeType size) -> decltype(seqN(Eigen::placeholders::last + fix<1>() - size, size)) {
-  return seqN(Eigen::placeholders::last + fix<1>() - size, size);
-}
-
-}  // namespace placeholders
-
-/** \namespace Eigen::indexing
-  * \ingroup Core_Module
-  *
-  * The sole purpose of this namespace is to be able to import all functions
-  * and symbols that are expected to be used within operator() for indexing
-  * and slicing. If you already imported the whole Eigen namespace:
-  * \code using namespace Eigen; \endcode
-  * then you are already all set. Otherwise, if you don't want/cannot import
-  * the whole Eigen namespace, the following line:
-  * \code using namespace Eigen::indexing; \endcode
-  * is equivalent to:
-  * \code
-  using Eigen::fix;
-  using Eigen::seq;
-  using Eigen::seqN;
-  using Eigen::placeholders::all;
-  using Eigen::placeholders::last;
-  using Eigen::placeholders::lastN;  // c++11 only
-  using Eigen::placeholders::lastp1;
-  \endcode
-  */
-namespace indexing {
-using Eigen::fix;
-using Eigen::seq;
-using Eigen::seqN;
-using Eigen::placeholders::all;
-using Eigen::placeholders::last;
-using Eigen::placeholders::lastN;
-using Eigen::placeholders::lastp1;
-}  // namespace indexing
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_ARITHMETIC_SEQUENCE_H

Eigen/src/Core/Array.h

@@ -3,365 +3,318 @@
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_ARRAY_H
 #define EIGEN_ARRAY_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
+/** \class Array 
+  * \ingroup Core_Module
+  *
+  * \brief General-purpose arrays with easy API for coefficient-wise operations
+  *
+  * The %Array class is very similar to the Matrix class. It provides
+  * general-purpose one- and two-dimensional arrays. The difference between the
+  * %Array and the %Matrix class is primarily in the API: the API for the
+  * %Array class provides easy access to coefficient-wise operations, while the
+  * API for the %Matrix class provides easy access to linear-algebra
+  * operations.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
+  *
+  * \sa \ref TutorialArrayClass, \ref TopicClassHierarchy
+  */
 namespace internal {
-template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
-struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>>
-    : traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
+struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
+{
   typedef ArrayXpr XprKind;
-  typedef ArrayBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> XprBase;
+  typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
 };
-}  // namespace internal
+}
 
-/** \class Array
- * \ingroup Core_Module
- *
- * \brief General-purpose arrays with easy API for coefficient-wise operations
- *
- * The %Array class is very similar to the Matrix class. It provides
- * general-purpose one- and two-dimensional arrays. The difference between the
- * %Array and the %Matrix class is primarily in the API: the API for the
- * %Array class provides easy access to coefficient-wise operations, while the
- * API for the %Matrix class provides easy access to linear-algebra
- * operations.
- *
- * See documentation of class Matrix for detailed information on the template parameters
- * storage layout.
- *
- * This class can be extended with the help of the plugin mechanism described on the page
- * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
- *
- * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
- */
-template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
-class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
- public:
-  typedef PlainObjectBase<Array> Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(Array)
+template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
+class Array
+  : public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
+{
+  public:
 
-  enum { Options = Options_ };
-  typedef typename Base::PlainObject PlainObject;
+    typedef PlainObjectBase<Array> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Array)
 
- protected:
-  template <typename Derived, typename OtherDerived, bool IsVector>
-  friend struct internal::conservative_resize_like_impl;
+    enum { Options = _Options };
+    typedef typename Base::PlainObject PlainObject;
 
-  using Base::m_storage;
+  protected:
+    template <typename Derived, typename OtherDerived, bool IsVector>
+    friend struct internal::conservative_resize_like_impl;
 
- public:
-  using Base::base;
-  using Base::coeff;
-  using Base::coeffRef;
+    using Base::m_storage;
 
-  /**
-   * The usage of
-   *   using Base::operator=;
-   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
-   * the usage of 'using'. This should be done only for operator=.
-   */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived>& other) {
-    return Base::operator=(other);
-  }
+  public:
 
-  /** Set all the entries to \a value.
-   * \sa DenseBase::setConstant(), DenseBase::fill()
-   */
-  /* This overload is needed because the usage of
-   *   using Base::operator=;
-   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
-   * the usage of 'using'. This should be done only for operator=.
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Scalar& value) {
-    Base::setConstant(value);
-    return *this;
-  }
+    using Base::base;
+    using Base::coeff;
+    using Base::coeffRef;
 
-  /** Copies the value of the expression \a other into \c *this with automatic resizing.
-   *
-   * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-   * it will be initialized.
-   *
-   * Note that copying a row-vector into a vector (and conversely) is allowed.
-   * The resizing, if any, is then done in the appropriate way so that row-vectors
-   * remain row-vectors and vectors remain vectors.
-   */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other) {
-    return Base::_set(other);
-  }
+    /**
+      * The usage of
+      *   using Base::operator=;
+      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+      * the usage of 'using'. This should be done only for operator=.
+      */
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
+    {
+      return Base::operator=(other);
+    }
 
-  /** This is a special case of the templated operator=. Its purpose is to
-   * prevent a default operator= from hiding the templated operator=.
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Array& other) { return Base::_set(other); }
+    /** Copies the value of the expression \a other into \c *this with automatic resizing.
+      *
+      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+      * it will be initialized.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      */
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other)
+    {
+      return Base::_set(other);
+    }
 
-  /** Default constructor.
-   *
-   * For fixed-size matrices, does nothing.
-   *
-   * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
-   * is called a null matrix. This constructor is the unique way to create null matrices: resizing
-   * a matrix to 0 is not supported.
-   *
-   * \sa resize(Index,Index)
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array() : Base() { EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    EIGEN_STRONG_INLINE Array& operator=(const Array& other)
+    {
+      return Base::_set(other);
+    }
+
+    /** Default constructor.
+      *
+      * For fixed-size matrices, does nothing.
+      *
+      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
+      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
+      * a matrix to 0 is not supported.
+      *
+      * \sa resize(Index,Index)
+      */
+    EIGEN_STRONG_INLINE explicit Array() : Base()
+    {
+      Base::_check_template_params();
+      EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
+    }
 
 #ifndef EIGEN_PARSED_BY_DOXYGEN
-  // FIXME is it still needed ??
-  /** \internal */
-  EIGEN_DEVICE_FUNC Array(internal::constructor_without_unaligned_array_assert)
-      : Base(internal::constructor_without_unaligned_array_assert()){EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED}
+    // FIXME is it still needed ??
+    /** \internal */
+    Array(internal::constructor_without_unaligned_array_assert)
+      : Base(internal::constructor_without_unaligned_array_assert())
+    {
+      Base::_check_template_params();
+      EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
+    }
 #endif
 
-        EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Array(Array && other) = default;
-  EIGEN_DEVICE_FUNC Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value) {
-    Base::operator=(std::move(other));
-    return *this;
-  }
+    /** Constructs a vector or row-vector with given dimension. \only_for_vectors
+      *
+      * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
+      * it is redundant to pass the dimension here, so it makes more sense to use the default
+      * constructor Matrix() instead.
+      */
+    EIGEN_STRONG_INLINE explicit Array(Index dim)
+      : Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
+    {
+      Base::_check_template_params();
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
+      eigen_assert(dim >= 0);
+      eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
+      EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
+    }
 
-  /** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const
-   * ArgTypes&... args)
-   *
-   * Example: \include Array_variadic_ctor_cxx11.cpp
-   * Output: \verbinclude Array_variadic_ctor_cxx11.out
-   *
-   * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
-   * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
-   */
-  template <typename... ArgTypes>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3,
-                                              const ArgTypes&... args)
-      : Base(a0, a1, a2, a3, args...) {}
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename T0, typename T1>
+    EIGEN_STRONG_INLINE Array(const T0& x, const T1& y)
+    {
+      Base::_check_template_params();
+      this->template _init2<T0,T1>(x, y);
+    }
+    #else
+    /** constructs an uninitialized matrix with \a rows rows and \a cols columns.
+      *
+      * This is useful for dynamic-size matrices. For fixed-size matrices,
+      * it is redundant to pass these parameters, so one should use the default constructor
+      * Matrix() instead. */
+    Array(Index rows, Index cols);
+    /** constructs an initialized 2D vector with given coefficients */
+    Array(const Scalar& x, const Scalar& y);
+    #endif
 
-  /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row.
-   * \cpp11
-   *
-   * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
-   *
-   * Example: \include Array_initializer_list_23_cxx11.cpp
-   * Output: \verbinclude Array_initializer_list_23_cxx11.out
-   *
-   * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is
-   * triggered.
-   *
-   * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
-   * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
-   * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
-   *
-   * Example: \include Array_initializer_list_vector_cxx11.cpp
-   * Output: \verbinclude Array_initializer_list_vector_cxx11.out
-   *
-   * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
-   * and implicit transposition is allowed for compile-time 1D arrays only.
-   *
-   * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Array(
-      const std::initializer_list<std::initializer_list<Scalar>>& list)
-      : Base(list) {}
+    /** constructs an initialized 3D vector with given coefficients */
+    EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z)
+    {
+      Base::_check_template_params();
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
+      m_storage.data()[0] = x;
+      m_storage.data()[1] = y;
+      m_storage.data()[2] = z;
+    }
+    /** constructs an initialized 4D vector with given coefficients */
+    EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
+    {
+      Base::_check_template_params();
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
+      m_storage.data()[0] = x;
+      m_storage.data()[1] = y;
+      m_storage.data()[2] = z;
+      m_storage.data()[3] = w;
+    }
 
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  template <typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(const T& x) {
-    Base::template _init1<T>(x);
-  }
+    explicit Array(const Scalar *data);
 
-  template <typename T0, typename T1>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1) {
-    this->template _init2<T0, T1>(val0, val1);
-  }
+    /** Constructor copying the value of the expression \a other */
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other)
+             : Base(other.rows() * other.cols(), other.rows(), other.cols())
+    {
+      Base::_check_template_params();
+      Base::_set_noalias(other);
+    }
+    /** Copy constructor */
+    EIGEN_STRONG_INLINE Array(const Array& other)
+            : Base(other.rows() * other.cols(), other.rows(), other.cols())
+    {
+      Base::_check_template_params();
+      Base::_set_noalias(other);
+    }
+    /** Copy constructor with in-place evaluation */
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
+    {
+      Base::_check_template_params();
+      Base::resize(other.rows(), other.cols());
+      other.evalTo(*this);
+    }
 
-#else
-  /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
-  EIGEN_DEVICE_FUNC explicit Array(const Scalar* data);
-  /** Constructs a vector or row-vector with given dimension. \only_for_vectors
-   *
-   * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
-   * it is redundant to pass the dimension here, so it makes more sense to use the default
-   * constructor Array() instead.
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(Index dim);
-  /** constructs an initialized 1x1 Array with the given coefficient
-   * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
-  Array(const Scalar& value);
-  /** constructs an uninitialized array with \a rows rows and \a cols columns.
-   *
-   * This is useful for dynamic-size arrays. For fixed-size arrays,
-   * it is redundant to pass these parameters, so one should use the default constructor
-   * Array() instead. */
-  Array(Index rows, Index cols);
-  /** constructs an initialized 2D vector with given coefficients
-   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
-  Array(const Scalar& val0, const Scalar& val1);
-#endif  // end EIGEN_PARSED_BY_DOXYGEN
+    /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
+      : Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
+    {
+      Base::_check_template_params();
+      Base::resize(other.rows(), other.cols());
+      *this = other;
+    }
 
-  /** constructs an initialized 3D vector with given coefficients
-   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2) {
-    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
-    m_storage.data()[0] = val0;
-    m_storage.data()[1] = val1;
-    m_storage.data()[2] = val2;
-  }
-  /** constructs an initialized 4D vector with given coefficients
-   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2,
-                                              const Scalar& val3) {
-    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
-    m_storage.data()[0] = val0;
-    m_storage.data()[1] = val1;
-    m_storage.data()[2] = val2;
-    m_storage.data()[3] = val3;
-  }
+    /** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
+      * data pointers.
+      */
+    template<typename OtherDerived>
+    void swap(ArrayBase<OtherDerived> const & other)
+    { this->_swap(other.derived()); }
 
-  /** Copy constructor */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Array(const Array& other) = default;
+    inline Index innerStride() const { return 1; }
+    inline Index outerStride() const { return this->innerSize(); }
 
- private:
-  struct PrivateType {};
+    #ifdef EIGEN_ARRAY_PLUGIN
+    #include EIGEN_ARRAY_PLUGIN
+    #endif
 
- public:
-  /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(
-      const EigenBase<OtherDerived>& other,
-      std::enable_if_t<internal::is_convertible<typename OtherDerived::Scalar, Scalar>::value, PrivateType> =
-          PrivateType())
-      : Base(other.derived()) {}
+  private:
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const EIGEN_NOEXCEPT { return 1; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
-
-#ifdef EIGEN_ARRAY_PLUGIN
-#include EIGEN_ARRAY_PLUGIN
-#endif
-
- private:
-  template <typename MatrixType, typename OtherDerived, bool SwapPointers>
-  friend struct internal::matrix_swap_impl;
+    template<typename MatrixType, typename OtherDerived, bool SwapPointers>
+    friend struct internal::matrix_swap_impl;
 };
 
 /** \defgroup arraytypedefs Global array typedefs
- * \ingroup Core_Module
- *
- * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
- *
- * The general patterns are the following:
- *
- * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for
- * dynamic size, and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c
- * cd for complex double.
- *
- * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of
- * floats.
- *
- * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
- * a fixed-size 1D array of 4 complex floats.
- *
- * With \cpp11, template alias are also defined for common sizes.
- * They follow the same pattern as above except that the scalar type suffix is replaced by a
- * template parameter, i.e.:
- *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
- *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
- *
- * \sa class Array
- */
+  * \ingroup Core_Module
+  *
+  * Eigen defines several typedef shortcuts for most common 1D and 2D array types.
+  *
+  * The general patterns are the following:
+  *
+  * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
+  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
+  * for complex double.
+  *
+  * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
+  *
+  * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
+  * a fixed-size 1D array of 4 complex floats.
+  *
+  * \sa class Array
+  */
 
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)        \
-  /** \ingroup arraytypedefs */                                              \
-  typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix; \
-  /** \ingroup arraytypedefs */                                              \
-  typedef Array<Type, Size, 1> Array##SizeSuffix##TypeSuffix;
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix;  \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Size, 1>    Array##SizeSuffix##TypeSuffix;
 
-#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)  \
-  /** \ingroup arraytypedefs */                                  \
-  typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix; \
-  /** \ingroup arraytypedefs */                                  \
-  typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix;  \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
 
 #define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2)           \
-  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3)           \
-  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4)           \
-  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X)     \
-  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2)        \
-  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3)        \
-  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
 
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>, cf)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int,                  i)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float,                f)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double,               d)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
 EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
 
 #undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
 #undef EIGEN_MAKE_ARRAY_TYPEDEFS
-#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
 
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)              \
-  /** \ingroup arraytypedefs */                                  \
-  /** \brief \cpp11 */                                           \
-  template <typename Type>                                       \
-  using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>; \
-  /** \ingroup arraytypedefs */                                  \
-  /** \brief \cpp11 */                                           \
-  template <typename Type>                                       \
-  using Array##SizeSuffix = Array<Type, Size, 1>;
-
-#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)        \
-  /** \ingroup arraytypedefs */                      \
-  /** \brief \cpp11 */                               \
-  template <typename Type>                           \
-  using Array##Size##X = Array<Type, Size, Dynamic>; \
-  /** \ingroup arraytypedefs */                      \
-  /** \brief \cpp11 */                               \
-  template <typename Type>                           \
-  using Array##X##Size = Array<Type, Dynamic, Size>;
-
-EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
-EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
-EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
-EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
-
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS
-#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS_LARGE
 
 #define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
-  using Eigen::Matrix##SizeSuffix##TypeSuffix;                               \
-  using Eigen::Vector##SizeSuffix##TypeSuffix;                               \
-  using Eigen::RowVector##SizeSuffix##TypeSuffix;
+using Eigen::Matrix##SizeSuffix##TypeSuffix; \
+using Eigen::Vector##SizeSuffix##TypeSuffix; \
+using Eigen::RowVector##SizeSuffix##TypeSuffix;
 
-#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix)       \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X)
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
 
-#define EIGEN_USING_ARRAY_TYPEDEFS        \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i)  \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f)  \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d)  \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
-  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
+#define EIGEN_USING_ARRAY_TYPEDEFS \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
 
-}  // end namespace Eigen
 
-#endif  // EIGEN_ARRAY_H
+#endif // EIGEN_ARRAY_H

Eigen/src/Core/ArrayBase.h

@@ -3,220 +3,237 @@
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_ARRAYBASE_H
 #define EIGEN_ARRAYBASE_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-template <typename ExpressionType>
-class MatrixWrapper;
+template<typename ExpressionType> class MatrixWrapper;
 
 /** \class ArrayBase
- * \ingroup Core_Module
- *
- * \brief Base class for all 1D and 2D array, and related expressions
- *
- * An array is similar to a dense vector or matrix. While matrices are mathematical
- * objects with well defined linear algebra operators, an array is just a collection
- * of scalar values arranged in a one or two dimensional fashion. As the main consequence,
- * all operations applied to an array are performed coefficient wise. Furthermore,
- * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
- * constructors allowing to easily write generic code working for both scalar values
- * and arrays.
- *
- * This class is the base that is inherited by all array expression types.
- *
- * \tparam Derived is the derived type, e.g., an array or an expression type.
- *
- * This class can be extended with the help of the plugin mechanism described on the page
- * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
- *
- * \sa class MatrixBase, \ref TopicClassHierarchy
- */
-template <typename Derived>
-class ArrayBase : public DenseBase<Derived> {
- public:
+  * \ingroup Core_Module
+  *
+  * \brief Base class for all 1D and 2D array, and related expressions
+  *
+  * An array is similar to a dense vector or matrix. While matrices are mathematical
+  * objects with well defined linear algebra operators, an array is just a collection
+  * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
+  * all operations applied to an array are performed coefficient wise. Furthermore,
+  * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
+  * constructors allowing to easily write generic code working for both scalar values
+  * and arrays.
+  *
+  * This class is the base that is inherited by all array expression types.
+  *
+  * \tparam Derived is the derived type, e.g., an array or an expression type.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
+  *
+  * \sa class MatrixBase, \ref TopicClassHierarchy
+  */
+template<typename Derived> class ArrayBase
+  : public DenseBase<Derived>
+{
+  public:
 #ifndef EIGEN_PARSED_BY_DOXYGEN
-  /** The base class for a given storage type. */
-  typedef ArrayBase StorageBaseType;
+    /** The base class for a given storage type. */
+    typedef ArrayBase StorageBaseType;
 
-  typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
+    typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
 
-  typedef typename internal::traits<Derived>::StorageKind StorageKind;
-  typedef typename internal::traits<Derived>::Scalar Scalar;
-  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
+    using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
+                typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
 
-  typedef DenseBase<Derived> Base;
-  using Base::ColsAtCompileTime;
-  using Base::Flags;
-  using Base::IsVectorAtCompileTime;
-  using Base::MaxColsAtCompileTime;
-  using Base::MaxRowsAtCompileTime;
-  using Base::MaxSizeAtCompileTime;
-  using Base::RowsAtCompileTime;
-  using Base::SizeAtCompileTime;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Index Index;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
 
-  using Base::coeff;
-  using Base::coeffRef;
-  using Base::cols;
-  using Base::const_cast_derived;
-  using Base::derived;
-  using Base::lazyAssign;
-  using Base::rows;
-  using Base::size;
-  using Base::operator-;
-  using Base::operator=;
-  using Base::operator+=;
-  using Base::operator-=;
-  using Base::operator*=;
-  using Base::operator/=;
+    typedef DenseBase<Derived> Base;
+    using Base::RowsAtCompileTime;
+    using Base::ColsAtCompileTime;
+    using Base::SizeAtCompileTime;
+    using Base::MaxRowsAtCompileTime;
+    using Base::MaxColsAtCompileTime;
+    using Base::MaxSizeAtCompileTime;
+    using Base::IsVectorAtCompileTime;
+    using Base::Flags;
+    using Base::CoeffReadCost;
 
-  typedef typename Base::CoeffReturnType CoeffReturnType;
+    using Base::derived;
+    using Base::const_cast_derived;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::coeff;
+    using Base::coeffRef;
+    using Base::lazyAssign;
+    using Base::operator=;
+    using Base::operator+=;
+    using Base::operator-=;
+    using Base::operator*=;
+    using Base::operator/=;
 
-#endif  // not EIGEN_PARSED_BY_DOXYGEN
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
 #ifndef EIGEN_PARSED_BY_DOXYGEN
-  typedef typename Base::PlainObject PlainObject;
+    /** \internal the plain matrix type corresponding to this expression. Note that is not necessarily
+      * exactly the return type of eval(): in the case of plain matrices, the return type of eval() is a const
+      * reference to a matrix, not a matrix! It is however guaranteed that the return type of eval() is either
+      * PlainObject or const PlainObject&.
+      */
+    typedef Array<typename internal::traits<Derived>::Scalar,
+                internal::traits<Derived>::RowsAtCompileTime,
+                internal::traits<Derived>::ColsAtCompileTime,
+                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
+                internal::traits<Derived>::MaxRowsAtCompileTime,
+                internal::traits<Derived>::MaxColsAtCompileTime
+          > PlainObject;
 
-  /** \internal Represents a matrix with all coefficients equal to one another*/
-  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
-#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
 
 #define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
-#define EIGEN_DOC_UNARY_ADDONS(X, Y)
-#include "../plugins/MatrixCwiseUnaryOps.inc"
-#include "../plugins/ArrayCwiseUnaryOps.inc"
-#include "../plugins/CommonCwiseBinaryOps.inc"
-#include "../plugins/MatrixCwiseBinaryOps.inc"
-#include "../plugins/ArrayCwiseBinaryOps.inc"
-#ifdef EIGEN_ARRAYBASE_PLUGIN
-#include EIGEN_ARRAYBASE_PLUGIN
-#endif
+#   include "../plugins/CommonCwiseUnaryOps.h"
+#   include "../plugins/MatrixCwiseUnaryOps.h"
+#   include "../plugins/ArrayCwiseUnaryOps.h"
+#   include "../plugins/CommonCwiseBinaryOps.h"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
+#   include "../plugins/ArrayCwiseBinaryOps.h"
+#   ifdef EIGEN_ARRAYBASE_PLUGIN
+#     include EIGEN_ARRAYBASE_PLUGIN
+#   endif
 #undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
 
-  /** Special case of the template operator=, in order to prevent the compiler
-   * from generating a default operator= (issue hit with g++ 4.1)
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const ArrayBase& other) {
-    internal::call_assignment(derived(), other.derived());
-    return derived();
-  }
+    /** Special case of the template operator=, in order to prevent the compiler
+      * from generating a default operator= (issue hit with g++ 4.1)
+      */
+    Derived& operator=(const ArrayBase& other)
+    {
+      return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
+    }
 
-  /** Set all the entries to \a value.
-   * \sa DenseBase::setConstant(), DenseBase::fill() */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Scalar& value) {
-    Base::setConstant(value);
-    return derived();
-  }
+    Derived& operator+=(const Scalar& scalar)
+    { return *this = derived() + scalar; }
+    Derived& operator-=(const Scalar& scalar)
+    { return *this = derived() - scalar; }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const Scalar& scalar);
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const Scalar& scalar);
+    template<typename OtherDerived>
+    Derived& operator+=(const ArrayBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator-=(const ArrayBase<OtherDerived>& other);
 
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const ArrayBase<OtherDerived>& other);
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const ArrayBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator*=(const ArrayBase<OtherDerived>& other);
 
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*=(const ArrayBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator/=(const ArrayBase<OtherDerived>& other);
 
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator/=(const ArrayBase<OtherDerived>& other);
+  public:
+    ArrayBase<Derived>& array() { return *this; }
+    const ArrayBase<Derived>& array() const { return *this; }
 
- public:
-  EIGEN_DEVICE_FUNC ArrayBase<Derived>& array() { return *this; }
-  EIGEN_DEVICE_FUNC const ArrayBase<Derived>& array() const { return *this; }
+    /** \returns an \link MatrixBase Matrix \endlink expression of this array
+      * \sa MatrixBase::array() */
+    MatrixWrapper<Derived> matrix() { return derived(); }
+    const MatrixWrapper<Derived> matrix() const { return derived(); }
 
-  /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
-   * \sa MatrixBase::array() */
-  EIGEN_DEVICE_FUNC MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
-  EIGEN_DEVICE_FUNC const MatrixWrapper<const Derived> matrix() const {
-    return MatrixWrapper<const Derived>(derived());
-  }
+//     template<typename Dest>
+//     inline void evalTo(Dest& dst) const { dst = matrix(); }
 
-  //     template<typename Dest>
-  //     inline void evalTo(Dest& dst) const { dst = matrix(); }
+  protected:
+    ArrayBase() : Base() {}
 
- protected:
-  EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
-  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
-
- private:
-  explicit ArrayBase(Index);
-  ArrayBase(Index, Index);
-  template <typename OtherDerived>
-  explicit ArrayBase(const ArrayBase<OtherDerived>&);
-
- protected:
-  // mixing arrays and matrices is not legal
-  template <typename OtherDerived>
-  Derived& operator+=(const MatrixBase<OtherDerived>&) {
-    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
-                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
-    return *this;
-  }
-  // mixing arrays and matrices is not legal
-  template <typename OtherDerived>
-  Derived& operator-=(const MatrixBase<OtherDerived>&) {
-    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
-                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
-    return *this;
-  }
+  private:
+    explicit ArrayBase(Index);
+    ArrayBase(Index,Index);
+    template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
+  protected:
+    // mixing arrays and matrices is not legal
+    template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
+    {EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
+    // mixing arrays and matrices is not legal
+    template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
+    {EIGEN_STATIC_ASSERT(sizeof(typename OtherDerived::Scalar)==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);}
 };
 
 /** replaces \c *this by \c *this - \a other.
- *
- * \returns a reference to \c *this
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
+{
+  SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
+  tmp = other.derived();
   return derived();
 }
 
 /** replaces \c *this by \c *this + \a other.
- *
- * \returns a reference to \c *this
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
+{
+  SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
+  tmp = other.derived();
   return derived();
 }
 
 /** replaces \c *this by \c *this * \a other coefficient wise.
- *
- * \returns a reference to \c *this
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar, typename OtherDerived::Scalar>());
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
+{
+  SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, OtherDerived> tmp(derived());
+  tmp = other.derived();
   return derived();
 }
 
 /** replaces \c *this by \c *this / \a other coefficient wise.
- *
- * \returns a reference to \c *this
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar, typename OtherDerived::Scalar>());
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
+{
+  SelfCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, Derived, OtherDerived> tmp(derived());
+  tmp = other.derived();
   return derived();
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_ARRAYBASE_H
+#endif // EIGEN_ARRAYBASE_H

Eigen/src/Core/ArrayWrapper.h

@@ -3,171 +3,237 @@
 //
 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_ARRAYWRAPPER_H
 #define EIGEN_ARRAYWRAPPER_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 /** \class ArrayWrapper
- * \ingroup Core_Module
- *
- * \brief Expression of a mathematical vector or matrix as an array object
- *
- * This class is the return type of MatrixBase::array(), and most of the time
- * this is the only way it is use.
- *
- * \sa MatrixBase::array(), class MatrixWrapper
- */
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a mathematical vector or matrix as an array object
+  *
+  * This class is the return type of MatrixBase::array(), and most of the time
+  * this is the only way it is use.
+  *
+  * \sa MatrixBase::array(), class MatrixWrapper
+  */
 
 namespace internal {
-template <typename ExpressionType>
-struct traits<ArrayWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+template<typename ExpressionType>
+struct traits<ArrayWrapper<ExpressionType> >
+  : public traits<typename remove_all<typename ExpressionType::Nested>::type >
+{
   typedef ArrayXpr XprKind;
-  // Let's remove NestByRefBit
-  enum {
-    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
-    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
-    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
-  };
 };
-}  // namespace internal
+}
 
-template <typename ExpressionType>
-class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > {
- public:
-  typedef ArrayBase<ArrayWrapper> Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
-  typedef internal::remove_all_t<ExpressionType> NestedExpression;
+template<typename ExpressionType>
+class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
+{
+  public:
+    typedef ArrayBase<ArrayWrapper> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
 
-  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
-      ScalarWithConstIfNotLvalue;
+    typedef typename internal::conditional<
+                       internal::is_lvalue<ExpressionType>::value,
+                       Scalar,
+                       const Scalar
+                     >::type ScalarWithConstIfNotLvalue;
 
-  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+    typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
 
-  using Base::coeffRef;
+    inline ArrayWrapper(const ExpressionType& matrix) : m_expression(matrix) {}
 
-  EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+    inline Index rows() const { return m_expression.rows(); }
+    inline Index cols() const { return m_expression.cols(); }
+    inline Index outerStride() const { return m_expression.outerStride(); }
+    inline Index innerStride() const { return m_expression.innerStride(); }
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const EIGEN_NOEXCEPT {
-    return m_expression.outerStride();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const EIGEN_NOEXCEPT {
-    return m_expression.innerStride();
-  }
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    inline const Scalar* data() const { return m_expression.data(); }
 
-  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+    inline const CoeffReturnType coeff(Index row, Index col) const
+    {
+      return m_expression.coeff(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
-    return m_expression.coeffRef(rowId, colId);
-  }
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_expression.const_cast_derived().coeffRef(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+    inline const Scalar& coeffRef(Index row, Index col) const
+    {
+      return m_expression.const_cast_derived().coeffRef(row, col);
+    }
 
-  template <typename Dest>
-  EIGEN_DEVICE_FUNC inline void evalTo(Dest& dst) const {
-    dst = m_expression;
-  }
+    inline const CoeffReturnType coeff(Index index) const
+    {
+      return m_expression.coeff(index);
+    }
 
-  EIGEN_DEVICE_FUNC const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
-    return m_expression;
-  }
+    inline Scalar& coeffRef(Index index)
+    {
+      return m_expression.const_cast_derived().coeffRef(index);
+    }
 
-  /** Forwards the resizing request to the nested expression
-   * \sa DenseBase::resize(Index)  */
-  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
-  /** Forwards the resizing request to the nested expression
-   * \sa DenseBase::resize(Index,Index)*/
-  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_expression.const_cast_derived().coeffRef(index);
+    }
 
- protected:
-  NestedExpressionType m_expression;
+    template<int LoadMode>
+    inline const PacketScalar packet(Index row, Index col) const
+    {
+      return m_expression.template packet<LoadMode>(row, col);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index index) const
+    {
+      return m_expression.template packet<LoadMode>(index);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index index, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
+    }
+
+    template<typename Dest>
+    inline void evalTo(Dest& dst) const { dst = m_expression; }
+
+  protected:
+    const NestedExpressionType m_expression;
 };
 
 /** \class MatrixWrapper
- * \ingroup Core_Module
- *
- * \brief Expression of an array as a mathematical vector or matrix
- *
- * This class is the return type of ArrayBase::matrix(), and most of the time
- * this is the only way it is use.
- *
- * \sa MatrixBase::matrix(), class ArrayWrapper
- */
+  * \ingroup Core_Module
+  *
+  * \brief Expression of an array as a mathematical vector or matrix
+  *
+  * This class is the return type of ArrayBase::matrix(), and most of the time
+  * this is the only way it is use.
+  *
+  * \sa MatrixBase::matrix(), class ArrayWrapper
+  */
 
 namespace internal {
-template <typename ExpressionType>
-struct traits<MatrixWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+template<typename ExpressionType>
+struct traits<MatrixWrapper<ExpressionType> >
+ : public traits<typename remove_all<typename ExpressionType::Nested>::type >
+{
   typedef MatrixXpr XprKind;
-  // Let's remove NestByRefBit
-  enum {
-    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
-    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
-    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
-  };
 };
-}  // namespace internal
+}
 
-template <typename ExpressionType>
-class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> > {
- public:
-  typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
-  typedef internal::remove_all_t<ExpressionType> NestedExpression;
+template<typename ExpressionType>
+class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
+{
+  public:
+    typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
 
-  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
-      ScalarWithConstIfNotLvalue;
+    typedef typename internal::conditional<
+                       internal::is_lvalue<ExpressionType>::value,
+                       Scalar,
+                       const Scalar
+                     >::type ScalarWithConstIfNotLvalue;
 
-  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+    typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
 
-  using Base::coeffRef;
+    inline MatrixWrapper(const ExpressionType& matrix) : m_expression(matrix) {}
 
-  EIGEN_DEVICE_FUNC explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+    inline Index rows() const { return m_expression.rows(); }
+    inline Index cols() const { return m_expression.cols(); }
+    inline Index outerStride() const { return m_expression.outerStride(); }
+    inline Index innerStride() const { return m_expression.innerStride(); }
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const EIGEN_NOEXCEPT {
-    return m_expression.outerStride();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const EIGEN_NOEXCEPT {
-    return m_expression.innerStride();
-  }
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    inline const Scalar* data() const { return m_expression.data(); }
 
-  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+    inline const CoeffReturnType coeff(Index row, Index col) const
+    {
+      return m_expression.coeff(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
-    return m_expression.derived().coeffRef(rowId, colId);
-  }
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_expression.const_cast_derived().coeffRef(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+    inline const Scalar& coeffRef(Index row, Index col) const
+    {
+      return m_expression.derived().coeffRef(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
-    return m_expression;
-  }
+    inline const CoeffReturnType coeff(Index index) const
+    {
+      return m_expression.coeff(index);
+    }
 
-  /** Forwards the resizing request to the nested expression
-   * \sa DenseBase::resize(Index)  */
-  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
-  /** Forwards the resizing request to the nested expression
-   * \sa DenseBase::resize(Index,Index)*/
-  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+    inline Scalar& coeffRef(Index index)
+    {
+      return m_expression.const_cast_derived().coeffRef(index);
+    }
 
- protected:
-  NestedExpressionType m_expression;
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_expression.const_cast_derived().coeffRef(index);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index row, Index col) const
+    {
+      return m_expression.template packet<LoadMode>(row, col);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index index) const
+    {
+      return m_expression.template packet<LoadMode>(index);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index index, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
+    }
+
+  protected:
+    const NestedExpressionType m_expression;
 };
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_ARRAYWRAPPER_H
+#endif // EIGEN_ARRAYWRAPPER_H

Eigen/src/Core/Assign.h

@@ -5,76 +5,589 @@
 // Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_ASSIGN_H
 #define EIGEN_ASSIGN_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
+namespace internal {
 
-namespace Eigen {
+/***************************************************************************
+* Part 1 : the logic deciding a strategy for traversal and unrolling       *
+***************************************************************************/
 
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::lazyAssign(const DenseBase<OtherDerived>& other) {
-  enum { SameType = internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value };
+template <typename Derived, typename OtherDerived>
+struct assign_traits
+{
+public:
+  enum {
+    DstIsAligned = Derived::Flags & AlignedBit,
+    DstHasDirectAccess = Derived::Flags & DirectAccessBit,
+    SrcIsAligned = OtherDerived::Flags & AlignedBit,
+    JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned
+  };
+
+private:
+  enum {
+    InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime)
+              : int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime)
+              : int(Derived::RowsAtCompileTime),
+    InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime)
+              : int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime)
+              : int(Derived::MaxRowsAtCompileTime),
+    MaxSizeAtCompileTime = Derived::SizeAtCompileTime,
+    PacketSize = packet_traits<typename Derived::Scalar>::size
+  };
+
+  enum {
+    StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
+    MightVectorize = StorageOrdersAgree
+                  && (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
+    MayInnerVectorize  = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
+                       && int(DstIsAligned) && int(SrcIsAligned),
+    MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
+    MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
+                       && (DstIsAligned || MaxSizeAtCompileTime == Dynamic),
+      /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
+         so it's only good for large enough sizes. */
+    MaySliceVectorize  = MightVectorize && DstHasDirectAccess
+                       && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize)
+      /* slice vectorization can be slow, so we only want it if the slices are big, which is
+         indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
+         in a fixed-size matrix */
+  };
+
+public:
+  enum {
+    Traversal = int(MayInnerVectorize)  ? int(InnerVectorizedTraversal)
+              : int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
+              : int(MaySliceVectorize)  ? int(SliceVectorizedTraversal)
+              : int(MayLinearize)       ? int(LinearTraversal)
+                                        : int(DefaultTraversal),
+    Vectorized = int(Traversal) == InnerVectorizedTraversal
+              || int(Traversal) == LinearVectorizedTraversal
+              || int(Traversal) == SliceVectorizedTraversal
+  };
+
+private:
+  enum {
+    UnrollingLimit      = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1),
+    MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic
+                       && int(OtherDerived::CoeffReadCost) != Dynamic
+                       && int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit),
+    MayUnrollInner      = int(InnerSize) != Dynamic
+                       && int(OtherDerived::CoeffReadCost) != Dynamic
+                       && int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit)
+  };
+
+public:
+  enum {
+    Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
+                ? (
+                    int(MayUnrollCompletely) ? int(CompleteUnrolling)
+                  : int(MayUnrollInner)      ? int(InnerUnrolling)
+                                             : int(NoUnrolling)
+                  )
+              : int(Traversal) == int(LinearVectorizedTraversal)
+                ? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) )
+              : int(Traversal) == int(LinearTraversal)
+                ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling) )
+              : int(NoUnrolling)
+  };
+
+#ifdef EIGEN_DEBUG_ASSIGN
+  static void debug()
+  {
+    EIGEN_DEBUG_VAR(DstIsAligned)
+    EIGEN_DEBUG_VAR(SrcIsAligned)
+    EIGEN_DEBUG_VAR(JointAlignment)
+    EIGEN_DEBUG_VAR(InnerSize)
+    EIGEN_DEBUG_VAR(InnerMaxSize)
+    EIGEN_DEBUG_VAR(PacketSize)
+    EIGEN_DEBUG_VAR(StorageOrdersAgree)
+    EIGEN_DEBUG_VAR(MightVectorize)
+    EIGEN_DEBUG_VAR(MayLinearize)
+    EIGEN_DEBUG_VAR(MayInnerVectorize)
+    EIGEN_DEBUG_VAR(MayLinearVectorize)
+    EIGEN_DEBUG_VAR(MaySliceVectorize)
+    EIGEN_DEBUG_VAR(Traversal)
+    EIGEN_DEBUG_VAR(UnrollingLimit)
+    EIGEN_DEBUG_VAR(MayUnrollCompletely)
+    EIGEN_DEBUG_VAR(MayUnrollInner)
+    EIGEN_DEBUG_VAR(Unrolling)
+  }
+#endif
+};
+
+/***************************************************************************
+* Part 2 : meta-unrollers
+***************************************************************************/
+
+/************************
+*** Default traversal ***
+************************/
+
+template<typename Derived1, typename Derived2, int Index, int Stop>
+struct assign_DefaultTraversal_CompleteUnrolling
+{
+  enum {
+    outer = Index / Derived1::InnerSizeAtCompileTime,
+    inner = Index % Derived1::InnerSizeAtCompileTime
+  };
+
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    dst.copyCoeffByOuterInner(outer, inner, src);
+    assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
+  }
+};
+
+template<typename Derived1, typename Derived2, int Stop>
+struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
+};
+
+template<typename Derived1, typename Derived2, int Index, int Stop>
+struct assign_DefaultTraversal_InnerUnrolling
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int outer)
+  {
+    dst.copyCoeffByOuterInner(outer, Index, src);
+    assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
+  }
+};
+
+template<typename Derived1, typename Derived2, int Stop>
+struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
+};
+
+/***********************
+*** Linear traversal ***
+***********************/
+
+template<typename Derived1, typename Derived2, int Index, int Stop>
+struct assign_LinearTraversal_CompleteUnrolling
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    dst.copyCoeff(Index, src);
+    assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
+  }
+};
+
+template<typename Derived1, typename Derived2, int Stop>
+struct assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
+};
+
+/**************************
+*** Inner vectorization ***
+**************************/
+
+template<typename Derived1, typename Derived2, int Index, int Stop>
+struct assign_innervec_CompleteUnrolling
+{
+  enum {
+    outer = Index / Derived1::InnerSizeAtCompileTime,
+    inner = Index % Derived1::InnerSizeAtCompileTime,
+    JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
+  };
+
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src);
+    assign_innervec_CompleteUnrolling<Derived1, Derived2,
+      Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src);
+  }
+};
+
+template<typename Derived1, typename Derived2, int Stop>
+struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &) {}
+};
+
+template<typename Derived1, typename Derived2, int Index, int Stop>
+struct assign_innervec_InnerUnrolling
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src, int outer)
+  {
+    dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src);
+    assign_innervec_InnerUnrolling<Derived1, Derived2,
+      Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer);
+  }
+};
+
+template<typename Derived1, typename Derived2, int Stop>
+struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &, const Derived2 &, int) {}
+};
+
+/***************************************************************************
+* Part 3 : implementation of all cases
+***************************************************************************/
+
+template<typename Derived1, typename Derived2,
+         int Traversal = assign_traits<Derived1, Derived2>::Traversal,
+         int Unrolling = assign_traits<Derived1, Derived2>::Unrolling>
+struct assign_impl;
+
+/************************
+*** Default traversal ***
+************************/
+
+template<typename Derived1, typename Derived2, int Unrolling>
+struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling>
+{
+  inline static void run(Derived1 &, const Derived2 &) { }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  inline static void run(Derived1 &dst, const Derived2 &src)
+  {
+    const Index innerSize = dst.innerSize();
+    const Index outerSize = dst.outerSize();
+    for(Index outer = 0; outer < outerSize; ++outer)
+      for(Index inner = 0; inner < innerSize; ++inner)
+        dst.copyCoeffByOuterInner(outer, inner, src);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
+      ::run(dst, src);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    const Index outerSize = dst.outerSize();
+    for(Index outer = 0; outer < outerSize; ++outer)
+      assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
+        ::run(dst, src, outer);
+  }
+};
+
+/***********************
+*** Linear traversal ***
+***********************/
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  inline static void run(Derived1 &dst, const Derived2 &src)
+  {
+    const Index size = dst.size();
+    for(Index i = 0; i < size; ++i)
+      dst.copyCoeff(i, src);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
+      ::run(dst, src);
+  }
+};
+
+/**************************
+*** Inner vectorization ***
+**************************/
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  inline static void run(Derived1 &dst, const Derived2 &src)
+  {
+    const Index innerSize = dst.innerSize();
+    const Index outerSize = dst.outerSize();
+    const Index packetSize = packet_traits<typename Derived1::Scalar>::size;
+    for(Index outer = 0; outer < outerSize; ++outer)
+      for(Index inner = 0; inner < innerSize; inner+=packetSize)
+        dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, inner, src);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling>
+{
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
+      ::run(dst, src);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    const Index outerSize = dst.outerSize();
+    for(Index outer = 0; outer < outerSize; ++outer)
+      assign_innervec_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
+        ::run(dst, src, outer);
+  }
+};
+
+/***************************
+*** Linear vectorization ***
+***************************/
+
+template <bool IsAligned = false>
+struct unaligned_assign_impl
+{
+  template <typename Derived, typename OtherDerived>
+  static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {}
+};
+
+template <>
+struct unaligned_assign_impl<false>
+{
+  // MSVC must not inline this functions. If it does, it fails to optimize the
+  // packet access path.
+#ifdef _MSC_VER
+  template <typename Derived, typename OtherDerived>
+  static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
+#else
+  template <typename Derived, typename OtherDerived>
+  static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
+#endif
+  {
+    for (typename Derived::Index index = start; index < end; ++index)
+      dst.copyCoeff(index, src);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    const Index size = dst.size();
+    typedef packet_traits<typename Derived1::Scalar> PacketTraits;
+    enum {
+      packetSize = PacketTraits::size,
+      dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
+      srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
+    };
+    const Index alignedStart = assign_traits<Derived1,Derived2>::DstIsAligned ? 0
+                             : first_aligned(&dst.coeffRef(0), size);
+    const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
+
+    unaligned_assign_impl<assign_traits<Derived1,Derived2>::DstIsAligned!=0>::run(src,dst,0,alignedStart);
+
+    for(Index index = alignedStart; index < alignedEnd; index += packetSize)
+    {
+      dst.template copyPacket<Derived2, dstAlignment, srcAlignment>(index, src);
+    }
+
+    unaligned_assign_impl<>::run(src,dst,alignedEnd,size);
+  }
+};
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  EIGEN_STRONG_INLINE static void run(Derived1 &dst, const Derived2 &src)
+  {
+    enum { size = Derived1::SizeAtCompileTime,
+           packetSize = packet_traits<typename Derived1::Scalar>::size,
+           alignedSize = (size/packetSize)*packetSize };
+
+    assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src);
+    assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src);
+  }
+};
+
+/**************************
+*** Slice vectorization ***
+***************************/
+
+template<typename Derived1, typename Derived2>
+struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling>
+{
+  typedef typename Derived1::Index Index;
+  inline static void run(Derived1 &dst, const Derived2 &src)
+  {
+    typedef packet_traits<typename Derived1::Scalar> PacketTraits;
+    enum {
+      packetSize = PacketTraits::size,
+      alignable = PacketTraits::AlignedOnScalar,
+      dstAlignment = alignable ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
+      srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
+    };
+    const Index packetAlignedMask = packetSize - 1;
+    const Index innerSize = dst.innerSize();
+    const Index outerSize = dst.outerSize();
+    const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0;
+    Index alignedStart = ((!alignable) || assign_traits<Derived1,Derived2>::DstIsAligned) ? 0
+                       : first_aligned(&dst.coeffRef(0,0), innerSize);
+
+    for(Index outer = 0; outer < outerSize; ++outer)
+    {
+      const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
+      // do the non-vectorizable part of the assignment
+      for(Index inner = 0; inner<alignedStart ; ++inner)
+        dst.copyCoeffByOuterInner(outer, inner, src);
+
+      // do the vectorizable part of the assignment
+      for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
+        dst.template copyPacketByOuterInner<Derived2, dstAlignment, Unaligned>(outer, inner, src);
+
+      // do the non-vectorizable part of the assignment
+      for(Index inner = alignedEnd; inner<innerSize ; ++inner)
+        dst.copyCoeffByOuterInner(outer, inner, src);
+
+      alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
+    }
+  }
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Part 4 : implementation of DenseBase methods
+***************************************************************************/
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
+  ::lazyAssign(const DenseBase<OtherDerived>& other)
+{
+  enum{
+    SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
+  };
 
   EIGEN_STATIC_ASSERT_LVALUE(Derived)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived, OtherDerived)
-  EIGEN_STATIC_ASSERT(
-      SameType,
-      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
 
+#ifdef EIGEN_DEBUG_ASSIGN
+  internal::assign_traits<Derived, OtherDerived>::debug();
+#endif
   eigen_assert(rows() == other.rows() && cols() == other.cols());
-  internal::call_assignment_no_alias(derived(), other.derived());
-
+  internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal)
+                                                       : int(InvalidTraversal)>::run(derived(),other.derived());
+#ifndef EIGEN_NO_DEBUG
+  checkTransposeAliasing(other.derived());
+#endif
   return derived();
 }
 
-template <typename Derived>
+namespace internal {
+
+template<typename Derived, typename OtherDerived,
+         bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
+         bool NeedToTranspose = Derived::IsVectorAtCompileTime
+                && OtherDerived::IsVectorAtCompileTime
+                && ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
+                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
+                         // revert to || as soon as not needed anymore.
+                    (int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
+                && int(Derived::SizeAtCompileTime) != 1>
+struct assign_selector;
+
+template<typename Derived, typename OtherDerived>
+struct assign_selector<Derived,OtherDerived,false,false> {
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
+};
+template<typename Derived, typename OtherDerived>
+struct assign_selector<Derived,OtherDerived,true,false> {
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
+};
+template<typename Derived, typename OtherDerived>
+struct assign_selector<Derived,OtherDerived,false,true> {
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
+};
+template<typename Derived, typename OtherDerived>
+struct assign_selector<Derived,OtherDerived,true,true> {
+  EIGEN_STRONG_INLINE static Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
+};
+
+} // end namespace internal
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
+{
+  return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
+}
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
+{
+  return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
+}
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
+{
+  return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
+}
+
+template<typename Derived>
 template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other) {
-  internal::call_assignment(derived(), other.derived());
-  return derived();
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
+{
+  return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
 }
 
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other) {
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other) {
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template <typename Derived>
+template<typename Derived>
 template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other) {
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other) {
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
-    const ReturnByValue<OtherDerived>& other) {
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
+{
   other.derived().evalTo(derived());
   return derived();
 }
 
-}  // end namespace Eigen
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
+{
+  other.evalTo(derived());
+  return derived();
+}
 
-#endif  // EIGEN_ASSIGN_H
+#endif // EIGEN_ASSIGN_H

Eigen/src/Core/AssignEvaluator.h

@@ -1,951 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGN_EVALUATOR_H
-#define EIGEN_ASSIGN_EVALUATOR_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-// This implementation is based on Assign.h
-
-namespace internal {
-
-/***************************************************************************
- * Part 1 : the logic deciding a strategy for traversal and unrolling       *
- ***************************************************************************/
-
-// copy_using_evaluator_traits is based on assign_traits
-
-template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc, int MaxPacketSize = -1>
-struct copy_using_evaluator_traits {
-  typedef typename DstEvaluator::XprType Dst;
-  typedef typename Dst::Scalar DstScalar;
-
-  enum { DstFlags = DstEvaluator::Flags, SrcFlags = SrcEvaluator::Flags };
-
- public:
-  enum {
-    DstAlignment = DstEvaluator::Alignment,
-    SrcAlignment = SrcEvaluator::Alignment,
-    DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
-    JointAlignment = plain_enum_min(DstAlignment, SrcAlignment)
-  };
-
- private:
-  enum {
-    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
-                : int(DstFlags) & RowMajorBit   ? int(Dst::ColsAtCompileTime)
-                                                : int(Dst::RowsAtCompileTime),
-    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
-                   : int(DstFlags) & RowMajorBit   ? int(Dst::MaxColsAtCompileTime)
-                                                   : int(Dst::MaxRowsAtCompileTime),
-    RestrictedInnerSize = min_size_prefer_fixed(InnerSize, MaxPacketSize),
-    RestrictedLinearSize = min_size_prefer_fixed(Dst::SizeAtCompileTime, MaxPacketSize),
-    OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
-    MaxSizeAtCompileTime = Dst::SizeAtCompileTime
-  };
-
-  // TODO distinguish between linear traversal and inner-traversals
-  typedef typename find_best_packet<DstScalar, RestrictedLinearSize>::type LinearPacketType;
-  typedef typename find_best_packet<DstScalar, RestrictedInnerSize>::type InnerPacketType;
-
-  enum {
-    LinearPacketSize = unpacket_traits<LinearPacketType>::size,
-    InnerPacketSize = unpacket_traits<InnerPacketType>::size
-  };
-
- public:
-  enum {
-    LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
-    InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
-  };
-
- private:
-  enum {
-    DstIsRowMajor = DstFlags & RowMajorBit,
-    SrcIsRowMajor = SrcFlags & RowMajorBit,
-    StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
-    MightVectorize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit) &&
-                     bool(functor_traits<AssignFunc>::PacketAccess),
-    MayInnerVectorize = MightVectorize && int(InnerSize) != Dynamic && int(InnerSize) % int(InnerPacketSize) == 0 &&
-                        int(OuterStride) != Dynamic && int(OuterStride) % int(InnerPacketSize) == 0 &&
-                        (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment) >= int(InnerRequiredAlignment)),
-    MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
-    MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess) &&
-                         (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment) >= int(LinearRequiredAlignment)) ||
-                          MaxSizeAtCompileTime == Dynamic),
-    /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
-       so it's only good for large enough sizes. */
-    MaySliceVectorize = bool(MightVectorize) && bool(DstHasDirectAccess) &&
-                        (int(InnerMaxSize) == Dynamic ||
-                         int(InnerMaxSize) >= (EIGEN_UNALIGNED_VECTORIZE ? InnerPacketSize : (3 * InnerPacketSize)))
-    /* slice vectorization can be slow, so we only want it if the slices are big, which is
-       indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
-       in a fixed-size matrix
-       However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
-  };
-
- public:
-  enum {
-    Traversal = int(Dst::SizeAtCompileTime) == 0
-                    ? int(AllAtOnceTraversal)  // If compile-size is zero, traversing will fail at compile-time.
-                : (int(MayLinearVectorize) && (LinearPacketSize > InnerPacketSize)) ? int(LinearVectorizedTraversal)
-                : int(MayInnerVectorize)                                            ? int(InnerVectorizedTraversal)
-                : int(MayLinearVectorize)                                           ? int(LinearVectorizedTraversal)
-                : int(MaySliceVectorize)                                            ? int(SliceVectorizedTraversal)
-                : int(MayLinearize)                                                 ? int(LinearTraversal)
-                                                                                    : int(DefaultTraversal),
-    Vectorized = int(Traversal) == InnerVectorizedTraversal || int(Traversal) == LinearVectorizedTraversal ||
-                 int(Traversal) == SliceVectorizedTraversal
-  };
-
-  typedef std::conditional_t<int(Traversal) == LinearVectorizedTraversal, LinearPacketType, InnerPacketType> PacketType;
-
- private:
-  enum {
-    ActualPacketSize = int(Traversal) == LinearVectorizedTraversal ? LinearPacketSize
-                       : Vectorized                                ? InnerPacketSize
-                                                                   : 1,
-    UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
-    MayUnrollCompletely =
-        int(Dst::SizeAtCompileTime) != Dynamic &&
-        int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost) + int(SrcEvaluator::CoeffReadCost)) <=
-            int(UnrollingLimit),
-    MayUnrollInner =
-        int(InnerSize) != Dynamic &&
-        int(InnerSize) * (int(DstEvaluator::CoeffReadCost) + int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
-  };
-
- public:
-  enum {
-    Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
-                    ? (int(MayUnrollCompletely) ? int(CompleteUnrolling)
-                       : int(MayUnrollInner)    ? int(InnerUnrolling)
-                                                : int(NoUnrolling))
-                : int(Traversal) == int(LinearVectorizedTraversal)
-                    ? (bool(MayUnrollCompletely) &&
-                               (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment) >= int(LinearRequiredAlignment)))
-                           ? int(CompleteUnrolling)
-                           : int(NoUnrolling))
-                : int(Traversal) == int(LinearTraversal)
-                    ? (bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling))
-#if EIGEN_UNALIGNED_VECTORIZE
-                : int(Traversal) == int(SliceVectorizedTraversal)
-                    ? (bool(MayUnrollInner) ? int(InnerUnrolling) : int(NoUnrolling))
-#endif
-                    : int(NoUnrolling)
-  };
-
-#ifdef EIGEN_DEBUG_ASSIGN
-  static void debug() {
-    std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
-    std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
-    std::cerr.setf(std::ios::hex, std::ios::basefield);
-    std::cerr << "DstFlags"
-              << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
-    std::cerr << "SrcFlags"
-              << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
-    std::cerr.unsetf(std::ios::hex);
-    EIGEN_DEBUG_VAR(DstAlignment)
-    EIGEN_DEBUG_VAR(SrcAlignment)
-    EIGEN_DEBUG_VAR(LinearRequiredAlignment)
-    EIGEN_DEBUG_VAR(InnerRequiredAlignment)
-    EIGEN_DEBUG_VAR(JointAlignment)
-    EIGEN_DEBUG_VAR(InnerSize)
-    EIGEN_DEBUG_VAR(InnerMaxSize)
-    EIGEN_DEBUG_VAR(LinearPacketSize)
-    EIGEN_DEBUG_VAR(InnerPacketSize)
-    EIGEN_DEBUG_VAR(ActualPacketSize)
-    EIGEN_DEBUG_VAR(StorageOrdersAgree)
-    EIGEN_DEBUG_VAR(MightVectorize)
-    EIGEN_DEBUG_VAR(MayLinearize)
-    EIGEN_DEBUG_VAR(MayInnerVectorize)
-    EIGEN_DEBUG_VAR(MayLinearVectorize)
-    EIGEN_DEBUG_VAR(MaySliceVectorize)
-    std::cerr << "Traversal"
-              << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
-    EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
-    EIGEN_DEBUG_VAR(DstEvaluator::CoeffReadCost)
-    EIGEN_DEBUG_VAR(Dst::SizeAtCompileTime)
-    EIGEN_DEBUG_VAR(UnrollingLimit)
-    EIGEN_DEBUG_VAR(MayUnrollCompletely)
-    EIGEN_DEBUG_VAR(MayUnrollInner)
-    std::cerr << "Unrolling"
-              << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
-    std::cerr << std::endl;
-  }
-#endif
-};
-
-/***************************************************************************
- * Part 2 : meta-unrollers
- ***************************************************************************/
-
-/************************
-*** Default traversal ***
-************************/
-
-template <typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling {
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-
-  enum { outer = Index / DstXprType::InnerSizeAtCompileTime, inner = Index % DstXprType::InnerSizeAtCompileTime };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    kernel.assignCoeffByOuterInner(outer, inner);
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index + 1, Stop>::run(kernel);
-  }
-};
-
-template <typename Kernel, int Stop>
-struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) {}
-};
-
-template <typename Kernel, int Index_, int Stop>
-struct copy_using_evaluator_DefaultTraversal_InnerUnrolling {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel, Index outer) {
-    kernel.assignCoeffByOuterInner(outer, Index_);
-    copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_ + 1, Stop>::run(kernel, outer);
-  }
-};
-
-template <typename Kernel, int Stop>
-struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) {}
-};
-
-/***********************
-*** Linear traversal ***
-***********************/
-
-template <typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_LinearTraversal_CompleteUnrolling {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    kernel.assignCoeff(Index);
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index + 1, Stop>::run(kernel);
-  }
-};
-
-template <typename Kernel, int Stop>
-struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) {}
-};
-
-/**************************
-*** Inner vectorization ***
-**************************/
-
-template <typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_innervec_CompleteUnrolling {
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-  typedef typename Kernel::PacketType PacketType;
-
-  enum {
-    outer = Index / DstXprType::InnerSizeAtCompileTime,
-    inner = Index % DstXprType::InnerSizeAtCompileTime,
-    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
-    DstAlignment = Kernel::AssignmentTraits::DstAlignment
-  };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
-    enum { NextIndex = Index + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
-  }
-};
-
-template <typename Kernel, int Stop>
-struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) {}
-};
-
-template <typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
-struct copy_using_evaluator_innervec_InnerUnrolling {
-  typedef typename Kernel::PacketType PacketType;
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel, Index outer) {
-    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
-    enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel,
-                                                                                                           outer);
-  }
-};
-
-template <typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
-struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) {}
-};
-
-/***************************************************************************
- * Part 3 : implementation of all cases
- ***************************************************************************/
-
-// dense_assignment_loop is based on assign_impl
-
-template <typename Kernel, int Traversal = Kernel::AssignmentTraits::Traversal,
-          int Unrolling = Kernel::AssignmentTraits::Unrolling>
-struct dense_assignment_loop;
-
-/************************
-***** Special Cases *****
-************************/
-
-// Zero-sized assignment is a no-op.
-template <typename Kernel, int Unrolling>
-struct dense_assignment_loop<Kernel, AllAtOnceTraversal, Unrolling> {
-  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE EIGEN_CONSTEXPR run(Kernel& /*kernel*/) {
-    EIGEN_STATIC_ASSERT(int(Kernel::DstEvaluatorType::XprType::SizeAtCompileTime) == 0,
-                        EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT)
-  }
-};
-
-/************************
-*** Default traversal ***
-************************/
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling> {
-  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel& kernel) {
-    for (Index outer = 0; outer < kernel.outerSize(); ++outer) {
-      for (Index inner = 0; inner < kernel.innerSize(); ++inner) {
-        kernel.assignCoeffByOuterInner(outer, inner);
-      }
-    }
-  }
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-
-    const Index outerSize = kernel.outerSize();
-    for (Index outer = 0; outer < outerSize; ++outer)
-      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel,
-                                                                                                               outer);
-  }
-};
-
-/***************************
-*** Linear vectorization ***
-***************************/
-
-// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
-// of the non vectorizable beginning and ending parts
-
-template <bool IsAligned = false>
-struct unaligned_dense_assignment_loop {
-  // if IsAligned = true, then do nothing
-  template <typename Kernel>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&, Index, Index) {}
-};
-
-template <>
-struct unaligned_dense_assignment_loop<false> {
-  // MSVC must not inline this functions. If it does, it fails to optimize the
-  // packet access path.
-  // FIXME check which version exhibits this issue
-#if EIGEN_COMP_MSVC
-  template <typename Kernel>
-  static EIGEN_DONT_INLINE void run(Kernel& kernel, Index start, Index end)
-#else
-  template <typename Kernel>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel, Index start, Index end)
-#endif
-  {
-    for (Index index = start; index < end; ++index) kernel.assignCoeff(index);
-  }
-};
-
-template <typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_linearvec_CompleteUnrolling {
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-  typedef typename Kernel::PacketType PacketType;
-
-  enum { SrcAlignment = Kernel::AssignmentTraits::SrcAlignment, DstAlignment = Kernel::AssignmentTraits::DstAlignment };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    kernel.template assignPacket<DstAlignment, SrcAlignment, PacketType>(Index);
-    enum { NextIndex = Index + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
-  }
-};
-
-template <typename Kernel, int Stop>
-struct copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, Stop, Stop> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) {}
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    const Index size = kernel.size();
-    typedef typename Kernel::Scalar Scalar;
-    typedef typename Kernel::PacketType PacketType;
-    enum {
-      requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
-      packetSize = unpacket_traits<PacketType>::size,
-      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment) >= int(requestedAlignment),
-      dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
-                                                            : int(Kernel::AssignmentTraits::DstAlignment),
-      srcAlignment = Kernel::AssignmentTraits::JointAlignment
-    };
-    const Index alignedStart =
-        dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
-    const Index alignedEnd = alignedStart + ((size - alignedStart) / packetSize) * packetSize;
-
-    unaligned_dense_assignment_loop<dstIsAligned != 0>::run(kernel, 0, alignedStart);
-
-    for (Index index = alignedStart; index < alignedEnd; index += packetSize)
-      kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
-
-    unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
-  }
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::PacketType PacketType;
-
-    enum {
-      size = DstXprType::SizeAtCompileTime,
-      packetSize = unpacket_traits<PacketType>::size,
-      alignedSize = (int(size) / packetSize) * packetSize
-    };
-
-    copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
-  }
-};
-
-/**************************
-*** Inner vectorization ***
-**************************/
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling> {
-  typedef typename Kernel::PacketType PacketType;
-  enum { SrcAlignment = Kernel::AssignmentTraits::SrcAlignment, DstAlignment = Kernel::AssignmentTraits::DstAlignment };
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    const Index innerSize = kernel.innerSize();
-    const Index outerSize = kernel.outerSize();
-    const Index packetSize = unpacket_traits<PacketType>::size;
-    for (Index outer = 0; outer < outerSize; ++outer)
-      for (Index inner = 0; inner < innerSize; inner += packetSize)
-        kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
-  }
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::AssignmentTraits Traits;
-    const Index outerSize = kernel.outerSize();
-    for (Index outer = 0; outer < outerSize; ++outer)
-      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime, Traits::SrcAlignment,
-                                                   Traits::DstAlignment>::run(kernel, outer);
-  }
-};
-
-/***********************
-*** Linear traversal ***
-***********************/
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    const Index size = kernel.size();
-    for (Index i = 0; i < size; ++i) kernel.assignCoeff(i);
-  }
-};
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-/**************************
-*** Slice vectorization ***
-***************************/
-
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    typedef typename Kernel::Scalar Scalar;
-    typedef typename Kernel::PacketType PacketType;
-    enum {
-      packetSize = unpacket_traits<PacketType>::size,
-      requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
-      alignable =
-          packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment) >= sizeof(Scalar),
-      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment) >= int(requestedAlignment),
-      dstAlignment = alignable ? int(requestedAlignment) : int(Kernel::AssignmentTraits::DstAlignment)
-    };
-    const Scalar* dst_ptr = kernel.dstDataPtr();
-    if ((!bool(dstIsAligned)) && (std::uintptr_t(dst_ptr) % sizeof(Scalar)) > 0) {
-      // the pointer is not aligned-on scalar, so alignment is not possible
-      return dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>::run(kernel);
-    }
-    const Index packetAlignedMask = packetSize - 1;
-    const Index innerSize = kernel.innerSize();
-    const Index outerSize = kernel.outerSize();
-    const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
-    Index alignedStart =
-        ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
-
-    for (Index outer = 0; outer < outerSize; ++outer) {
-      const Index alignedEnd = alignedStart + ((innerSize - alignedStart) & ~packetAlignedMask);
-      // do the non-vectorizable part of the assignment
-      for (Index inner = 0; inner < alignedStart; ++inner) kernel.assignCoeffByOuterInner(outer, inner);
-
-      // do the vectorizable part of the assignment
-      for (Index inner = alignedStart; inner < alignedEnd; inner += packetSize)
-        kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
-
-      // do the non-vectorizable part of the assignment
-      for (Index inner = alignedEnd; inner < innerSize; ++inner) kernel.assignCoeffByOuterInner(outer, inner);
-
-      alignedStart = numext::mini((alignedStart + alignedStep) % packetSize, innerSize);
-    }
-  }
-};
-
-#if EIGEN_UNALIGNED_VECTORIZE
-template <typename Kernel>
-struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel) {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::PacketType PacketType;
-
-    enum {
-      innerSize = DstXprType::InnerSizeAtCompileTime,
-      packetSize = unpacket_traits<PacketType>::size,
-      vectorizableSize = (int(innerSize) / int(packetSize)) * int(packetSize),
-      size = DstXprType::SizeAtCompileTime
-    };
-
-    for (Index outer = 0; outer < kernel.outerSize(); ++outer) {
-      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
-      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, innerSize>::run(kernel, outer);
-    }
-  }
-};
-#endif
-
-/***************************************************************************
- * Part 4 : Generic dense assignment kernel
- ***************************************************************************/
-
-// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
-// to another dense writable evaluator.
-// It is parametrized by the two evaluators, and the actual assignment functor.
-// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
-// One can customize the assignment using this generic dense_assignment_kernel with different
-// functors, or by completely overloading it, by-passing a functor.
-template <typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
-class generic_dense_assignment_kernel {
- protected:
-  typedef typename DstEvaluatorTypeT::XprType DstXprType;
-  typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
-
- public:
-  typedef DstEvaluatorTypeT DstEvaluatorType;
-  typedef SrcEvaluatorTypeT SrcEvaluatorType;
-  typedef typename DstEvaluatorType::Scalar Scalar;
-  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
-  typedef typename AssignmentTraits::PacketType PacketType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE generic_dense_assignment_kernel(DstEvaluatorType& dst,
-                                                                        const SrcEvaluatorType& src,
-                                                                        const Functor& func, DstXprType& dstExpr)
-      : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr) {
-#ifdef EIGEN_DEBUG_ASSIGN
-    AssignmentTraits::debug();
-#endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_dstExpr.size(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index innerSize() const EIGEN_NOEXCEPT { return m_dstExpr.innerSize(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerSize() const EIGEN_NOEXCEPT { return m_dstExpr.outerSize(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dstExpr.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_dstExpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerStride() const EIGEN_NOEXCEPT { return m_dstExpr.outerStride(); }
-
-  EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() EIGEN_NOEXCEPT { return m_dst; }
-  EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const EIGEN_NOEXCEPT { return m_src; }
-
-  /// Assign src(row,col) to dst(row,col) through the assignment functor.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col) {
-    m_functor.assignCoeff(m_dst.coeffRef(row, col), m_src.coeff(row, col));
-  }
-
-  /// \sa assignCoeff(Index,Index)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index) {
-    m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
-  }
-
-  /// \sa assignCoeff(Index,Index)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner) {
-    Index row = rowIndexByOuterInner(outer, inner);
-    Index col = colIndexByOuterInner(outer, inner);
-    assignCoeff(row, col);
-  }
-
-  template <int StoreMode, int LoadMode, typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col) {
-    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row, col),
-                                               m_src.template packet<LoadMode, Packet>(row, col));
-  }
-
-  template <int StoreMode, int LoadMode, typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index) {
-    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode, Packet>(index));
-  }
-
-  template <int StoreMode, int LoadMode, typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner) {
-    Index row = rowIndexByOuterInner(outer, inner);
-    Index col = colIndexByOuterInner(outer, inner);
-    assignPacket<StoreMode, LoadMode, Packet>(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) {
-    typedef typename DstEvaluatorType::ExpressionTraits Traits;
-    return int(Traits::RowsAtCompileTime) == 1          ? 0
-           : int(Traits::ColsAtCompileTime) == 1        ? inner
-           : int(DstEvaluatorType::Flags) & RowMajorBit ? outer
-                                                        : inner;
-  }
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) {
-    typedef typename DstEvaluatorType::ExpressionTraits Traits;
-    return int(Traits::ColsAtCompileTime) == 1          ? 0
-           : int(Traits::RowsAtCompileTime) == 1        ? inner
-           : int(DstEvaluatorType::Flags) & RowMajorBit ? inner
-                                                        : outer;
-  }
-
-  EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const { return m_dstExpr.data(); }
-
- protected:
-  DstEvaluatorType& m_dst;
-  const SrcEvaluatorType& m_src;
-  const Functor& m_functor;
-  // TODO find a way to avoid the needs of the original expression
-  DstXprType& m_dstExpr;
-};
-
-// Special kernel used when computing small products whose operands have dynamic dimensions.  It ensures that the
-// PacketSize used is no larger than 4, thereby increasing the chance that vectorized instructions will be used
-// when computing the product.
-
-template <typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor>
-class restricted_packet_dense_assignment_kernel
-    : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> {
- protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> Base;
-
- public:
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::DstXprType DstXprType;
-  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, 4> AssignmentTraits;
-  typedef typename AssignmentTraits::PacketType PacketType;
-
-  EIGEN_DEVICE_FUNC restricted_packet_dense_assignment_kernel(DstEvaluatorTypeT& dst, const SrcEvaluatorTypeT& src,
-                                                              const Functor& func, DstXprType& dstExpr)
-      : Base(dst, src, func, dstExpr) {}
-};
-
-/***************************************************************************
- * Part 5 : Entry point for dense rectangular assignment
- ***************************************************************************/
-
-template <typename DstXprType, typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize_if_allowed(DstXprType& dst, const SrcXprType& src,
-                                                             const Functor& /*func*/) {
-  EIGEN_ONLY_USED_FOR_DEBUG(dst);
-  EIGEN_ONLY_USED_FOR_DEBUG(src);
-  eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-}
-
-template <typename DstXprType, typename SrcXprType, typename T1, typename T2>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize_if_allowed(DstXprType& dst, const SrcXprType& src,
-                                                             const internal::assign_op<T1, T2>& /*func*/) {
-  Index dstRows = src.rows();
-  Index dstCols = src.cols();
-  if (((dst.rows() != dstRows) || (dst.cols() != dstCols))) dst.resize(dstRows, dstCols);
-  eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
-}
-
-template <typename DstXprType, typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_dense_assignment_loop(DstXprType& dst,
-                                                                                      const SrcXprType& src,
-                                                                                      const Functor& func) {
-  typedef evaluator<DstXprType> DstEvaluatorType;
-  typedef evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
-  // we need to resize the destination after the source evaluator has been created.
-  resize_if_allowed(dst, src, func);
-
-  DstEvaluatorType dstEvaluator(dst);
-
-  typedef generic_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Functor> Kernel;
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  dense_assignment_loop<Kernel>::run(kernel);
-}
-
-// Specialization for filling the destination with a constant value.
-#ifndef EIGEN_GPU_COMPILE_PHASE
-template <typename DstXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(
-    DstXprType& dst,
-    const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<typename DstXprType::Scalar>, DstXprType>& src,
-    const internal::assign_op<typename DstXprType::Scalar, typename DstXprType::Scalar>& func) {
-  resize_if_allowed(dst, src, func);
-  std::fill_n(dst.data(), dst.size(), src.functor()());
-}
-#endif
-
-template <typename DstXprType, typename SrcXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src) {
-  call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>());
-}
-
-/***************************************************************************
- * Part 6 : Generic assignment
- ***************************************************************************/
-
-// Based on the respective shapes of the destination and source,
-// the class AssignmentKind determine the kind of assignment mechanism.
-// AssignmentKind must define a Kind typedef.
-template <typename DstShape, typename SrcShape>
-struct AssignmentKind;
-
-// Assignment kind defined in this file:
-struct Dense2Dense {};
-struct EigenBase2EigenBase {};
-
-template <typename, typename>
-struct AssignmentKind {
-  typedef EigenBase2EigenBase Kind;
-};
-template <>
-struct AssignmentKind<DenseShape, DenseShape> {
-  typedef Dense2Dense Kind;
-};
-
-// This is the main assignment class
-template <typename DstXprType, typename SrcXprType, typename Functor,
-          typename Kind = typename AssignmentKind<typename evaluator_traits<DstXprType>::Shape,
-                                                  typename evaluator_traits<SrcXprType>::Shape>::Kind,
-          typename EnableIf = void>
-struct Assignment;
-
-// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic
-// transposition. Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite
-// complicated. So this intermediate function removes everything related to "assume-aliasing" such that Assignment does
-// not has to bother about these annoying details.
-
-template <typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_assignment(Dst& dst, const Src& src) {
-  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
-}
-template <typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_assignment(const Dst& dst, const Src& src) {
-  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
-}
-
-// Deal with "assume-aliasing"
-template <typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment(
-    Dst& dst, const Src& src, const Func& func, std::enable_if_t<evaluator_assume_aliasing<Src>::value, void*> = 0) {
-  typename plain_matrix_type<Src>::type tmp(src);
-  call_assignment_no_alias(dst, tmp, func);
-}
-
-template <typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_assignment(
-    Dst& dst, const Src& src, const Func& func, std::enable_if_t<!evaluator_assume_aliasing<Src>::value, void*> = 0) {
-  call_assignment_no_alias(dst, src, func);
-}
-
-// by-pass "assume-aliasing"
-// When there is no aliasing, we require that 'dst' has been properly resized
-template <typename Dst, template <typename> class StorageBase, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment(NoAlias<Dst, StorageBase>& dst,
-                                                                           const Src& src, const Func& func) {
-  call_assignment_no_alias(dst.expression(), src, func);
-}
-
-template <typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment_no_alias(Dst& dst, const Src& src,
-                                                                                    const Func& func) {
-  enum {
-    NeedToTranspose = ((int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1) ||
-                       (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)) &&
-                      int(Dst::SizeAtCompileTime) != 1
-  };
-
-  typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst> ActualDstTypeCleaned;
-  typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&> ActualDstType;
-  ActualDstType actualDst(dst);
-
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned, Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename ActualDstTypeCleaned::Scalar, typename Src::Scalar);
-
-  Assignment<ActualDstTypeCleaned, Src, Func>::run(actualDst, src, func);
-}
-
-template <typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src,
-                                                                                      const Func& func) {
-  typedef evaluator<Dst> DstEvaluatorType;
-  typedef evaluator<Src> SrcEvaluatorType;
-  typedef restricted_packet_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Func> Kernel;
-
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename Dst::Scalar, typename Src::Scalar);
-
-  SrcEvaluatorType srcEvaluator(src);
-  resize_if_allowed(dst, src, func);
-
-  DstEvaluatorType dstEvaluator(dst);
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  dense_assignment_loop<Kernel>::run(kernel);
-}
-
-template <typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment_no_alias(Dst& dst, const Src& src) {
-  call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
-}
-
-template <typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment_no_alias_no_transpose(Dst& dst,
-                                                                                                 const Src& src,
-                                                                                                 const Func& func) {
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst, Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename Dst::Scalar, typename Src::Scalar);
-
-  Assignment<Dst, Src, Func>::run(dst, src, func);
-}
-template <typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment_no_alias_no_transpose(Dst& dst,
-                                                                                                 const Src& src) {
-  call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar, typename Src::Scalar>());
-}
-
-// forward declaration
-template <typename Dst, typename Src>
-EIGEN_DEVICE_FUNC void check_for_aliasing(const Dst& dst, const Src& src);
-
-// Generic Dense to Dense assignment
-// Note that the last template argument "Weak" is needed to make it possible to perform
-// both partial specialization+SFINAE without ambiguous specialization
-template <typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func) {
-#ifndef EIGEN_NO_DEBUG
-    internal::check_for_aliasing(dst, src);
-#endif
-
-    call_dense_assignment_loop(dst, src, func);
-  }
-};
-
-// Generic assignment through evalTo.
-// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
-// Note that the last template argument "Weak" is needed to make it possible to perform
-// both partial specialization+SFINAE without ambiguous specialization
-template <typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(
-      DstXprType& dst, const SrcXprType& src,
-      const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.evalTo(dst);
-  }
-
-  // NOTE The following two functions are templated to avoid their instantiation if not needed
-  //      This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
-  template <typename SrcScalarType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(
-      DstXprType& dst, const SrcXprType& src,
-      const internal::add_assign_op<typename DstXprType::Scalar, SrcScalarType>& /*func*/) {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.addTo(dst);
-  }
-
-  template <typename SrcScalarType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(
-      DstXprType& dst, const SrcXprType& src,
-      const internal::sub_assign_op<typename DstXprType::Scalar, SrcScalarType>& /*func*/) {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.subTo(dst);
-  }
-};
-
-}  // namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_ASSIGN_EVALUATOR_H

Eigen/src/Core/Assign_MKL.h

@@ -1,183 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
- Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL
- *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
- ********************************************************************************
-*/
-
-#ifndef EIGEN_ASSIGN_VML_H
-#define EIGEN_ASSIGN_VML_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Dst, typename Src>
-class vml_assign_traits {
- private:
-  enum {
-    DstHasDirectAccess = Dst::Flags & DirectAccessBit,
-    SrcHasDirectAccess = Src::Flags & DirectAccessBit,
-    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
-    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
-                : int(Dst::Flags) & RowMajorBit ? int(Dst::ColsAtCompileTime)
-                                                : int(Dst::RowsAtCompileTime),
-    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
-                   : int(Dst::Flags) & RowMajorBit ? int(Dst::MaxColsAtCompileTime)
-                                                   : int(Dst::MaxRowsAtCompileTime),
-    MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
-
-    MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess &&
-                     Src::InnerStrideAtCompileTime == 1 && Dst::InnerStrideAtCompileTime == 1,
-    MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
-    VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
-    LargeEnough = VmlSize == Dynamic || VmlSize >= EIGEN_MKL_VML_THRESHOLD
-  };
-
- public:
-  enum { EnableVml = MightEnableVml && LargeEnough, Traversal = MightLinearize ? LinearTraversal : DefaultTraversal };
-};
-
-#define EIGEN_PP_EXPAND(ARG) ARG
-#if !defined(EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
-#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
-#else
-#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
-#endif
-
-#define EIGEN_VMLMODE_EXPAND_x_
-
-#define EIGEN_VMLMODE_PREFIX_xLA vm
-#define EIGEN_VMLMODE_PREFIX_x_ v
-#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x, VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                      \
-  template <typename DstXprType, typename SrcXprNested>                                                    \
-  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>,              \
-                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
-                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
-    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                       \
-    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
-      resize_if_allowed(dst, src, func);                                                                   \
-      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
-      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == LinearTraversal) {                     \
-        VMLOP(dst.size(), (const VMLTYPE *)src.nestedExpression().data(),                                  \
-              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
-      } else {                                                                                             \
-        const Index outerSize = dst.outerSize();                                                           \
-        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
-          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer, 0))         \
-                                                    : &(src.nestedExpression().coeffRef(0, outer));        \
-          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
-          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr,                                                 \
-                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
-        }                                                                                                  \
-      }                                                                                                    \
-    }                                                                                                      \
-  };
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), s##VMLOP), float, float, VMLMODE) \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), d##VMLOP), double, double, VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                   \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), c##VMLOP), scomplex, \
-                                   MKL_Complex8, VMLMODE)                                                 \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), z##VMLOP), dcomplex, \
-                                   MKL_Complex16, VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)  \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
-
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
-// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
-
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(cbrt, Cbrt, _)
-
-#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                        \
-  template <typename DstXprType, typename SrcXprNested, typename Plain>                                    \
-  struct Assignment<DstXprType,                                                                            \
-                    CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,               \
-                                  const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>,   \
-                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
-                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
-    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,                       \
-                          const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>            \
-        SrcXprType;                                                                                        \
-    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
-      resize_if_allowed(dst, src, func);                                                                   \
-      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
-      VMLTYPE exponent = reinterpret_cast<const VMLTYPE &>(src.rhs().functor().m_other);                   \
-      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == LinearTraversal) {                     \
-        VMLOP(dst.size(), (const VMLTYPE *)src.lhs().data(), exponent,                                     \
-              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
-      } else {                                                                                             \
-        const Index outerSize = dst.outerSize();                                                           \
-        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
-          const EIGENTYPE *src_ptr =                                                                       \
-              src.IsRowMajor ? &(src.lhs().coeffRef(outer, 0)) : &(src.lhs().coeffRef(0, outer));          \
-          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
-          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr, exponent,                                       \
-                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
-        }                                                                                                  \
-      }                                                                                                    \
-    }                                                                                                      \
-  };
-
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_ASSIGN_VML_H

Eigen/src/Core/BandMatrix.h

@@ -3,336 +3,344 @@
 //
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_BANDMATRIX_H
 #define EIGEN_BANDMATRIX_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 namespace internal {
 
-template <typename Derived>
-class BandMatrixBase : public EigenBase<Derived> {
- public:
-  enum {
-    Flags = internal::traits<Derived>::Flags,
-    CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
-    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-    Supers = internal::traits<Derived>::Supers,
-    Subs = internal::traits<Derived>::Subs,
-    Options = internal::traits<Derived>::Options
-  };
-  typedef typename internal::traits<Derived>::Scalar Scalar;
-  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> DenseMatrixType;
-  typedef typename DenseMatrixType::StorageIndex StorageIndex;
-  typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
-  typedef EigenBase<Derived> Base;
 
- protected:
-  enum {
-    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic,
-    SizeAtCompileTime = min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)
-  };
+template<typename Derived>
+class BandMatrixBase : public EigenBase<Derived>
+{
+  public:
 
- public:
-  using Base::cols;
-  using Base::derived;
-  using Base::rows;
-
-  /** \returns the number of super diagonals */
-  inline Index supers() const { return derived().supers(); }
-
-  /** \returns the number of sub diagonals */
-  inline Index subs() const { return derived().subs(); }
-
-  /** \returns an expression of the underlying coefficient matrix */
-  inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
-
-  /** \returns an expression of the underlying coefficient matrix */
-  inline CoefficientsType& coeffs() { return derived().coeffs(); }
-
-  /** \returns a vector expression of the \a i -th column,
-   * only the meaningful part is returned.
-   * \warning the internal storage must be column major. */
-  inline Block<CoefficientsType, Dynamic, 1> col(Index i) {
-    EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-    Index start = 0;
-    Index len = coeffs().rows();
-    if (i <= supers()) {
-      start = supers() - i;
-      len = (std::min)(rows(), std::max<Index>(0, coeffs().rows() - (supers() - i)));
-    } else if (i >= rows() - subs())
-      len = std::max<Index>(0, coeffs().rows() - (i + 1 - rows() + subs()));
-    return Block<CoefficientsType, Dynamic, 1>(coeffs(), start, i, len, 1);
-  }
-
-  /** \returns a vector expression of the main diagonal */
-  inline Block<CoefficientsType, 1, SizeAtCompileTime> diagonal() {
-    return Block<CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
-  }
-
-  /** \returns a vector expression of the main diagonal (const version) */
-  inline const Block<const CoefficientsType, 1, SizeAtCompileTime> diagonal() const {
-    return Block<const CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
-  }
-
-  template <int Index>
-  struct DiagonalIntReturnType {
     enum {
-      ReturnOpposite =
-          (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
-      Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
-      ActualIndex = ReturnOpposite ? -Index : Index,
-      DiagonalSize =
-          (RowsAtCompileTime == Dynamic || ColsAtCompileTime == Dynamic)
-              ? Dynamic
-              : (ActualIndex < 0 ? min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
-                                 : min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
+      Flags = internal::traits<Derived>::Flags,
+      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+      Supers = internal::traits<Derived>::Supers,
+      Subs   = internal::traits<Derived>::Subs,
+      Options = internal::traits<Derived>::Options
     };
-    typedef Block<CoefficientsType, 1, DiagonalSize> BuildType;
-    typedef std::conditional_t<Conjugate, CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, BuildType>, BuildType>
-        Type;
-  };
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
+    typedef typename DenseMatrixType::Index Index;
+    typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
+    typedef EigenBase<Derived> Base;
 
-  /** \returns a vector expression of the \a N -th sub or super diagonal */
-  template <int N>
-  inline typename DiagonalIntReturnType<N>::Type diagonal() {
-    return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers() - N, (std::max)(0, N), 1, diagonalLength(N));
-  }
+  protected:
+    enum {
+      DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
+                            ? 1 + Supers + Subs
+                            : Dynamic,
+      SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
+    };
 
-  /** \returns a vector expression of the \a N -th sub or super diagonal */
-  template <int N>
-  inline const typename DiagonalIntReturnType<N>::Type diagonal() const {
-    return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers() - N, (std::max)(0, N), 1, diagonalLength(N));
-  }
+  public:
+    
+    using Base::derived;
+    using Base::rows;
+    using Base::cols;
 
-  /** \returns a vector expression of the \a i -th sub or super diagonal */
-  inline Block<CoefficientsType, 1, Dynamic> diagonal(Index i) {
-    eigen_assert((i < 0 && -i <= subs()) || (i >= 0 && i <= supers()));
-    return Block<CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1, diagonalLength(i));
-  }
+    /** \returns the number of super diagonals */
+    inline Index supers() const { return derived().supers(); }
 
-  /** \returns a vector expression of the \a i -th sub or super diagonal */
-  inline const Block<const CoefficientsType, 1, Dynamic> diagonal(Index i) const {
-    eigen_assert((i < 0 && -i <= subs()) || (i >= 0 && i <= supers()));
-    return Block<const CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1,
-                                                     diagonalLength(i));
-  }
+    /** \returns the number of sub diagonals */
+    inline Index subs() const { return derived().subs(); }
+    
+    /** \returns an expression of the underlying coefficient matrix */
+    inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
+    
+    /** \returns an expression of the underlying coefficient matrix */
+    inline CoefficientsType& coeffs() { return derived().coeffs(); }
 
-  template <typename Dest>
-  inline void evalTo(Dest& dst) const {
-    dst.resize(rows(), cols());
-    dst.setZero();
-    dst.diagonal() = diagonal();
-    for (Index i = 1; i <= supers(); ++i) dst.diagonal(i) = diagonal(i);
-    for (Index i = 1; i <= subs(); ++i) dst.diagonal(-i) = diagonal(-i);
-  }
+    /** \returns a vector expression of the \a i -th column,
+      * only the meaningful part is returned.
+      * \warning the internal storage must be column major. */
+    inline Block<CoefficientsType,Dynamic,1> col(Index i)
+    {
+      EIGEN_STATIC_ASSERT((Options&RowMajor)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+      Index start = 0;
+      Index len = coeffs().rows();
+      if (i<=supers())
+      {
+        start = supers()-i;
+        len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
+      }
+      else if (i>=rows()-subs())
+        len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
+      return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
+    }
 
-  DenseMatrixType toDenseMatrix() const {
-    DenseMatrixType res(rows(), cols());
-    evalTo(res);
-    return res;
-  }
+    /** \returns a vector expression of the main diagonal */
+    inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
+    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
 
- protected:
-  inline Index diagonalLength(Index i) const {
-    return i < 0 ? (std::min)(cols(), rows() + i) : (std::min)(rows(), cols() - i);
-  }
+    /** \returns a vector expression of the main diagonal (const version) */
+    inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
+    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
+
+    template<int Index> struct DiagonalIntReturnType {
+      enum {
+        ReturnOpposite = (Options&SelfAdjoint) && (((Index)>0 && Supers==0) || ((Index)<0 && Subs==0)),
+        Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
+        ActualIndex = ReturnOpposite ? -Index : Index,
+        DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
+                     ? Dynamic
+                     : (ActualIndex<0
+                     ? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
+                     : EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
+      };
+      typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
+      typedef typename internal::conditional<Conjugate,
+                 CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
+                 BuildType>::type Type;
+    };
+
+    /** \returns a vector expression of the \a N -th sub or super diagonal */
+    template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
+    {
+      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
+    }
+
+    /** \returns a vector expression of the \a N -th sub or super diagonal */
+    template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
+    {
+      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
+    }
+
+    /** \returns a vector expression of the \a i -th sub or super diagonal */
+    inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
+    {
+      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
+      return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
+    }
+
+    /** \returns a vector expression of the \a i -th sub or super diagonal */
+    inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
+    {
+      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
+      return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
+    }
+    
+    template<typename Dest> inline void evalTo(Dest& dst) const
+    {
+      dst.resize(rows(),cols());
+      dst.setZero();
+      dst.diagonal() = diagonal();
+      for (Index i=1; i<=supers();++i)
+        dst.diagonal(i) = diagonal(i);
+      for (Index i=1; i<=subs();++i)
+        dst.diagonal(-i) = diagonal(-i);
+    }
+
+    DenseMatrixType toDenseMatrix() const
+    {
+      DenseMatrixType res(rows(),cols());
+      evalTo(res);
+      return res;
+    }
+
+  protected:
+
+    inline Index diagonalLength(Index i) const
+    { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
 };
 
 /**
- * \class BandMatrix
- * \ingroup Core_Module
- *
- * \brief Represents a rectangular matrix with a banded storage
- *
- * \tparam Scalar_ Numeric type, i.e. float, double, int
- * \tparam Rows_ Number of rows, or \b Dynamic
- * \tparam Cols_ Number of columns, or \b Dynamic
- * \tparam Supers_ Number of super diagonal
- * \tparam Subs_ Number of sub diagonal
- * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
- *                  The former controls \ref TopicStorageOrders "storage order", and defaults to
- *                  column-major. The latter controls whether the matrix represents a selfadjoint
- *                  matrix in which case either Supers of Subs have to be null.
- *
- * \sa class TridiagonalMatrix
- */
+  * \class BandMatrix
+  * \ingroup Core_Module
+  *
+  * \brief Represents a rectangular matrix with a banded storage
+  *
+  * \param _Scalar Numeric type, i.e. float, double, int
+  * \param Rows Number of rows, or \b Dynamic
+  * \param Cols Number of columns, or \b Dynamic
+  * \param Supers Number of super diagonal
+  * \param Subs Number of sub diagonal
+  * \param _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
+  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to
+  *                 column-major. The latter controls whether the matrix represents a selfadjoint 
+  *                 matrix in which case either Supers of Subs have to be null.
+  *
+  * \sa class TridiagonalMatrix
+  */
 
-template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
-struct traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
-  typedef Scalar_ Scalar;
+template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
+struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
+{
+  typedef _Scalar Scalar;
   typedef Dense StorageKind;
-  typedef Eigen::Index StorageIndex;
+  typedef DenseIndex Index;
   enum {
     CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    RowsAtCompileTime = Rows_,
-    ColsAtCompileTime = Cols_,
-    MaxRowsAtCompileTime = Rows_,
-    MaxColsAtCompileTime = Cols_,
+    RowsAtCompileTime = _Rows,
+    ColsAtCompileTime = _Cols,
+    MaxRowsAtCompileTime = _Rows,
+    MaxColsAtCompileTime = _Cols,
     Flags = LvalueBit,
-    Supers = Supers_,
-    Subs = Subs_,
-    Options = Options_,
-    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic
+    Supers = _Supers,
+    Subs = _Subs,
+    Options = _Options,
+    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
   };
-  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor>
-      CoefficientsType;
+  typedef Matrix<Scalar,DataRowsAtCompileTime,ColsAtCompileTime,Options&RowMajor?RowMajor:ColMajor> CoefficientsType;
 };
 
-template <typename Scalar_, int Rows, int Cols, int Supers, int Subs, int Options>
-class BandMatrix : public BandMatrixBase<BandMatrix<Scalar_, Rows, Cols, Supers, Subs, Options> > {
- public:
-  typedef typename internal::traits<BandMatrix>::Scalar Scalar;
-  typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
-  typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
+template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
+class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
+{
+  public:
 
-  explicit inline BandMatrix(Index rows = Rows, Index cols = Cols, Index supers = Supers, Index subs = Subs)
-      : m_coeffs(1 + supers + subs, cols), m_rows(rows), m_supers(supers), m_subs(subs) {}
+    typedef typename internal::traits<BandMatrix>::Scalar Scalar;
+    typedef typename internal::traits<BandMatrix>::Index Index;
+    typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
 
-  /** \returns the number of columns */
-  inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
+    inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
+      : m_coeffs(1+supers+subs,cols),
+        m_rows(rows), m_supers(supers), m_subs(subs)
+    {
+    }
 
-  /** \returns the number of rows */
-  inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
+    /** \returns the number of columns */
+    inline Index rows() const { return m_rows.value(); }
 
-  /** \returns the number of super diagonals */
-  inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
+    /** \returns the number of rows */
+    inline Index cols() const { return m_coeffs.cols(); }
 
-  /** \returns the number of sub diagonals */
-  inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
+    /** \returns the number of super diagonals */
+    inline Index supers() const { return m_supers.value(); }
 
-  inline const CoefficientsType& coeffs() const { return m_coeffs; }
-  inline CoefficientsType& coeffs() { return m_coeffs; }
+    /** \returns the number of sub diagonals */
+    inline Index subs() const { return m_subs.value(); }
 
- protected:
-  CoefficientsType m_coeffs;
-  internal::variable_if_dynamic<Index, Rows> m_rows;
-  internal::variable_if_dynamic<Index, Supers> m_supers;
-  internal::variable_if_dynamic<Index, Subs> m_subs;
+    inline const CoefficientsType& coeffs() const { return m_coeffs; }
+    inline CoefficientsType& coeffs() { return m_coeffs; }
+
+  protected:
+
+    CoefficientsType m_coeffs;
+    internal::variable_if_dynamic<Index, Rows>   m_rows;
+    internal::variable_if_dynamic<Index, Supers> m_supers;
+    internal::variable_if_dynamic<Index, Subs>   m_subs;
 };
 
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
 class BandMatrixWrapper;
 
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
-struct traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
-  typedef typename CoefficientsType_::Scalar Scalar;
-  typedef typename CoefficientsType_::StorageKind StorageKind;
-  typedef typename CoefficientsType_::StorageIndex StorageIndex;
+template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
+struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
+{
+  typedef typename _CoefficientsType::Scalar Scalar;
+  typedef typename _CoefficientsType::StorageKind StorageKind;
+  typedef typename _CoefficientsType::Index Index;
   enum {
-    CoeffReadCost = internal::traits<CoefficientsType_>::CoeffReadCost,
-    RowsAtCompileTime = Rows_,
-    ColsAtCompileTime = Cols_,
-    MaxRowsAtCompileTime = Rows_,
-    MaxColsAtCompileTime = Cols_,
+    CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
+    RowsAtCompileTime = _Rows,
+    ColsAtCompileTime = _Cols,
+    MaxRowsAtCompileTime = _Rows,
+    MaxColsAtCompileTime = _Cols,
     Flags = LvalueBit,
-    Supers = Supers_,
-    Subs = Subs_,
-    Options = Options_,
-    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic
+    Supers = _Supers,
+    Subs = _Subs,
+    Options = _Options,
+    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
   };
-  typedef CoefficientsType_ CoefficientsType;
+  typedef _CoefficientsType CoefficientsType;
 };
 
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
-class BandMatrixWrapper
-    : public BandMatrixBase<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
- public:
-  typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
-  typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
-  typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
+template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
+class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
+{
+  public:
 
-  explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows = Rows_, Index cols = Cols_,
-                                    Index supers = Supers_, Index subs = Subs_)
-      : m_coeffs(coeffs), m_rows(rows), m_supers(supers), m_subs(subs) {
-    EIGEN_UNUSED_VARIABLE(cols);
-    // eigen_assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
-  }
+    typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
+    typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
+    typedef typename internal::traits<BandMatrixWrapper>::Index Index;
 
-  /** \returns the number of columns */
-  inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
+    inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
+      : m_coeffs(coeffs),
+        m_rows(rows), m_supers(supers), m_subs(subs)
+    {
+      EIGEN_UNUSED_VARIABLE(cols);
+      //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
+    }
 
-  /** \returns the number of rows */
-  inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
+    /** \returns the number of columns */
+    inline Index rows() const { return m_rows.value(); }
 
-  /** \returns the number of super diagonals */
-  inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
+    /** \returns the number of rows */
+    inline Index cols() const { return m_coeffs.cols(); }
 
-  /** \returns the number of sub diagonals */
-  inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
+    /** \returns the number of super diagonals */
+    inline Index supers() const { return m_supers.value(); }
 
-  inline const CoefficientsType& coeffs() const { return m_coeffs; }
+    /** \returns the number of sub diagonals */
+    inline Index subs() const { return m_subs.value(); }
 
- protected:
-  const CoefficientsType& m_coeffs;
-  internal::variable_if_dynamic<Index, Rows_> m_rows;
-  internal::variable_if_dynamic<Index, Supers_> m_supers;
-  internal::variable_if_dynamic<Index, Subs_> m_subs;
+    inline const CoefficientsType& coeffs() const { return m_coeffs; }
+
+  protected:
+
+    const CoefficientsType& m_coeffs;
+    internal::variable_if_dynamic<Index, _Rows>   m_rows;
+    internal::variable_if_dynamic<Index, _Supers> m_supers;
+    internal::variable_if_dynamic<Index, _Subs>   m_subs;
 };
 
 /**
- * \class TridiagonalMatrix
- * \ingroup Core_Module
- *
- * \brief Represents a tridiagonal matrix with a compact banded storage
- *
- * \tparam Scalar Numeric type, i.e. float, double, int
- * \tparam Size Number of rows and cols, or \b Dynamic
- * \tparam Options Can be 0 or \b SelfAdjoint
- *
- * \sa class BandMatrix
- */
-template <typename Scalar, int Size, int Options>
-class TridiagonalMatrix : public BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> {
-  typedef BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> Base;
-  typedef typename Base::StorageIndex StorageIndex;
+  * \class TridiagonalMatrix
+  * \ingroup Core_Module
+  *
+  * \brief Represents a tridiagonal matrix with a compact banded storage
+  *
+  * \param _Scalar Numeric type, i.e. float, double, int
+  * \param Size Number of rows and cols, or \b Dynamic
+  * \param _Options Can be 0 or \b SelfAdjoint
+  *
+  * \sa class BandMatrix
+  */
+template<typename Scalar, int Size, int Options>
+class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
+{
+    typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
+    typedef typename Base::Index Index;
+  public:
+    TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
 
- public:
-  explicit TridiagonalMatrix(Index size = Size) : Base(size, size, Options & SelfAdjoint ? 0 : 1, 1) {}
-
-  inline typename Base::template DiagonalIntReturnType<1>::Type super() { return Base::template diagonal<1>(); }
-  inline const typename Base::template DiagonalIntReturnType<1>::Type super() const {
-    return Base::template diagonal<1>();
-  }
-  inline typename Base::template DiagonalIntReturnType<-1>::Type sub() { return Base::template diagonal<-1>(); }
-  inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const {
-    return Base::template diagonal<-1>();
-  }
-
- protected:
+    inline typename Base::template DiagonalIntReturnType<1>::Type super()
+    { return Base::template diagonal<1>(); }
+    inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
+    { return Base::template diagonal<1>(); }
+    inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
+    { return Base::template diagonal<-1>(); }
+    inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
+    { return Base::template diagonal<-1>(); }
+  protected:
 };
 
-struct BandShape {};
+} // end namespace internal
 
-template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
-struct evaluator_traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> >
-    : public evaluator_traits_base<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
-  typedef BandShape Shape;
-};
-
-template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
-struct evaluator_traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> >
-    : public evaluator_traits_base<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
-  typedef BandShape Shape;
-};
-
-template <>
-struct AssignmentKind<DenseShape, BandShape> {
-  typedef EigenBase2EigenBase Kind;
-};
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_BANDMATRIX_H
+#endif // EIGEN_BANDMATRIX_H

Eigen/src/Core/Block.h

@@ -4,436 +4,346 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_BLOCK_H
 #define EIGEN_BLOCK_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
+/** \class Block
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a fixed-size or dynamic-size block
+  *
+  * \param XprType the type of the expression in which we are taking a block
+  * \param BlockRows the number of rows of the block we are taking at compile time (optional)
+  * \param BlockCols the number of columns of the block we are taking at compile time (optional)
+  * \param _DirectAccessStatus \internal used for partial specialization
+  *
+  * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
+  * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
+  * most of the time this is the only way it is used.
+  *
+  * However, if you want to directly maniputate block expressions,
+  * for instance if you want to write a function returning such an expression, you
+  * will need to use this class.
+  *
+  * Here is an example illustrating the dynamic case:
+  * \include class_Block.cpp
+  * Output: \verbinclude class_Block.out
+  *
+  * \note Even though this expression has dynamic size, in the case where \a XprType
+  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
+  * it does not cause a dynamic memory allocation.
+  *
+  * Here is an example illustrating the fixed-size case:
+  * \include class_FixedBlock.cpp
+  * Output: \verbinclude class_FixedBlock.out
+  *
+  * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
+  */
 
 namespace internal {
-template <typename XprType_, int BlockRows, int BlockCols, bool InnerPanel_>
-struct traits<Block<XprType_, BlockRows, BlockCols, InnerPanel_>> : traits<XprType_> {
-  typedef typename traits<XprType_>::Scalar Scalar;
-  typedef typename traits<XprType_>::StorageKind StorageKind;
-  typedef typename traits<XprType_>::XprKind XprKind;
-  typedef typename ref_selector<XprType_>::type XprTypeNested;
-  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
-  enum {
-    MatrixRows = traits<XprType_>::RowsAtCompileTime,
-    MatrixCols = traits<XprType_>::ColsAtCompileTime,
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
+struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> > : traits<XprType>
+{
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::XprKind XprKind;
+  typedef typename nested<XprType>::type XprTypeNested;
+  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
+  enum{
+    MatrixRows = traits<XprType>::RowsAtCompileTime,
+    MatrixCols = traits<XprType>::ColsAtCompileTime,
     RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
     ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
-    MaxRowsAtCompileTime = BlockRows == 0                 ? 0
-                           : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
-                                                          : int(traits<XprType_>::MaxRowsAtCompileTime),
-    MaxColsAtCompileTime = BlockCols == 0                 ? 0
-                           : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
-                                                          : int(traits<XprType_>::MaxColsAtCompileTime),
-
-    XprTypeIsRowMajor = (int(traits<XprType_>::Flags) & RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? 1
-                 : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
-                                                                            : XprTypeIsRowMajor,
+    MaxRowsAtCompileTime = BlockRows==0 ? 0
+                         : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
+                         : int(traits<XprType>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = BlockCols==0 ? 0
+                         : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
+                         : int(traits<XprType>::MaxColsAtCompileTime),
+    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
+               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
+               : XprTypeIsRowMajor,
     HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
     InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType_>::ret)
-                                                            : int(outer_stride_at_compile_time<XprType_>::ret),
-    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType_>::ret)
-                                                            : int(inner_stride_at_compile_time<XprType_>::ret),
-
-    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
-    FlagsLvalueBit = is_lvalue<XprType_>::value ? LvalueBit : 0,
+    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
+                             ? int(inner_stride_at_compile_time<XprType>::ret)
+                             : int(outer_stride_at_compile_time<XprType>::ret),
+    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
+                             ? int(outer_stride_at_compile_time<XprType>::ret)
+                             : int(inner_stride_at_compile_time<XprType>::ret),
+    MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
+                       && (InnerStrideAtCompileTime == 1)
+                        ? PacketAccessBit : 0,
+    MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
     FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
-    Flags = (traits<XprType_>::Flags & (DirectAccessBit | (InnerPanel_ ? CompressedAccessBit : 0))) | FlagsLvalueBit |
-            FlagsRowMajorBit,
-    // FIXME DirectAccessBit should not be handled by expressions
-    //
-    // Alignment is needed by MapBase's assertions
-    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the
-    // respective evaluator
-    Alignment = 0,
-    InnerPanel = InnerPanel_ ? 1 : 0
+    Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
+                                        DirectAccessBit |
+                                        MaskPacketAccessBit |
+                                        MaskAlignedBit),
+    Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
   };
 };
+}
 
-template <typename XprType, int BlockRows = Dynamic, int BlockCols = Dynamic, bool InnerPanel = false,
-          bool HasDirectAccess = internal::has_direct_access<XprType>::ret>
-class BlockImpl_dense;
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class Block
+  : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> >::type
+{
+  public:
 
-}  // end namespace internal
+    typedef typename internal::dense_xpr_base<Block>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Block)
 
-template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind>
-class BlockImpl;
+    class InnerIterator;
 
-/** \class Block
- * \ingroup Core_Module
- *
- * \brief Expression of a fixed-size or dynamic-size block
- *
- * \tparam XprType the type of the expression in which we are taking a block
- * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
- * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
- * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
- *         to set of columns of a column major matrix (optional). The parameter allows to determine
- *         at compile time whether aligned access is possible on the block expression.
- *
- * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
- * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
- * most of the time this is the only way it is used.
- *
- * However, if you want to directly manipulate block expressions,
- * for instance if you want to write a function returning such an expression, you
- * will need to use this class.
- *
- * Here is an example illustrating the dynamic case:
- * \include class_Block.cpp
- * Output: \verbinclude class_Block.out
- *
- * \note Even though this expression has dynamic size, in the case where \a XprType
- * has fixed size, this expression inherits a fixed maximal size which means that evaluating
- * it does not cause a dynamic memory allocation.
- *
- * Here is an example illustrating the fixed-size case:
- * \include class_FixedBlock.cpp
- * Output: \verbinclude class_FixedBlock.out
- *
- * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
- */
-template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class Block
-    : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> {
-  typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
-  using BlockHelper = internal::block_xpr_helper<Block>;
-
- public:
-  // typedef typename Impl::Base Base;
-  typedef Impl Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
-
-  typedef internal::remove_all_t<XprType> NestedExpression;
-
-  /** Column or Row constructor
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block(XprType& xpr, Index i) : Impl(xpr, i) {
-    eigen_assert((i >= 0) && (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && i < xpr.rows()) ||
-                              ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) && i < xpr.cols())));
-  }
-
-  /** Fixed-size constructor
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol)
-      : Impl(xpr, startRow, startCol) {
-    EIGEN_STATIC_ASSERT(RowsAtCompileTime != Dynamic && ColsAtCompileTime != Dynamic,
-                        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
-    eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows() && startCol >= 0 &&
-                 BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
-  }
-
-  /** Dynamic-size constructor
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol, Index blockRows,
-                                              Index blockCols)
-      : Impl(xpr, startRow, startCol, blockRows, blockCols) {
-    eigen_assert((RowsAtCompileTime == Dynamic || RowsAtCompileTime == blockRows) &&
-                 (ColsAtCompileTime == Dynamic || ColsAtCompileTime == blockCols));
-    eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 &&
-                 blockCols >= 0 && startCol <= xpr.cols() - blockCols);
-  }
-
-  // convert nested blocks (e.g. Block<Block<MatrixType>>) to a simple block expression (Block<MatrixType>)
-
-  using ConstUnwindReturnType = Block<const typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
-  using UnwindReturnType = Block<typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ConstUnwindReturnType unwind() const {
-    return ConstUnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
-                                 this->rows(), this->cols());
-  }
-
-  template <typename T = Block, typename EnableIf = std::enable_if_t<!std::is_const<T>::value>>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UnwindReturnType unwind() {
-    return UnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
-                            this->rows(), this->cols());
-  }
-};
-
-// The generic default implementation for dense block simply forward to the internal::BlockImpl_dense
-// that must be specialized for direct and non-direct access...
-template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
-    : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> {
-  typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
-  typedef typename XprType::StorageIndex StorageIndex;
-
- public:
-  typedef Impl Base;
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr, i) {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol)
-      : Impl(xpr, startRow, startCol) {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows,
-                                                  Index blockCols)
-      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
-};
-
-namespace internal {
-
-/** \internal Internal implementation of dense Blocks in the general case. */
-template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
-class BlockImpl_dense : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel>>::type {
-  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-
- public:
-  typedef typename internal::dense_xpr_base<BlockType>::type Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
-
-  // class InnerIterator; // FIXME apparently never used
-
-  /** Column or Row constructor
-   */
-  EIGEN_DEVICE_FUNC inline BlockImpl_dense(XprType& xpr, Index i)
+    /** Column or Row constructor
+      */
+    inline Block(XprType& xpr, Index i)
       : m_xpr(xpr),
         // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
         // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
         // all other cases are invalid.
         // The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
-        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
-        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0),
-        m_blockRows(BlockRows == 1 ? 1 : xpr.rows()),
-        m_blockCols(BlockCols == 1 ? 1 : xpr.cols()) {}
+        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
+        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
+        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
+    {
+      eigen_assert( (i>=0) && (
+          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
+        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
+    }
 
-  /** Fixed-size constructor
-   */
-  EIGEN_DEVICE_FUNC inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(BlockRows), m_blockCols(BlockCols) {}
+    /** Fixed-size constructor
+      */
+    inline Block(XprType& xpr, Index startRow, Index startCol)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
+        m_blockRows(BlockRows), m_blockCols(BlockCols)
+    {
+      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
+      eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
+             && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
+    }
 
-  /** Dynamic-size constructor
-   */
-  EIGEN_DEVICE_FUNC inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
-                                           Index blockCols)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(blockRows), m_blockCols(blockCols) {}
+    /** Dynamic-size constructor
+      */
+    inline Block(XprType& xpr,
+          Index startRow, Index startCol,
+          Index blockRows, Index blockCols)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
+                          m_blockRows(blockRows), m_blockCols(blockCols)
+    {
+      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
+          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
+      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
+          && startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
+    }
 
-  EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
-  EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
 
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index rowId, Index colId) {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
-  }
+    inline Index rows() const { return m_blockRows.value(); }
+    inline Index cols() const { return m_blockCols.value(); }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
-    return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
-  }
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(XprType)
+      return m_xpr.const_cast_derived()
+               .coeffRef(row + m_startRow.value(), col + m_startCol.value());
+    }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const {
-    return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
-  }
+    inline const Scalar& coeffRef(Index row, Index col) const
+    {
+      return m_xpr.derived()
+               .coeffRef(row + m_startRow.value(), col + m_startCol.value());
+    }
 
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-  }
+    EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
+    {
+      return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value());
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
-    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-  }
-
-  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const {
-    return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+    inline Scalar& coeffRef(Index index)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(XprType)
+      return m_xpr.const_cast_derived()
+             .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
                        m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-  }
+    }
 
-  template <int LoadMode>
-  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const {
-    return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
-  }
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_xpr.const_cast_derived()
+             .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                       m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
 
-  template <int LoadMode>
-  EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val) {
-    m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
-  }
+    inline const CoeffReturnType coeff(Index index) const
+    {
+      return m_xpr
+             .coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                    m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
 
-  template <int LoadMode>
-  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const {
-    return m_xpr.template packet<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-  }
+    template<int LoadMode>
+    inline PacketScalar packet(Index row, Index col) const
+    {
+      return m_xpr.template packet<Unaligned>
+              (row + m_startRow.value(), col + m_startCol.value());
+    }
 
-  template <int LoadMode>
-  EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val) {
-    m_xpr.template writePacket<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
-  }
+    template<int LoadMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
+    {
+      m_xpr.const_cast_derived().template writePacket<Unaligned>
+              (row + m_startRow.value(), col + m_startCol.value(), x);
+    }
 
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \sa MapBase::data() */
-  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const;
-  EIGEN_DEVICE_FUNC inline Index innerStride() const;
-  EIGEN_DEVICE_FUNC inline Index outerStride() const;
-#endif
+    template<int LoadMode>
+    inline PacketScalar packet(Index index) const
+    {
+      return m_xpr.template packet<Unaligned>
+              (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const {
-    return m_xpr;
-  }
+    template<int LoadMode>
+    inline void writePacket(Index index, const PacketScalar& x)
+    {
+      m_xpr.const_cast_derived().template writePacket<Unaligned>
+         (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x);
+    }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \sa MapBase::data() */
+    inline const Scalar* data() const;
+    inline Index innerStride() const;
+    inline Index outerStride() const;
+    #endif
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR StorageIndex startRow() const EIGEN_NOEXCEPT {
-    return m_startRow.value();
-  }
+  protected:
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR StorageIndex startCol() const EIGEN_NOEXCEPT {
-    return m_startCol.value();
-  }
-
- protected:
-  XprTypeNested m_xpr;
-  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
-      m_startRow;
-  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
-      m_startCol;
-  const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
-  const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
+    const typename XprType::Nested m_xpr;
+    const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
+    const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
+    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
+    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
 };
 
-/** \internal Internal implementation of dense Blocks in the direct access case.*/
-template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel, true>
-    : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel>> {
-  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-  enum { XprTypeIsRowMajor = (int(traits<XprType>::Flags) & RowMajorBit) != 0 };
+/** \internal */
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
+  : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel, true> >
+{
+  public:
 
-  /** \internal Returns base+offset (unless base is null, in which case returns null).
-   * Adding an offset to nullptr is undefined behavior, so we must avoid it.
-   */
-  template <typename Scalar>
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE static Scalar* add_to_nullable_pointer(Scalar* base,
-                                                                                               Index offset) {
-    return base != nullptr ? base + offset : nullptr;
-  }
+    typedef MapBase<Block> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Block)
 
- public:
-  typedef MapBase<BlockType> Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
 
-  /** Column or Row constructor
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index i)
-      : Base((BlockRows == 0 || BlockCols == 0)
-                 ? nullptr
-                 : add_to_nullable_pointer(
-                       xpr.data(),
-                       i * (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor)) ||
-                                    ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) &&
-                                     (XprTypeIsRowMajor))
-                                ? xpr.innerStride()
-                                : xpr.outerStride())),
-             BlockRows == 1 ? 1 : xpr.rows(), BlockCols == 1 ? 1 : xpr.cols()),
-        m_xpr(xpr),
-        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
-        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0) {
-    init();
-  }
+    /** Column or Row constructor
+      */
+    inline Block(XprType& xpr, Index i)
+      : Base(internal::const_cast_ptr(&xpr.coeffRef(
+              (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
+              (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
+             BlockRows==1 ? 1 : xpr.rows(),
+             BlockCols==1 ? 1 : xpr.cols()),
+        m_xpr(xpr)
+    {
+      eigen_assert( (i>=0) && (
+          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
+        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
+      init();
+    }
 
-  /** Fixed-size constructor
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : Base((BlockRows == 0 || BlockCols == 0)
-                 ? nullptr
-                 : add_to_nullable_pointer(xpr.data(),
-                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
-                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol))),
-        m_xpr(xpr),
-        m_startRow(startRow),
-        m_startCol(startCol) {
-    init();
-  }
+    /** Fixed-size constructor
+      */
+    inline Block(XprType& xpr, Index startRow, Index startCol)
+      : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
+    {
+      eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
+             && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
+      init();
+    }
 
-  /** Dynamic-size constructor
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
-                                                        Index blockCols)
-      : Base((blockRows == 0 || blockCols == 0)
-                 ? nullptr
-                 : add_to_nullable_pointer(xpr.data(),
-                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
-                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol)),
-             blockRows, blockCols),
-        m_xpr(xpr),
-        m_startRow(startRow),
-        m_startCol(startCol) {
-    init();
-  }
+    /** Dynamic-size constructor
+      */
+    inline Block(XprType& xpr,
+          Index startRow, Index startCol,
+          Index blockRows, Index blockCols)
+      : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
+        m_xpr(xpr)
+    {
+      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
+             && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
+      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
+             && startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
+      init();
+    }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const
-      EIGEN_NOEXCEPT {
-    return m_xpr;
-  }
+    /** \sa MapBase::innerStride() */
+    inline Index innerStride() const
+    {
+      return internal::traits<Block>::HasSameStorageOrderAsXprType
+             ? m_xpr.innerStride()
+             : m_xpr.outerStride();
+    }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
+    /** \sa MapBase::outerStride() */
+    inline Index outerStride() const
+    {
+      return m_outerStride;
+    }
 
-  /** \sa MapBase::innerStride() */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index innerStride() const EIGEN_NOEXCEPT {
-    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.innerStride() : m_xpr.outerStride();
-  }
-
-  /** \sa MapBase::outerStride() */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index outerStride() const EIGEN_NOEXCEPT {
-    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR StorageIndex startRow() const EIGEN_NOEXCEPT {
-    return m_startRow.value();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR StorageIndex startCol() const EIGEN_NOEXCEPT {
-    return m_startCol.value();
-  }
-
-#ifndef __SUNPRO_CC
+  #ifndef __SUNPRO_CC
   // FIXME sunstudio is not friendly with the above friend...
   // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
- protected:
-#endif
+  protected:
+  #endif
 
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  /** \internal used by allowAligned() */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows,
-                                                        Index blockCols)
-      : Base(data, blockRows, blockCols), m_xpr(xpr) {
-    init();
-  }
-#endif
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal used by allowAligned() */
+    inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
+      : Base(data, blockRows, blockCols), m_xpr(xpr)
+    {
+      init();
+    }
+    #endif
 
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void init() {
-    m_outerStride =
-        internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
-  }
+  protected:
+    void init()
+    {
+      m_outerStride = internal::traits<Block>::HasSameStorageOrderAsXprType
+                    ? m_xpr.outerStride()
+                    : m_xpr.innerStride();
+    }
 
-  XprTypeNested m_xpr;
-  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
-      m_startRow;
-  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
-      m_startCol;
-  Index m_outerStride;
+    const typename XprType::Nested m_xpr;
+    int m_outerStride;
 };
 
-}  // end namespace internal
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_BLOCK_H
+#endif // EIGEN_BLOCK_H

Eigen/src/Core/BooleanRedux.h

@@ -0,0 +1,149 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_ALLANDANY_H
+#define EIGEN_ALLANDANY_H
+
+namespace internal {
+
+template<typename Derived, int UnrollCount>
+struct all_unroller
+{
+  enum {
+    col = (UnrollCount-1) / Derived::RowsAtCompileTime,
+    row = (UnrollCount-1) % Derived::RowsAtCompileTime
+  };
+
+  inline static bool run(const Derived &mat)
+  {
+    return all_unroller<Derived, UnrollCount-1>::run(mat) && mat.coeff(row, col);
+  }
+};
+
+template<typename Derived>
+struct all_unroller<Derived, 1>
+{
+  inline static bool run(const Derived &mat) { return mat.coeff(0, 0); }
+};
+
+template<typename Derived>
+struct all_unroller<Derived, Dynamic>
+{
+  inline static bool run(const Derived &) { return false; }
+};
+
+template<typename Derived, int UnrollCount>
+struct any_unroller
+{
+  enum {
+    col = (UnrollCount-1) / Derived::RowsAtCompileTime,
+    row = (UnrollCount-1) % Derived::RowsAtCompileTime
+  };
+
+  inline static bool run(const Derived &mat)
+  {
+    return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
+  }
+};
+
+template<typename Derived>
+struct any_unroller<Derived, 1>
+{
+  inline static bool run(const Derived &mat) { return mat.coeff(0, 0); }
+};
+
+template<typename Derived>
+struct any_unroller<Derived, Dynamic>
+{
+  inline static bool run(const Derived &) { return false; }
+};
+
+} // end namespace internal
+
+/** \returns true if all coefficients are true
+  *
+  * Example: \include MatrixBase_all.cpp
+  * Output: \verbinclude MatrixBase_all.out
+  *
+  * \sa any(), Cwise::operator<()
+  */
+template<typename Derived>
+inline bool DenseBase<Derived>::all() const
+{
+  enum {
+    unroll = SizeAtCompileTime != Dynamic
+          && CoeffReadCost != Dynamic
+          && NumTraits<Scalar>::AddCost != Dynamic
+          && SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
+  };
+  if(unroll)
+    return internal::all_unroller<Derived,
+                           unroll ? int(SizeAtCompileTime) : Dynamic
+     >::run(derived());
+  else
+  {
+    for(Index j = 0; j < cols(); ++j)
+      for(Index i = 0; i < rows(); ++i)
+        if (!coeff(i, j)) return false;
+    return true;
+  }
+}
+
+/** \returns true if at least one coefficient is true
+  *
+  * \sa all()
+  */
+template<typename Derived>
+inline bool DenseBase<Derived>::any() const
+{
+  enum {
+    unroll = SizeAtCompileTime != Dynamic
+          && CoeffReadCost != Dynamic
+          && NumTraits<Scalar>::AddCost != Dynamic
+          && SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
+  };
+  if(unroll)
+    return internal::any_unroller<Derived,
+                           unroll ? int(SizeAtCompileTime) : Dynamic
+           >::run(derived());
+  else
+  {
+    for(Index j = 0; j < cols(); ++j)
+      for(Index i = 0; i < rows(); ++i)
+        if (coeff(i, j)) return true;
+    return false;
+  }
+}
+
+/** \returns the number of coefficients which evaluate to true
+  *
+  * \sa all(), any()
+  */
+template<typename Derived>
+inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
+{
+  return derived().template cast<bool>().template cast<Index>().sum();
+}
+
+#endif // EIGEN_ALLANDANY_H

Eigen/src/Core/CMakeLists.txt

@@ -0,0 +1,10 @@
+FILE(GLOB Eigen_Core_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel
+  )
+
+ADD_SUBDIRECTORY(products)
+ADD_SUBDIRECTORY(util)
+ADD_SUBDIRECTORY(arch)

Eigen/src/Core/CommaInitializer.h

@@ -4,146 +4,147 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_COMMAINITIALIZER_H
 #define EIGEN_COMMAINITIALIZER_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 /** \class CommaInitializer
- * \ingroup Core_Module
- *
- * \brief Helper class used by the comma initializer operator
- *
- * This class is internally used to implement the comma initializer feature. It is
- * the return type of MatrixBase::operator<<, and most of the time this is the only
- * way it is used.
- *
- * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
- */
-template <typename XprType>
-struct CommaInitializer {
+  * \ingroup Core_Module
+  *
+  * \brief Helper class used by the comma initializer operator
+  *
+  * This class is internally used to implement the comma initializer feature. It is
+  * the return type of MatrixBase::operator<<, and most of the time this is the only
+  * way it is used.
+  *
+  * \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
+  */
+template<typename XprType>
+struct CommaInitializer
+{
   typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::Index Index;
 
-  EIGEN_DEVICE_FUNC inline CommaInitializer(XprType& xpr, const Scalar& s)
-      : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) {
-    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0 && "Cannot comma-initialize a 0x0 matrix (operator<<)");
-    m_xpr.coeffRef(0, 0) = s;
+  inline CommaInitializer(XprType& xpr, const Scalar& s)
+    : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
+  {
+    m_xpr.coeffRef(0,0) = s;
   }
 
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
-      : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) {
-    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols() &&
-                 "Cannot comma-initialize a 0x0 matrix (operator<<)");
-    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(0, 0, other.rows(),
-                                                                                           other.cols()) = other;
-  }
-
-  /* Copy/Move constructor which transfers ownership. This is crucial in
-   * absence of return value optimization to avoid assertions during destruction. */
-  // FIXME in C++11 mode this could be replaced by a proper RValue constructor
-  EIGEN_DEVICE_FUNC inline CommaInitializer(const CommaInitializer& o)
-      : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
-    // Mark original object as finished. In absence of R-value references we need to const_cast:
-    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
-    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
-    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
+  template<typename OtherDerived>
+  inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
+    : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
+  {
+    m_xpr.block(0, 0, other.rows(), other.cols()) = other;
   }
 
   /* inserts a scalar value in the target matrix */
-  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const Scalar& s) {
-    if (m_col == m_xpr.cols()) {
-      m_row += m_currentBlockRows;
+  CommaInitializer& operator,(const Scalar& s)
+  {
+    if (m_col==m_xpr.cols())
+    {
+      m_row+=m_currentBlockRows;
       m_col = 0;
       m_currentBlockRows = 1;
-      eigen_assert(m_row < m_xpr.rows() && "Too many rows passed to comma initializer (operator<<)");
+      eigen_assert(m_row<m_xpr.rows()
+        && "Too many rows passed to comma initializer (operator<<)");
     }
-    eigen_assert(m_col < m_xpr.cols() && "Too many coefficients passed to comma initializer (operator<<)");
-    eigen_assert(m_currentBlockRows == 1);
+    eigen_assert(m_col<m_xpr.cols()
+      && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows==1);
     m_xpr.coeffRef(m_row, m_col++) = s;
     return *this;
   }
 
   /* inserts a matrix expression in the target matrix */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const DenseBase<OtherDerived>& other) {
-    if (m_col == m_xpr.cols() && (other.cols() != 0 || other.rows() != m_currentBlockRows)) {
-      m_row += m_currentBlockRows;
+  template<typename OtherDerived>
+  CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
+  {
+    if (m_col==m_xpr.cols())
+    {
+      m_row+=m_currentBlockRows;
       m_col = 0;
       m_currentBlockRows = other.rows();
-      eigen_assert(m_row + m_currentBlockRows <= m_xpr.rows() &&
-                   "Too many rows passed to comma initializer (operator<<)");
+      eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
+        && "Too many rows passed to comma initializer (operator<<)");
     }
-    eigen_assert((m_col + other.cols() <= m_xpr.cols()) &&
-                 "Too many coefficients passed to comma initializer (operator<<)");
-    eigen_assert(m_currentBlockRows == other.rows());
-    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(m_row, m_col, other.rows(),
-                                                                                           other.cols()) = other;
+    eigen_assert(m_col<m_xpr.cols()
+      && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows==other.rows());
+    if (OtherDerived::SizeAtCompileTime != Dynamic)
+      m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
+                              OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1>
+                    (m_row, m_col) = other;
+    else
+      m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other;
     m_col += other.cols();
     return *this;
   }
 
-  EIGEN_DEVICE_FUNC inline ~CommaInitializer()
-#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
-      EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
-#endif
+  inline ~CommaInitializer()
   {
-    finished();
+    eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
+         && m_col == m_xpr.cols()
+         && "Too few coefficients passed to comma initializer (operator<<)");
   }
 
   /** \returns the built matrix once all its coefficients have been set.
-   * Calling finished is 100% optional. Its purpose is to write expressions
-   * like this:
-   * \code
-   * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
-   * \endcode
-   */
-  EIGEN_DEVICE_FUNC inline XprType& finished() {
-    eigen_assert(((m_row + m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0) && m_col == m_xpr.cols() &&
-                 "Too few coefficients passed to comma initializer (operator<<)");
-    return m_xpr;
-  }
+    * Calling finished is 100% optional. Its purpose is to write expressions
+    * like this:
+    * \code
+    * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
+    * \endcode
+    */
+  inline XprType& finished() { return m_xpr; }
 
-  XprType& m_xpr;            // target expression
-  Index m_row;               // current row id
-  Index m_col;               // current col id
-  Index m_currentBlockRows;  // current block height
+  XprType& m_xpr;   // target expression
+  Index m_row;              // current row id
+  Index m_col;              // current col id
+  Index m_currentBlockRows; // current block height
 };
 
 /** \anchor MatrixBaseCommaInitRef
- * Convenient operator to set the coefficients of a matrix.
- *
- * The coefficients must be provided in a row major order and exactly match
- * the size of the matrix. Otherwise an assertion is raised.
- *
- * Example: \include MatrixBase_set.cpp
- * Output: \verbinclude MatrixBase_set.out
- *
- * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary
- * order.
- *
- * \sa CommaInitializer::finished(), class CommaInitializer
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(const Scalar& s) {
+  * Convenient operator to set the coefficients of a matrix.
+  *
+  * The coefficients must be provided in a row major order and exactly match
+  * the size of the matrix. Otherwise an assertion is raised.
+  *
+  * Example: \include MatrixBase_set.cpp
+  * Output: \verbinclude MatrixBase_set.out
+  *
+  * \sa CommaInitializer::finished(), class CommaInitializer
+  */
+template<typename Derived>
+inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
+{
   return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
 }
 
 /** \sa operator<<(const Scalar&) */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(
-    const DenseBase<OtherDerived>& other) {
-  return CommaInitializer<Derived>(*static_cast<Derived*>(this), other);
+template<typename Derived>
+template<typename OtherDerived>
+inline CommaInitializer<Derived>
+DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
+{
+  return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_COMMAINITIALIZER_H
+#endif // EIGEN_COMMAINITIALIZER_H

Eigen/src/Core/ConditionEstimator.h

@@ -1,173 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONDITIONESTIMATOR_H
-#define EIGEN_CONDITIONESTIMATOR_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Vector, typename RealVector, bool IsComplex>
-struct rcond_compute_sign {
-  static inline Vector run(const Vector& v) {
-    const RealVector v_abs = v.cwiseAbs();
-    return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
-        .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
-  }
-};
-
-// Partial specialization to avoid elementwise division for real vectors.
-template <typename Vector>
-struct rcond_compute_sign<Vector, Vector, false> {
-  static inline Vector run(const Vector& v) {
-    return (v.array() < static_cast<typename Vector::RealScalar>(0))
-        .select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
-  }
-};
-
-/**
- * \returns an estimate of ||inv(matrix)||_1 given a decomposition of
- * \a matrix that implements .solve() and .adjoint().solve() methods.
- *
- * This function implements Algorithms 4.1 and 5.1 from
- *   http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
- * which also forms the basis for the condition number estimators in
- * LAPACK. Since at most 10 calls to the solve method of dec are
- * performed, the total cost is O(dims^2), as opposed to O(dims^3)
- * needed to compute the inverse matrix explicitly.
- *
- * The most common usage is in estimating the condition number
- * ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
- * computed directly in O(n^2) operations.
- *
- * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
- * LLT.
- *
- * \sa FullPivLU, PartialPivLU, LDLT, LLT.
- */
-template <typename Decomposition>
-typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec) {
-  typedef typename Decomposition::MatrixType MatrixType;
-  typedef typename Decomposition::Scalar Scalar;
-  typedef typename Decomposition::RealScalar RealScalar;
-  typedef typename internal::plain_col_type<MatrixType>::type Vector;
-  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
-  const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
-
-  eigen_assert(dec.rows() == dec.cols());
-  const Index n = dec.rows();
-  if (n == 0) return 0;
-
-    // Disable Index to float conversion warning
-#ifdef __INTEL_COMPILER
-#pragma warning push
-#pragma warning(disable : 2259)
-#endif
-  Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
-#ifdef __INTEL_COMPILER
-#pragma warning pop
-#endif
-
-  // lower_bound is a lower bound on
-  //   ||inv(matrix)||_1  = sup_v ||inv(matrix) v||_1 / ||v||_1
-  // and is the objective maximized by the ("super-") gradient ascent
-  // algorithm below.
-  RealScalar lower_bound = v.template lpNorm<1>();
-  if (n == 1) return lower_bound;
-
-  // Gradient ascent algorithm follows: We know that the optimum is achieved at
-  // one of the simplices v = e_i, so in each iteration we follow a
-  // super-gradient to move towards the optimal one.
-  RealScalar old_lower_bound = lower_bound;
-  Vector sign_vector(n);
-  Vector old_sign_vector;
-  Index v_max_abs_index = -1;
-  Index old_v_max_abs_index = v_max_abs_index;
-  for (int k = 0; k < 4; ++k) {
-    sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
-    if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
-      // Break if the solution stagnated.
-      break;
-    }
-    // v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
-    v = dec.adjoint().solve(sign_vector);
-    v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
-    if (v_max_abs_index == old_v_max_abs_index) {
-      // Break if the solution stagnated.
-      break;
-    }
-    // Move to the new simplex e_j, where j = v_max_abs_index.
-    v = dec.solve(Vector::Unit(n, v_max_abs_index));  // v = inv(matrix) * e_j.
-    lower_bound = v.template lpNorm<1>();
-    if (lower_bound <= old_lower_bound) {
-      // Break if the gradient step did not increase the lower_bound.
-      break;
-    }
-    if (!is_complex) {
-      old_sign_vector = sign_vector;
-    }
-    old_v_max_abs_index = v_max_abs_index;
-    old_lower_bound = lower_bound;
-  }
-  // The following calculates an independent estimate of ||matrix||_1 by
-  // multiplying matrix by a vector with entries of slowly increasing
-  // magnitude and alternating sign:
-  //   v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
-  // This improvement to Hager's algorithm above is due to Higham. It was
-  // added to make the algorithm more robust in certain corner cases where
-  // large elements in the matrix might otherwise escape detection due to
-  // exact cancellation (especially when op and op_adjoint correspond to a
-  // sequence of backsubstitutions and permutations), which could cause
-  // Hager's algorithm to vastly underestimate ||matrix||_1.
-  Scalar alternating_sign(RealScalar(1));
-  for (Index i = 0; i < n; ++i) {
-    // The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
-    v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
-    alternating_sign = -alternating_sign;
-  }
-  v = dec.solve(v);
-  const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
-  return numext::maxi(lower_bound, alternate_lower_bound);
-}
-
-/** \brief Reciprocal condition number estimator.
- *
- * Computing a decomposition of a dense matrix takes O(n^3) operations, while
- * this method estimates the condition number quickly and reliably in O(n^2)
- * operations.
- *
- * \returns an estimate of the reciprocal condition number
- * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
- * its decomposition. Supports the following decompositions: FullPivLU,
- * PartialPivLU, LDLT, and LLT.
- *
- * \sa FullPivLU, PartialPivLU, LDLT, LLT.
- */
-template <typename Decomposition>
-typename Decomposition::RealScalar rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm,
-                                                         const Decomposition& dec) {
-  typedef typename Decomposition::RealScalar RealScalar;
-  eigen_assert(dec.rows() == dec.cols());
-  if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
-  if (numext::is_exactly_zero(matrix_norm)) return RealScalar(0);
-  if (dec.rows() == 1) return RealScalar(1);
-  const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
-  return (numext::is_exactly_zero(inverse_matrix_norm) ? RealScalar(0)
-                                                       : (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
-}
-
-}  // namespace internal
-
-}  // namespace Eigen
-
-#endif

Eigen/src/Core/CoreIterators.h

@@ -1,141 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COREITERATORS_H
-#define EIGEN_COREITERATORS_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
- */
-
-namespace internal {
-
-template <typename XprType, typename EvaluatorKind>
-class inner_iterator_selector;
-
-}
-
-/** \class InnerIterator
- * \brief An InnerIterator allows to loop over the element of any matrix expression.
- *
- * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is
- * constructed.
- *
- * TODO: add a usage example
- */
-template <typename XprType>
-class InnerIterator {
- protected:
-  typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
-  typedef internal::evaluator<XprType> EvaluatorType;
-  typedef typename internal::traits<XprType>::Scalar Scalar;
-
- public:
-  /** Construct an iterator over the \a outerId -th row or column of \a xpr */
-  InnerIterator(const XprType &xpr, const Index &outerId) : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize()) {}
-
-  /// \returns the value of the current coefficient.
-  EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
-  /** Increment the iterator \c *this to the next non-zero coefficient.
-   * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
-   */
-  EIGEN_STRONG_INLINE InnerIterator &operator++() {
-    m_iter.operator++();
-    return *this;
-  }
-  EIGEN_STRONG_INLINE InnerIterator &operator+=(Index i) {
-    m_iter.operator+=(i);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE InnerIterator operator+(Index i) {
-    InnerIterator result(*this);
-    result += i;
-    return result;
-  }
-
-  /// \returns the column or row index of the current coefficient.
-  EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
-  /// \returns the row index of the current coefficient.
-  EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
-  /// \returns the column index of the current coefficient.
-  EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
-  /// \returns \c true if the iterator \c *this still references a valid coefficient.
-  EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
-
- protected:
-  EvaluatorType m_eval;
-  IteratorType m_iter;
-
- private:
-  // If you get here, then you're not using the right InnerIterator type, e.g.:
-  //   SparseMatrix<double,RowMajor> A;
-  //   SparseMatrix<double>::InnerIterator it(A,0);
-  template <typename T>
-  InnerIterator(const EigenBase<T> &, Index outer);
-};
-
-namespace internal {
-
-// Generic inner iterator implementation for dense objects
-template <typename XprType>
-class inner_iterator_selector<XprType, IndexBased> {
- protected:
-  typedef evaluator<XprType> EvaluatorType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  enum { IsRowMajor = (XprType::Flags & RowMajorBit) == RowMajorBit };
-
- public:
-  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
-      : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize) {}
-
-  EIGEN_STRONG_INLINE Scalar value() const {
-    return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner) : m_eval.coeff(m_inner, m_outer);
-  }
-
-  EIGEN_STRONG_INLINE inner_iterator_selector &operator++() {
-    m_inner++;
-    return *this;
-  }
-
-  EIGEN_STRONG_INLINE Index index() const { return m_inner; }
-  inline Index row() const { return IsRowMajor ? m_outer : index(); }
-  inline Index col() const { return IsRowMajor ? index() : m_outer; }
-
-  EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner >= 0; }
-
- protected:
-  const EvaluatorType &m_eval;
-  Index m_inner;
-  const Index m_outer;
-  const Index m_end;
-};
-
-// For iterator-based evaluator, inner-iterator is already implemented as
-// evaluator<>::InnerIterator
-template <typename XprType>
-class inner_iterator_selector<XprType, IteratorBased> : public evaluator<XprType>::InnerIterator {
- protected:
-  typedef typename evaluator<XprType>::InnerIterator Base;
-  typedef evaluator<XprType> EvaluatorType;
-
- public:
-  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId,
-                                              const Index & /*innerSize*/)
-      : Base(eval, outerId) {}
-};
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_COREITERATORS_H

Eigen/src/Core/CwiseBinaryOp.h

@@ -1,27 +1,58 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_CWISE_BINARY_OP_H
 #define EIGEN_CWISE_BINARY_OP_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
+/** \class CwiseBinaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
+  *
+  * \param BinaryOp template functor implementing the operator
+  * \param Lhs the type of the left-hand side
+  * \param Rhs the type of the right-hand side
+  *
+  * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
+  * It is the return type of binary operators, by which we mean only those binary operators where
+  * both the left-hand side and the right-hand side are Eigen expressions.
+  * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
+  *
+  * Most of the time, this is the only way that it is used, so you typically don't have to name
+  * CwiseBinaryOp types explicitly.
+  *
+  * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
+  */
 
 namespace internal {
-template <typename BinaryOp, typename Lhs, typename Rhs>
-struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
   // we must not inherit from traits<Lhs> since it has
   // the potential to cause problems with MSVC
-  typedef remove_all_t<Lhs> Ancestor;
+  typedef typename remove_all<Lhs>::type Ancestor;
   typedef typename traits<Ancestor>::XprKind XprKind;
   enum {
     RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
@@ -32,135 +63,178 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs>> {
 
   // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
   // we still want to handle the case when the result type is different.
-  typedef typename result_of<BinaryOp(const typename Lhs::Scalar&, const typename Rhs::Scalar&)>::type Scalar;
-  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
-                                              BinaryOp>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex, typename traits<Rhs>::StorageIndex>::type
-      StorageIndex;
+  typedef typename result_of<
+                     BinaryOp(
+                       typename Lhs::Scalar,
+                       typename Rhs::Scalar
+                     )
+                   >::type Scalar;
+  typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
+                                           typename traits<Rhs>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<Lhs>::Index,
+                                         typename traits<Rhs>::Index>::type Index;
   typedef typename Lhs::Nested LhsNested;
   typedef typename Rhs::Nested RhsNested;
-  typedef std::remove_reference_t<LhsNested> LhsNested_;
-  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
   enum {
-    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind,
-                                        LhsNested_::Flags & RowMajorBit, RhsNested_::Flags & RowMajorBit>::value
+    LhsCoeffReadCost = _LhsNested::CoeffReadCost,
+    RhsCoeffReadCost = _RhsNested::CoeffReadCost,
+    LhsFlags = _LhsNested::Flags,
+    RhsFlags = _RhsNested::Flags,
+    SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value,
+    StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit),
+    Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
+        HereditaryBits
+      | (int(LhsFlags) & int(RhsFlags) &
+           ( AlignedBit
+           | (StorageOrdersAgree ? LinearAccessBit : 0)
+           | (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
+           )
+        )
+     ),
+    Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
+    CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits<BinaryOp>::Cost
   };
 };
-}  // end namespace internal
+} // end namespace internal
 
-template <typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
+// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
+// that would take two operands of different types. If there were such an example, then this check should be
+// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
+// currently they take only one typename Scalar template parameter.
+// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
+// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
+// add together a float matrix and a double matrix.
+#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
+  EIGEN_STATIC_ASSERT((internal::functor_allows_mixing_real_and_complex<BINOP>::ret \
+                        ? int(internal::is_same<typename NumTraits<LHS>::Real, typename NumTraits<RHS>::Real>::value) \
+                        : int(internal::is_same<LHS, RHS>::value)), \
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
 class CwiseBinaryOpImpl;
 
-/** \class CwiseBinaryOp
- * \ingroup Core_Module
- *
- * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
- *
- * \tparam BinaryOp template functor implementing the operator
- * \tparam LhsType the type of the left-hand side
- * \tparam RhsType the type of the right-hand side
- *
- * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
- * It is the return type of binary operators, by which we mean only those binary operators where
- * both the left-hand side and the right-hand side are Eigen expressions.
- * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
- *
- * Most of the time, this is the only way that it is used, so you typically don't have to name
- * CwiseBinaryOp types explicitly.
- *
- * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class
- * CwiseNullaryOp
- */
-template <typename BinaryOp, typename LhsType, typename RhsType>
-class CwiseBinaryOp : public CwiseBinaryOpImpl<BinaryOp, LhsType, RhsType,
-                                               typename internal::cwise_promote_storage_type<
-                                                   typename internal::traits<LhsType>::StorageKind,
-                                                   typename internal::traits<RhsType>::StorageKind, BinaryOp>::ret>,
-                      internal::no_assignment_operator {
- public:
-  typedef internal::remove_all_t<BinaryOp> Functor;
-  typedef internal::remove_all_t<LhsType> Lhs;
-  typedef internal::remove_all_t<RhsType> Rhs;
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOp : internal::no_assignment_operator,
+  public CwiseBinaryOpImpl<
+          BinaryOp, Lhs, Rhs,
+          typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
+                                           typename internal::traits<Rhs>::StorageKind>::ret>
+{
+  public:
 
-  typedef typename CwiseBinaryOpImpl<
-      BinaryOp, LhsType, RhsType,
-      typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
-                                                    typename internal::traits<Rhs>::StorageKind, BinaryOp>::ret>::Base
-      Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
+    typedef typename CwiseBinaryOpImpl<
+        BinaryOp, Lhs, Rhs,
+        typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
+                                         typename internal::traits<Rhs>::StorageKind>::ret>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
 
-  EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp, typename Lhs::Scalar, typename Rhs::Scalar)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
+    typedef typename internal::nested<Lhs>::type LhsNested;
+    typedef typename internal::nested<Rhs>::type RhsNested;
+    typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
+    typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
 
-  typedef typename internal::ref_selector<LhsType>::type LhsNested;
-  typedef typename internal::ref_selector<RhsType>::type RhsNested;
-  typedef std::remove_reference_t<LhsNested> LhsNested_;
-  typedef std::remove_reference_t<RhsNested> RhsNested_;
+    EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
+      : m_lhs(lhs), m_rhs(rhs), m_functor(func)
+    {
+      EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
+      // require the sizes to match
+      EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
+      eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
+    }
 
-#if EIGEN_COMP_MSVC
-  // Required for Visual Studio or the Copy constructor will probably not get inlined!
-  EIGEN_STRONG_INLINE CwiseBinaryOp(const CwiseBinaryOp<BinaryOp, LhsType, RhsType>&) = default;
-#endif
+    EIGEN_STRONG_INLINE Index rows() const {
+      // return the fixed size type if available to enable compile time optimizations
+      if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
+        return m_rhs.rows();
+      else
+        return m_lhs.rows();
+    }
+    EIGEN_STRONG_INLINE Index cols() const {
+      // return the fixed size type if available to enable compile time optimizations
+      if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic)
+        return m_rhs.cols();
+      else
+        return m_lhs.cols();
+    }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs,
-                                                      const BinaryOp& func = BinaryOp())
-      : m_lhs(aLhs), m_rhs(aRhs), m_functor(func) {
-    eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
-  }
+    /** \returns the left hand side nested expression */
+    const _LhsNested& lhs() const { return m_lhs; }
+    /** \returns the right hand side nested expression */
+    const _RhsNested& rhs() const { return m_rhs; }
+    /** \returns the functor representing the binary operation */
+    const BinaryOp& functor() const { return m_functor; }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT {
-    // return the fixed size type if available to enable compile time optimizations
-    return internal::traits<internal::remove_all_t<LhsNested>>::RowsAtCompileTime == Dynamic ? m_rhs.rows()
-                                                                                             : m_lhs.rows();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT {
-    // return the fixed size type if available to enable compile time optimizations
-    return internal::traits<internal::remove_all_t<LhsNested>>::ColsAtCompileTime == Dynamic ? m_rhs.cols()
-                                                                                             : m_lhs.cols();
-  }
-
-  /** \returns the left hand side nested expression */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const LhsNested_& lhs() const { return m_lhs; }
-  /** \returns the right hand side nested expression */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const RhsNested_& rhs() const { return m_rhs; }
-  /** \returns the functor representing the binary operation */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const BinaryOp& functor() const { return m_functor; }
-
- protected:
-  LhsNested m_lhs;
-  RhsNested m_rhs;
-  const BinaryOp m_functor;
+  protected:
+    const LhsNested m_lhs;
+    const RhsNested m_rhs;
+    const BinaryOp m_functor;
 };
 
-// Generic API dispatcher
-template <typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
-class CwiseBinaryOpImpl : public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>>::type {
- public:
-  typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs>>::type Base;
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense>
+  : public internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
+{
+    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
+  public:
+
+    typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
+
+    EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
+    {
+      return derived().functor()(derived().lhs().coeff(row, col),
+                                 derived().rhs().coeff(row, col));
+    }
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
+    {
+      return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(row, col),
+                                          derived().rhs().template packet<LoadMode>(row, col));
+    }
+
+    EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
+    {
+      return derived().functor()(derived().lhs().coeff(index),
+                                 derived().rhs().coeff(index));
+    }
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
+    {
+      return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index),
+                                          derived().rhs().template packet<LoadMode>(index));
+    }
 };
 
 /** replaces \c *this by \c *this - \a other.
- *
- * \returns a reference to \c *this
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
+{
+  SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
+  tmp = other.derived();
   return derived();
 }
 
 /** replaces \c *this by \c *this + \a other.
- *
- * \returns a reference to \c *this
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
+{
+  SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
+  tmp = other.derived();
   return derived();
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_CWISE_BINARY_OP_H
+#endif // EIGEN_CWISE_BINARY_OP_H

Eigen/src/Core/CwiseTernaryOp.h

@@ -1,171 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_TERNARY_OP_H
-#define EIGEN_CWISE_TERNARY_OP_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-namespace internal {
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>> {
-  // we must not inherit from traits<Arg1> since it has
-  // the potential to cause problems with MSVC
-  typedef remove_all_t<Arg1> Ancestor;
-  typedef typename traits<Ancestor>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
-  };
-
-  // even though we require Arg1, Arg2, and Arg3 to have the same scalar type
-  // (see CwiseTernaryOp constructor),
-  // we still want to handle the case when the result type is different.
-  typedef typename result_of<TernaryOp(const typename Arg1::Scalar&, const typename Arg2::Scalar&,
-                                       const typename Arg3::Scalar&)>::type Scalar;
-
-  typedef typename internal::traits<Arg1>::StorageKind StorageKind;
-  typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
-
-  typedef typename Arg1::Nested Arg1Nested;
-  typedef typename Arg2::Nested Arg2Nested;
-  typedef typename Arg3::Nested Arg3Nested;
-  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
-  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
-  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
-  enum { Flags = Arg1Nested_::Flags & RowMajorBit };
-};
-}  // end namespace internal
-
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3, typename StorageKind>
-class CwiseTernaryOpImpl;
-
-/** \class CwiseTernaryOp
- * \ingroup Core_Module
- *
- * \brief Generic expression where a coefficient-wise ternary operator is
- * applied to two expressions
- *
- * \tparam TernaryOp template functor implementing the operator
- * \tparam Arg1Type the type of the first argument
- * \tparam Arg2Type the type of the second argument
- * \tparam Arg3Type the type of the third argument
- *
- * This class represents an expression where a coefficient-wise ternary
- * operator is applied to three expressions.
- * It is the return type of ternary operators, by which we mean only those
- * ternary operators where
- * all three arguments are Eigen expressions.
- * For example, the return type of betainc(matrix1, matrix2, matrix3) is a
- * CwiseTernaryOp.
- *
- * Most of the time, this is the only way that it is used, so you typically
- * don't have to name
- * CwiseTernaryOp types explicitly.
- *
- * \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
- * MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
- * class CwiseUnaryOp, class CwiseNullaryOp
- */
-template <typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type>
-class CwiseTernaryOp : public CwiseTernaryOpImpl<TernaryOp, Arg1Type, Arg2Type, Arg3Type,
-                                                 typename internal::traits<Arg1Type>::StorageKind>,
-                       internal::no_assignment_operator {
- public:
-  typedef internal::remove_all_t<Arg1Type> Arg1;
-  typedef internal::remove_all_t<Arg2Type> Arg2;
-  typedef internal::remove_all_t<Arg3Type> Arg3;
-
-  // require the sizes to match
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
-
-  // The index types should match
-  EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
-                                         typename internal::traits<Arg2Type>::StorageKind>::value),
-                      STORAGE_KIND_MUST_MATCH)
-  EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
-                                         typename internal::traits<Arg3Type>::StorageKind>::value),
-                      STORAGE_KIND_MUST_MATCH)
-
-  typedef typename CwiseTernaryOpImpl<TernaryOp, Arg1Type, Arg2Type, Arg3Type,
-                                      typename internal::traits<Arg1Type>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
-
-  typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
-  typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
-  typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
-  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
-  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
-  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2, const Arg3& a3,
-                                                       const TernaryOp& func = TernaryOp())
-      : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
-    eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() && a1.rows() == a3.rows() && a1.cols() == a3.cols());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const {
-    // return the fixed size type if available to enable compile time
-    // optimizations
-    if (internal::traits<internal::remove_all_t<Arg1Nested>>::RowsAtCompileTime == Dynamic &&
-        internal::traits<internal::remove_all_t<Arg2Nested>>::RowsAtCompileTime == Dynamic)
-      return m_arg3.rows();
-    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::RowsAtCompileTime == Dynamic &&
-             internal::traits<internal::remove_all_t<Arg3Nested>>::RowsAtCompileTime == Dynamic)
-      return m_arg2.rows();
-    else
-      return m_arg1.rows();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const {
-    // return the fixed size type if available to enable compile time
-    // optimizations
-    if (internal::traits<internal::remove_all_t<Arg1Nested>>::ColsAtCompileTime == Dynamic &&
-        internal::traits<internal::remove_all_t<Arg2Nested>>::ColsAtCompileTime == Dynamic)
-      return m_arg3.cols();
-    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::ColsAtCompileTime == Dynamic &&
-             internal::traits<internal::remove_all_t<Arg3Nested>>::ColsAtCompileTime == Dynamic)
-      return m_arg2.cols();
-    else
-      return m_arg1.cols();
-  }
-
-  /** \returns the first argument nested expression */
-  EIGEN_DEVICE_FUNC const Arg1Nested_& arg1() const { return m_arg1; }
-  /** \returns the first argument nested expression */
-  EIGEN_DEVICE_FUNC const Arg2Nested_& arg2() const { return m_arg2; }
-  /** \returns the third argument nested expression */
-  EIGEN_DEVICE_FUNC const Arg3Nested_& arg3() const { return m_arg3; }
-  /** \returns the functor representing the ternary operation */
-  EIGEN_DEVICE_FUNC const TernaryOp& functor() const { return m_functor; }
-
- protected:
-  Arg1Nested m_arg1;
-  Arg2Nested m_arg2;
-  Arg3Nested m_arg3;
-  const TernaryOp m_functor;
-};
-
-// Generic API dispatcher
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3, typename StorageKind>
-class CwiseTernaryOpImpl : public internal::generic_xpr_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>::type {
- public:
-  typedef typename internal::generic_xpr_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>>::type Base;
-};
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CWISE_TERNARY_OP_H

Eigen/src/Core/CwiseUnaryOp.h

@@ -1,91 +1,137 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_CWISE_UNARY_OP_H
 #define EIGEN_CWISE_UNARY_OP_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
+/** \class CwiseUnaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
+  *
+  * \param UnaryOp template functor implementing the operator
+  * \param XprType the type of the expression to which we are applying the unary operator
+  *
+  * This class represents an expression where a unary operator is applied to an expression.
+  * It is the return type of all operations taking exactly 1 input expression, regardless of the
+  * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
+  * is considered unary, because only the right-hand side is an expression, and its
+  * return type is a specialization of CwiseUnaryOp.
+  *
+  * Most of the time, this is the only way that it is used, so you typically don't have to name
+  * CwiseUnaryOp types explicitly.
+  *
+  * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
+  */
 
 namespace internal {
-template <typename UnaryOp, typename XprType>
-struct traits<CwiseUnaryOp<UnaryOp, XprType> > : traits<XprType> {
-  typedef typename result_of<UnaryOp(const typename XprType::Scalar&)>::type Scalar;
+template<typename UnaryOp, typename XprType>
+struct traits<CwiseUnaryOp<UnaryOp, XprType> >
+ : traits<XprType>
+{
+  typedef typename result_of<
+                     UnaryOp(typename XprType::Scalar)
+                   >::type Scalar;
   typedef typename XprType::Nested XprTypeNested;
-  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
-  enum { Flags = XprTypeNested_::Flags & RowMajorBit };
+  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
+  enum {
+    Flags = _XprTypeNested::Flags & (
+      HereditaryBits | LinearAccessBit | AlignedBit
+      | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
+    CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost
+  };
 };
-}  // namespace internal
+}
 
-template <typename UnaryOp, typename XprType, typename StorageKind>
+template<typename UnaryOp, typename XprType, typename StorageKind>
 class CwiseUnaryOpImpl;
 
-/** \class CwiseUnaryOp
- * \ingroup Core_Module
- *
- * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
- *
- * \tparam UnaryOp template functor implementing the operator
- * \tparam XprType the type of the expression to which we are applying the unary operator
- *
- * This class represents an expression where a unary operator is applied to an expression.
- * It is the return type of all operations taking exactly 1 input expression, regardless of the
- * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
- * is considered unary, because only the right-hand side is an expression, and its
- * return type is a specialization of CwiseUnaryOp.
- *
- * Most of the time, this is the only way that it is used, so you typically don't have to name
- * CwiseUnaryOp types explicitly.
- *
- * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
- */
-template <typename UnaryOp, typename XprType>
-class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>,
-                     internal::no_assignment_operator {
- public:
-  typedef typename CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
-  typedef typename internal::ref_selector<XprType>::type XprTypeNested;
-  typedef internal::remove_all_t<XprType> NestedExpression;
+template<typename UnaryOp, typename XprType>
+class CwiseUnaryOp : internal::no_assignment_operator,
+  public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>
+{
+  public:
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
+    typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
+
+    inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
       : m_xpr(xpr), m_functor(func) {}
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
+    EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); }
 
-  /** \returns the functor representing the unary operation */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const UnaryOp& functor() const { return m_functor; }
+    /** \returns the functor representing the unary operation */
+    const UnaryOp& functor() const { return m_functor; }
 
-  /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const {
-    return m_xpr;
-  }
+    /** \returns the nested expression */
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    nestedExpression() const { return m_xpr; }
 
-  /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::remove_all_t<XprTypeNested>& nestedExpression() { return m_xpr; }
+    /** \returns the nested expression */
+    typename internal::remove_all<typename XprType::Nested>::type&
+    nestedExpression() { return m_xpr.const_cast_derived(); }
 
- protected:
-  XprTypeNested m_xpr;
-  const UnaryOp m_functor;
+  protected:
+    const typename XprType::Nested m_xpr;
+    const UnaryOp m_functor;
 };
 
-// Generic API dispatcher
-template <typename UnaryOp, typename XprType, typename StorageKind>
-class CwiseUnaryOpImpl : public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type {
- public:
-  typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
+// This is the generic implementation for dense storage.
+// It can be used for any expression types implementing the dense concept.
+template<typename UnaryOp, typename XprType>
+class CwiseUnaryOpImpl<UnaryOp,XprType,Dense>
+  : public internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
+{
+  public:
+
+    typedef CwiseUnaryOp<UnaryOp, XprType> Derived;
+    typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+
+    EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
+    {
+      return derived().functor()(derived().nestedExpression().coeff(row, col));
+    }
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
+    {
+      return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(row, col));
+    }
+
+    EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
+    {
+      return derived().functor()(derived().nestedExpression().coeff(index));
+    }
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
+    {
+      return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index));
+    }
 };
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_CWISE_UNARY_OP_H
+#endif // EIGEN_CWISE_UNARY_OP_H

Eigen/src/Core/CwiseUnaryView.h

@@ -3,165 +3,146 @@
 //
 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_CWISE_UNARY_VIEW_H
 #define EIGEN_CWISE_UNARY_VIEW_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
+/** \class CwiseUnaryView
+  * \ingroup Core_Module
+  *
+  * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
+  *
+  * \param ViewOp template functor implementing the view
+  * \param MatrixType the type of the matrix we are applying the unary operator
+  *
+  * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
+  * It is the return type of real() and imag(), and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
+  */
 
 namespace internal {
-template <typename ViewOp, typename MatrixType, typename StrideType>
-struct traits<CwiseUnaryView<ViewOp, MatrixType, StrideType> > : traits<MatrixType> {
-  typedef typename result_of<ViewOp(typename traits<MatrixType>::Scalar&)>::type1 ScalarRef;
-  static_assert(std::is_reference<ScalarRef>::value, "Views must return a reference type.");
-  typedef remove_all_t<ScalarRef> Scalar;
+template<typename ViewOp, typename MatrixType>
+struct traits<CwiseUnaryView<ViewOp, MatrixType> >
+ : traits<MatrixType>
+{
+  typedef typename result_of<
+                     ViewOp(typename traits<MatrixType>::Scalar)
+                   >::type Scalar;
   typedef typename MatrixType::Nested MatrixTypeNested;
-  typedef remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+  typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
   enum {
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags =
-        traits<MatrixTypeNested_>::Flags &
-        (RowMajorBit | FlagsLvalueBit | DirectAccessBit),  // FIXME DirectAccessBit should not be handled by expressions
-    MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
+    Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)),
+    CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost,
+    MatrixTypeInnerStride =  inner_stride_at_compile_time<MatrixType>::ret,
     // need to cast the sizeof's from size_t to int explicitly, otherwise:
     // "error: no integral type can represent all of the enumerator values
-    InnerStrideAtCompileTime =
-        StrideType::InnerStrideAtCompileTime == 0
-            ? (MatrixTypeInnerStride == Dynamic
-                   ? int(Dynamic)
-                   : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
-            : int(StrideType::InnerStrideAtCompileTime),
-
-    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
-                                   ? (outer_stride_at_compile_time<MatrixType>::ret == Dynamic
-                                          ? int(Dynamic)
-                                          : outer_stride_at_compile_time<MatrixType>::ret *
-                                                int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
-                                   : int(StrideType::OuterStrideAtCompileTime)
+    InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
+                             ? int(Dynamic)
+                             : int(MatrixTypeInnerStride)
+                               * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
+    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
   };
 };
+}
 
-// Generic API dispatcher
-template <typename ViewOp, typename XprType, typename StrideType, typename StorageKind,
-          bool Mutable = !std::is_const<XprType>::value>
-class CwiseUnaryViewImpl : public generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type {
- public:
-  typedef typename generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type Base;
-};
+template<typename ViewOp, typename MatrixType, typename StorageKind>
+class CwiseUnaryViewImpl;
 
-template <typename ViewOp, typename MatrixType, typename StrideType>
-class CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false>
-    : public dense_xpr_base<CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type {
- public:
-  typedef CwiseUnaryView<ViewOp, MatrixType, StrideType> Derived;
-  typedef typename dense_xpr_base<CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryView : internal::no_assignment_operator,
+  public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
+{
+  public:
 
-  EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeffRef(0)); }
+    typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const {
-    return StrideType::InnerStrideAtCompileTime != 0 ? int(StrideType::InnerStrideAtCompileTime)
-                                                     : derived().nestedExpression().innerStride() *
-                                                           sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const {
-    return StrideType::OuterStrideAtCompileTime != 0 ? int(StrideType::OuterStrideAtCompileTime)
-                                                     : derived().nestedExpression().outerStride() *
-                                                           sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar);
-  }
-
- protected:
-  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
-
-  // Allow const access to coeffRef for the case of direct access being enabled.
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
-    return internal::evaluator<Derived>(derived()).coeffRef(index);
-  }
-
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index col) const {
-    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
-  }
-};
-
-template <typename ViewOp, typename MatrixType, typename StrideType>
-class CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, true>
-    : public CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false> {
- public:
-  typedef CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, Dense, false> Base;
-  typedef CwiseUnaryView<ViewOp, MatrixType, StrideType> Derived;
-  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
-
-  using Base::data;
-  EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) {
-    return internal::evaluator<Derived>(derived()).coeffRef(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
-    return internal::evaluator<Derived>(derived()).coeffRef(index);
-  }
-
- protected:
-  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
-};
-
-}  // namespace internal
-
-/** \class CwiseUnaryView
- * \ingroup Core_Module
- *
- * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
- *
- * \tparam ViewOp template functor implementing the view
- * \tparam MatrixType the type of the matrix we are applying the unary operator
- *
- * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
- * It is the return type of real() and imag(), and most of the time this is the only way it is used.
- *
- * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
- */
-template <typename ViewOp, typename MatrixType, typename StrideType>
-class CwiseUnaryView : public internal::CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType,
-                                                           typename internal::traits<MatrixType>::StorageKind> {
- public:
-  typedef typename internal::CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType,
-                                                typename internal::traits<MatrixType>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
-  typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-  typedef internal::remove_all_t<MatrixType> NestedExpression;
-
-  explicit EIGEN_DEVICE_FUNC inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
+    inline CwiseUnaryView(const MatrixType& mat, const ViewOp& func = ViewOp())
       : m_matrix(mat), m_functor(func) {}
 
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+    EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
 
-  /** \returns the functor representing unary operation */
-  EIGEN_DEVICE_FUNC const ViewOp& functor() const { return m_functor; }
+    /** \returns the functor representing unary operation */
+    const ViewOp& functor() const { return m_functor; }
 
-  /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC const internal::remove_all_t<MatrixTypeNested>& nestedExpression() const { return m_matrix; }
+    /** \returns the nested expression */
+    const typename internal::remove_all<typename MatrixType::Nested>::type&
+    nestedExpression() const { return m_matrix; }
 
-  /** \returns the nested expression */
-  EIGEN_DEVICE_FUNC std::remove_reference_t<MatrixTypeNested>& nestedExpression() { return m_matrix; }
+    /** \returns the nested expression */
+    typename internal::remove_all<typename MatrixType::Nested>::type&
+    nestedExpression() { return m_matrix.const_cast_derived(); }
 
- protected:
-  MatrixTypeNested m_matrix;
-  ViewOp m_functor;
+  protected:
+    // FIXME changed from MatrixType::Nested because of a weird compilation error with sun CC
+    const typename internal::nested<MatrixType>::type m_matrix;
+    ViewOp m_functor;
 };
 
-}  // namespace Eigen
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
+  : public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
+{
+  public:
 
-#endif  // EIGEN_CWISE_UNARY_VIEW_H
+    typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
+    typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
+
+    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+
+    inline Index innerStride() const
+    {
+      return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
+    }
+
+    inline Index outerStride() const
+    {
+      return derived().nestedExpression().outerStride();
+    }
+
+    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
+    {
+      return derived().functor()(derived().nestedExpression().coeff(row, col));
+    }
+
+    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+    {
+      return derived().functor()(derived().nestedExpression().coeff(index));
+    }
+
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
+    {
+      return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
+    }
+
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
+    {
+      return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
+    }
+};
+
+
+
+#endif // EIGEN_CWISE_UNARY_VIEW_H

Eigen/src/Core/DenseStorage.h

@@ -3,648 +3,302 @@
 //
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
+// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_MATRIXSTORAGE_H
 #define EIGEN_MATRIXSTORAGE_H
 
 #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) \
-  X;                                                \
-  EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
+  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
 #else
-#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
+  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
 #endif
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 namespace internal {
 
 struct constructor_without_unaligned_array_assert {};
 
-template <typename T, int Size>
-EIGEN_DEVICE_FUNC constexpr void check_static_allocation_size() {
-// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
-#if EIGEN_STACK_ALLOCATION_LIMIT
-  EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
-#endif
-}
-
 /** \internal
- * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
- * to 16 bytes boundary if the total size is a multiple of 16 bytes.
- */
+  * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
+  * to 16 bytes boundary if the total size is a multiple of 16 bytes.
+  */
 template <typename T, int Size, int MatrixOrArrayOptions,
-          int Alignment = (MatrixOrArrayOptions & DontAlign) ? 0 : compute_default_alignment<T, Size>::value>
-struct plain_array {
+          int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
+                        : (((Size*sizeof(T))%16)==0) ? 16
+                        : 0 >
+struct plain_array
+{
   T array[Size];
-
-  EIGEN_DEVICE_FUNC constexpr plain_array() { check_static_allocation_size<T, Size>(); }
-
-  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
-    check_static_allocation_size<T, Size>();
-  }
+  plain_array() {}
+  plain_array(constructor_without_unaligned_array_assert) {}
 };
 
-#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
-#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
+#ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
+  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
 #else
-#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)                                                \
-  eigen_assert((internal::is_constant_evaluated() || (std::uintptr_t(array) & (sizemask)) == 0) && \
-               "this assertion is explained here: "                                                \
-               "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html"        \
-               " **** READ THIS WEB PAGE !!! ****");
+  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
+    eigen_assert((reinterpret_cast<size_t>(array) & sizemask) == 0 \
+              && "this assertion is explained here: " \
+              "http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
+              " **** READ THIS WEB PAGE !!! ****");
 #endif
 
 template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 8> {
-  EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
-
-  EIGEN_DEVICE_FUNC constexpr plain_array() {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
-    check_static_allocation_size<T, Size>();
-  }
-
-  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
-    check_static_allocation_size<T, Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 16> {
-  EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
-
-  EIGEN_DEVICE_FUNC constexpr plain_array() {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
-    check_static_allocation_size<T, Size>();
-  }
-
-  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
-    check_static_allocation_size<T, Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 32> {
-  EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
-
-  EIGEN_DEVICE_FUNC constexpr plain_array() {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
-    check_static_allocation_size<T, Size>();
-  }
-
-  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
-    check_static_allocation_size<T, Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 64> {
-  EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
-
-  EIGEN_DEVICE_FUNC constexpr plain_array() {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
-    check_static_allocation_size<T, Size>();
-  }
-
-  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
-    check_static_allocation_size<T, Size>();
-  }
+struct plain_array<T, Size, MatrixOrArrayOptions, 16>
+{
+  EIGEN_USER_ALIGN16 T array[Size];
+  plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) }
+  plain_array(constructor_without_unaligned_array_assert) {}
 };
 
 template <typename T, int MatrixOrArrayOptions, int Alignment>
-struct plain_array<T, 0, MatrixOrArrayOptions, Alignment> {
-  T array[1];
-  EIGEN_DEVICE_FUNC constexpr plain_array() {}
-  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {}
+struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
+{
+  EIGEN_USER_ALIGN16 T array[1];
+  plain_array() {}
+  plain_array(constructor_without_unaligned_array_assert) {}
 };
 
-struct plain_array_helper {
-  template <typename T, int Size, int MatrixOrArrayOptions, int Alignment>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void copy(
-      const plain_array<T, Size, MatrixOrArrayOptions, Alignment>& src, const Eigen::Index size,
-      plain_array<T, Size, MatrixOrArrayOptions, Alignment>& dst) {
-    smart_copy(src.array, src.array + size, dst.array);
-  }
-
-  template <typename T, int Size, int MatrixOrArrayOptions, int Alignment>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void swap(plain_array<T, Size, MatrixOrArrayOptions, Alignment>& a,
-                                                         const Eigen::Index a_size,
-                                                         plain_array<T, Size, MatrixOrArrayOptions, Alignment>& b,
-                                                         const Eigen::Index b_size) {
-    if (a_size < b_size) {
-      std::swap_ranges(b.array, b.array + a_size, a.array);
-      smart_move(b.array + a_size, b.array + b_size, a.array + a_size);
-    } else if (a_size > b_size) {
-      std::swap_ranges(a.array, a.array + b_size, b.array);
-      smart_move(a.array + b_size, a.array + a_size, b.array + b_size);
-    } else {
-      std::swap_ranges(a.array, a.array + a_size, b.array);
-    }
-  }
-};
-
-}  // end namespace internal
+} // end namespace internal
 
 /** \internal
- *
- * \class DenseStorage
- * \ingroup Core_Module
- *
- * \brief Stores the data of a matrix
- *
- * This class stores the data of fixed-size, dynamic-size or mixed matrices
- * in a way as compact as possible.
- *
- * \sa Matrix
- */
-template <typename T, int Size, int Rows_, int Cols_, int Options_>
-class DenseStorage;
+  *
+  * \class DenseStorage
+  * \ingroup Core_Module
+  *
+  * \brief Stores the data of a matrix
+  *
+  * This class stores the data of fixed-size, dynamic-size or mixed matrices
+  * in a way as compact as possible.
+  *
+  * \sa Matrix
+  */
+template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
 
 // purely fixed-size matrix
-template <typename T, int Size, int Rows_, int Cols_, int Options_>
-class DenseStorage {
-  internal::plain_array<T, Size, Options_> m_data;
-
- public:
-  constexpr EIGEN_DEVICE_FUNC DenseStorage(){EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(
-      Index size =
-          Size)} EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
+{
+    internal::plain_array<T,Size,_Options> m_data;
+  public:
+    inline explicit DenseStorage() {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
       : m_data(internal::constructor_without_unaligned_array_assert()) {}
-#if defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN)
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
-      : m_data(other.m_data){EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)}
-#else
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
-#endif
-        EIGEN_DEVICE_FUNC constexpr DenseStorage
-        &
-        operator=(const DenseStorage&) = default;
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&&) = default;
-  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&&) = default;
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-    eigen_internal_assert(size == rows * cols && rows == Rows_ && cols == Cols_);
-    EIGEN_UNUSED_VARIABLE(size);
-    EIGEN_UNUSED_VARIABLE(rows);
-    EIGEN_UNUSED_VARIABLE(cols);
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { numext::swap(m_data, other.m_data); }
-  EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
-  EIGEN_DEVICE_FUNC static constexpr Index cols(void) EIGEN_NOEXCEPT { return Cols_; }
-  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index) {}
-  EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index) {}
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+    inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
+    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
+    inline static DenseIndex rows(void) {return _Rows;}
+    inline static DenseIndex cols(void) {return _Cols;}
+    inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
+    inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
+    inline const T *data() const { return m_data.array; }
+    inline T *data() { return m_data.array; }
 };
 
 // null matrix
-template <typename T, int Rows_, int Cols_, int Options_>
-class DenseStorage<T, 0, Rows_, Cols_, Options_> {
- public:
-  static_assert(Rows_ * Cols_ == 0, "The fixed number of rows times columns must equal the storage size.");
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() {}
-  EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) { return *this; }
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index, Index) {}
-  EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage&) {}
-  EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
-  EIGEN_DEVICE_FUNC static constexpr Index cols(void) EIGEN_NOEXCEPT { return Cols_; }
-  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index) {}
-  EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index) {}
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return 0; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return 0; }
-};
-
-// more specializations for null matrices; these are necessary to resolve ambiguities
-template <typename T, int Options_>
-class DenseStorage<T, 0, Dynamic, Dynamic, Options_> {
-  Index m_rows;
-  Index m_cols;
-
- public:
-  EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
-  EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
-  EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_rows(other.m_rows), m_cols(other.m_cols) {}
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    m_rows = other.m_rows;
-    m_cols = other.m_cols;
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {
-    eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    numext::swap(m_rows, other.m_rows);
-    numext::swap(m_cols, other.m_cols);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_rows; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_cols; }
-  EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) {
-    m_rows = rows;
-    m_cols = cols;
-    eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) {
-    m_rows = rows;
-    m_cols = cols;
-    eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
-};
-
-template <typename T, int Rows_, int Options_>
-class DenseStorage<T, 0, Rows_, Dynamic, Options_> {
-  Index m_cols;
-
- public:
-  EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
-  EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
-  EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_cols(other.m_cols) {}
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    m_cols = other.m_cols;
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {
-    eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { numext::swap(m_cols, other.m_cols); }
-  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT { return m_cols; }
-  EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) {
-    m_cols = cols;
-    eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) {
-    m_cols = cols;
-    eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
-};
-
-template <typename T, int Cols_, int Options_>
-class DenseStorage<T, 0, Dynamic, Cols_, Options_> {
-  Index m_rows;
-
- public:
-  EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
-  EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
-  EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_rows(other.m_rows) {}
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    m_rows = other.m_rows;
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {
-    eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { numext::swap(m_rows, other.m_rows); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT { return m_rows; }
-  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT { return Cols_; }
-  EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) {
-    m_rows = rows;
-    eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) {
-    m_rows = rows;
-    eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return nullptr; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return nullptr; }
+template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
+{
+  public:
+    inline explicit DenseStorage() {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
+    inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
+    inline void swap(DenseStorage& ) {}
+    inline static DenseIndex rows(void) {return _Rows;}
+    inline static DenseIndex cols(void) {return _Cols;}
+    inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
+    inline void resize(DenseIndex,DenseIndex,DenseIndex) {}
+    inline const T *data() const { return 0; }
+    inline T *data() { return 0; }
 };
 
 // dynamic-size matrix with fixed-size storage
-template <typename T, int Size, int Options_>
-class DenseStorage<T, Size, Dynamic, Dynamic, Options_> {
-  internal::plain_array<T, Size, Options_> m_data;
-  Index m_rows;
-  Index m_cols;
-
- public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(), m_rows(0), m_cols(0) {}
-  EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
+{
+    internal::plain_array<T,Size,_Options> m_data;
+    DenseIndex m_rows;
+    DenseIndex m_cols;
+  public:
+    inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
       : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols) {
-    internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    if (this != &other) {
-      m_rows = other.m_rows;
-      m_cols = other.m_cols;
-      internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
-    }
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    internal::plain_array_helper::swap(m_data, m_rows * m_cols, other.m_data, other.m_rows * other.m_cols);
-    numext::swap(m_rows, other.m_rows);
-    numext::swap(m_cols, other.m_cols);
-  }
-  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
-  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
-  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index rows, Index cols) {
-    m_rows = rows;
-    m_cols = cols;
-  }
-  EIGEN_DEVICE_FUNC constexpr void resize(Index, Index rows, Index cols) {
-    m_rows = rows;
-    m_cols = cols;
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+    inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {}
+    inline void swap(DenseStorage& other)
+    { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
+    inline DenseIndex rows(void) const {return m_rows;}
+    inline DenseIndex cols(void) const {return m_cols;}
+    inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
+    inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
+    inline const T *data() const { return m_data.array; }
+    inline T *data() { return m_data.array; }
 };
 
 // dynamic-size matrix with fixed-size storage and fixed width
-template <typename T, int Size, int Cols_, int Options_>
-class DenseStorage<T, Size, Dynamic, Cols_, Options_> {
-  internal::plain_array<T, Size, Options_> m_data;
-  Index m_rows;
-
- public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_rows(0) {}
-  EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
+{
+    internal::plain_array<T,Size,_Options> m_data;
+    DenseIndex m_rows;
+  public:
+    inline explicit DenseStorage() : m_rows(0) {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
       : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows) {
-    internal::plain_array_helper::copy(other.m_data, m_rows * Cols_, m_data);
-  }
-
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    if (this != &other) {
-      m_rows = other.m_rows;
-      internal::plain_array_helper::copy(other.m_data, m_rows * Cols_, m_data);
-    }
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    internal::plain_array_helper::swap(m_data, m_rows * Cols_, other.m_data, other.m_rows * Cols_);
-    numext::swap(m_rows, other.m_rows);
-  }
-  EIGEN_DEVICE_FUNC constexpr Index rows(void) const EIGEN_NOEXCEPT { return m_rows; }
-  EIGEN_DEVICE_FUNC constexpr Index cols(void) const EIGEN_NOEXCEPT { return Cols_; }
-  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
-  EIGEN_DEVICE_FUNC constexpr void resize(Index, Index rows, Index) { m_rows = rows; }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+    inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {}
+    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
+    inline DenseIndex rows(void) const {return m_rows;}
+    inline DenseIndex cols(void) const {return _Cols;}
+    inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
+    inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
+    inline const T *data() const { return m_data.array; }
+    inline T *data() { return m_data.array; }
 };
 
 // dynamic-size matrix with fixed-size storage and fixed height
-template <typename T, int Size, int Rows_, int Options_>
-class DenseStorage<T, Size, Rows_, Dynamic, Options_> {
-  internal::plain_array<T, Size, Options_> m_data;
-  Index m_cols;
-
- public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_cols(0) {}
-  EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
+{
+    internal::plain_array<T,Size,_Options> m_data;
+    DenseIndex m_cols;
+  public:
+    inline explicit DenseStorage() : m_cols(0) {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
       : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols) {
-    internal::plain_array_helper::copy(other.m_data, Rows_ * m_cols, m_data);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    if (this != &other) {
-      m_cols = other.m_cols;
-      internal::plain_array_helper::copy(other.m_data, Rows_ * m_cols, m_data);
-    }
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    internal::plain_array_helper::swap(m_data, Rows_ * m_cols, other.m_data, Rows_ * other.m_cols);
-    numext::swap(m_cols, other.m_cols);
-  }
-  EIGEN_DEVICE_FUNC constexpr Index rows(void) const EIGEN_NOEXCEPT { return Rows_; }
-  EIGEN_DEVICE_FUNC constexpr Index cols(void) const EIGEN_NOEXCEPT { return m_cols; }
-  EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
-  EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index cols) { m_cols = cols; }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+    inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {}
+    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
+    inline DenseIndex rows(void) const {return _Rows;}
+    inline DenseIndex cols(void) const {return m_cols;}
+    inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
+    inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
+    inline const T *data() const { return m_data.array; }
+    inline T *data() { return m_data.array; }
 };
 
 // purely dynamic matrix.
-template <typename T, int Options_>
-class DenseStorage<T, Dynamic, Dynamic, Dynamic, Options_> {
-  T* m_data;
-  Index m_rows;
-  Index m_cols;
-
- public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
-  EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(0), m_rows(0), m_cols(0) {}
-  EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
-      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)),
-        m_rows(rows),
-        m_cols(cols) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-    eigen_internal_assert(size == rows * cols && rows >= 0 && cols >= 0);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(other.m_rows * other.m_cols)),
-        m_rows(other.m_rows),
-        m_cols(other.m_cols) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows * m_cols)
-    internal::smart_copy(other.m_data, other.m_data + other.m_rows * other.m_cols, m_data);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    if (this != &other) {
-      DenseStorage tmp(other);
-      this->swap(tmp);
+template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
+{
+    T *m_data;
+    DenseIndex m_rows;
+    DenseIndex m_cols;
+  public:
+    inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert)
+       : m_data(0), m_rows(0), m_cols(0) {}
+    inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols)
+      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols) 
+    { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
+    inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
+    inline void swap(DenseStorage& other)
+    { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
+    inline DenseIndex rows(void) const {return m_rows;}
+    inline DenseIndex cols(void) const {return m_cols;}
+    inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols)
+    {
+      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
+      m_rows = rows;
+      m_cols = cols;
     }
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT : m_data(std::move(other.m_data)),
-                                                                        m_rows(std::move(other.m_rows)),
-                                                                        m_cols(std::move(other.m_cols)) {
-    other.m_data = nullptr;
-    other.m_rows = 0;
-    other.m_cols = 0;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT {
-    numext::swap(m_data, other.m_data);
-    numext::swap(m_rows, other.m_rows);
-    numext::swap(m_cols, other.m_cols);
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC ~DenseStorage() {
-    internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, m_rows * m_cols);
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    numext::swap(m_data, other.m_data);
-    numext::swap(m_rows, other.m_rows);
-    numext::swap(m_cols, other.m_cols);
-  }
-  EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT { return m_rows; }
-  EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT { return m_cols; }
-  void conservativeResize(Index size, Index rows, Index cols) {
-    m_data =
-        internal::conditional_aligned_realloc_new_auto<T, (Options_ & DontAlign) == 0>(m_data, size, m_rows * m_cols);
-    m_rows = rows;
-    m_cols = cols;
-  }
-  EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols) {
-    if (size != m_rows * m_cols) {
-      internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, m_rows * m_cols);
-      if (size > 0)  // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-        m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size);
-      else
-        m_data = 0;
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    void resize(DenseIndex size, DenseIndex rows, DenseIndex cols)
+    {
+      if(size != m_rows*m_cols)
+      {
+        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
+        if (size)
+          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
+        else
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
+      }
+      m_rows = rows;
+      m_cols = cols;
     }
-    m_rows = rows;
-    m_cols = cols;
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+    inline const T *data() const { return m_data; }
+    inline T *data() { return m_data; }
 };
 
 // matrix with dynamic width and fixed height (so that matrix has dynamic size).
-template <typename T, int Rows_, int Options_>
-class DenseStorage<T, Dynamic, Rows_, Dynamic, Options_> {
-  T* m_data;
-  Index m_cols;
-
- public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_cols(0) {}
-  explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
-  EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
-      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_cols(cols) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-    eigen_internal_assert(size == rows * cols && rows == Rows_ && cols >= 0);
-    EIGEN_UNUSED_VARIABLE(rows);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(Rows_ * other.m_cols)),
-        m_cols(other.m_cols) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols * Rows_)
-    internal::smart_copy(other.m_data, other.m_data + Rows_ * m_cols, m_data);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    if (this != &other) {
-      DenseStorage tmp(other);
-      this->swap(tmp);
+template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
+{
+    T *m_data;
+    DenseIndex m_cols;
+  public:
+    inline explicit DenseStorage() : m_data(0), m_cols(0) {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
+    inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
+    { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
+    inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
+    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
+    inline static DenseIndex rows(void) {return _Rows;}
+    inline DenseIndex cols(void) const {return m_cols;}
+    inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols)
+    {
+      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
+      m_cols = cols;
     }
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT : m_data(std::move(other.m_data)),
-                                                                        m_cols(std::move(other.m_cols)) {
-    other.m_data = nullptr;
-    other.m_cols = 0;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT {
-    numext::swap(m_data, other.m_data);
-    numext::swap(m_cols, other.m_cols);
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC ~DenseStorage() {
-    internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, Rows_ * m_cols);
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    numext::swap(m_data, other.m_data);
-    numext::swap(m_cols, other.m_cols);
-  }
-  EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
-  EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT { return m_cols; }
-  EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols) {
-    m_data =
-        internal::conditional_aligned_realloc_new_auto<T, (Options_ & DontAlign) == 0>(m_data, size, Rows_ * m_cols);
-    m_cols = cols;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols) {
-    if (size != Rows_ * m_cols) {
-      internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, Rows_ * m_cols);
-      if (size > 0)  // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-        m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size);
-      else
-        m_data = 0;
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols)
+    {
+      if(size != _Rows*m_cols)
+      {
+        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
+        if (size)
+          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
+        else
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
+      }
+      m_cols = cols;
     }
-    m_cols = cols;
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+    inline const T *data() const { return m_data; }
+    inline T *data() { return m_data; }
 };
 
 // matrix with dynamic height and fixed width (so that matrix has dynamic size).
-template <typename T, int Cols_, int Options_>
-class DenseStorage<T, Dynamic, Dynamic, Cols_, Options_> {
-  T* m_data;
-  Index m_rows;
-
- public:
-  EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_rows(0) {}
-  explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
-  EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols)
-      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_rows(rows) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-    eigen_internal_assert(size == rows * cols && rows >= 0 && cols == Cols_);
-    EIGEN_UNUSED_VARIABLE(cols);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(other.m_rows * Cols_)),
-        m_rows(other.m_rows) {
-    EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows * Cols_)
-    internal::smart_copy(other.m_data, other.m_data + other.m_rows * Cols_, m_data);
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
-    if (this != &other) {
-      DenseStorage tmp(other);
-      this->swap(tmp);
+template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
+{
+    T *m_data;
+    DenseIndex m_rows;
+  public:
+    inline explicit DenseStorage() : m_data(0), m_rows(0) {}
+    inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
+    inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
+    { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
+    inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
+    inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
+    inline DenseIndex rows(void) const {return m_rows;}
+    inline static DenseIndex cols(void) {return _Cols;}
+    inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex)
+    {
+      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
+      m_rows = rows;
     }
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT : m_data(std::move(other.m_data)),
-                                                                        m_rows(std::move(other.m_rows)) {
-    other.m_data = nullptr;
-    other.m_rows = 0;
-  }
-  EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT {
-    numext::swap(m_data, other.m_data);
-    numext::swap(m_rows, other.m_rows);
-    return *this;
-  }
-  EIGEN_DEVICE_FUNC ~DenseStorage() {
-    internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, Cols_ * m_rows);
-  }
-  EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-    numext::swap(m_data, other.m_data);
-    numext::swap(m_rows, other.m_rows);
-  }
-  EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT { return m_rows; }
-  EIGEN_DEVICE_FUNC static constexpr Index cols(void) { return Cols_; }
-  void conservativeResize(Index size, Index rows, Index) {
-    m_data =
-        internal::conditional_aligned_realloc_new_auto<T, (Options_ & DontAlign) == 0>(m_data, size, m_rows * Cols_);
-    m_rows = rows;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index) {
-    if (size != m_rows * Cols_) {
-      internal::conditional_aligned_delete_auto<T, (Options_ & DontAlign) == 0>(m_data, Cols_ * m_rows);
-      if (size > 0)  // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-        m_data = internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size);
-      else
-        m_data = 0;
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+    EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex)
+    {
+      if(size != m_rows*_Cols)
+      {
+        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
+        if (size)
+          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
+        else
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
+      }
+      m_rows = rows;
     }
-    m_rows = rows;
-  }
-  EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data; }
-  EIGEN_DEVICE_FUNC constexpr T* data() { return m_data; }
+    inline const T *data() const { return m_data; }
+    inline T *data() { return m_data; }
 };
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATRIX_H
+#endif // EIGEN_MATRIX_H

Eigen/src/Core/DeviceWrapper.h

@@ -1,155 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2023 Charlie Schlosser <cs.schlosser@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DEVICEWRAPPER_H
-#define EIGEN_DEVICEWRAPPER_H
-
-namespace Eigen {
-template <typename Derived, typename Device>
-struct DeviceWrapper {
-  using Base = EigenBase<internal::remove_all_t<Derived>>;
-  using Scalar = typename Derived::Scalar;
-
-  EIGEN_DEVICE_FUNC DeviceWrapper(Base& xpr, Device& device) : m_xpr(xpr.derived()), m_device(device) {}
-  EIGEN_DEVICE_FUNC DeviceWrapper(const Base& xpr, Device& device) : m_xpr(xpr.derived()), m_device(device) {}
-
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived>& other) {
-    using AssignOp = internal::assign_op<Scalar, typename OtherDerived::Scalar>;
-    internal::call_assignment(*this, other.derived(), AssignOp());
-    return m_xpr;
-  }
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const EigenBase<OtherDerived>& other) {
-    using AddAssignOp = internal::add_assign_op<Scalar, typename OtherDerived::Scalar>;
-    internal::call_assignment(*this, other.derived(), AddAssignOp());
-    return m_xpr;
-  }
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const EigenBase<OtherDerived>& other) {
-    using SubAssignOp = internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>;
-    internal::call_assignment(*this, other.derived(), SubAssignOp());
-    return m_xpr;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& derived() { return m_xpr; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Device& device() { return m_device; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NoAlias<DeviceWrapper, EigenBase> noalias() {
-    return NoAlias<DeviceWrapper, EigenBase>(*this);
-  }
-
-  Derived& m_xpr;
-  Device& m_device;
-};
-
-namespace internal {
-
-// this is where we differentiate between lazy assignment and specialized kernels (e.g. matrix products)
-template <typename DstXprType, typename SrcXprType, typename Functor, typename Device,
-          typename Kind = typename AssignmentKind<typename evaluator_traits<DstXprType>::Shape,
-                                                  typename evaluator_traits<SrcXprType>::Shape>::Kind,
-          typename EnableIf = void>
-struct AssignmentWithDevice;
-
-// unless otherwise specified, use the default product implementation
-template <typename DstXprType, typename Lhs, typename Rhs, int Options, typename Functor, typename Device,
-          typename Weak>
-struct AssignmentWithDevice<DstXprType, Product<Lhs, Rhs, Options>, Functor, Device, Dense2Dense, Weak> {
-  using SrcXprType = Product<Lhs, Rhs, Options>;
-  using Base = Assignment<DstXprType, SrcXprType, Functor>;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func,
-                                                        Device&) {
-    Base::run(dst, src, func);
-  };
-};
-
-// specialization for coeffcient-wise assignment
-template <typename DstXprType, typename SrcXprType, typename Functor, typename Device, typename Weak>
-struct AssignmentWithDevice<DstXprType, SrcXprType, Functor, Device, Dense2Dense, Weak> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(DstXprType& dst, const SrcXprType& src, const Functor& func,
-                                                        Device& device) {
-#ifndef EIGEN_NO_DEBUG
-    internal::check_for_aliasing(dst, src);
-#endif
-
-    call_dense_assignment_loop(dst, src, func, device);
-  }
-};
-
-// this allows us to use the default evaluation scheme if it is not specialized for the device
-template <typename Kernel, typename Device, int Traversal = Kernel::AssignmentTraits::Traversal,
-          int Unrolling = Kernel::AssignmentTraits::Unrolling>
-struct dense_assignment_loop_with_device {
-  using Base = dense_assignment_loop<Kernel, Traversal, Unrolling>;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel& kernel, Device&) { Base::run(kernel); }
-};
-
-// entry point for a generic expression with device
-template <typename Dst, typename Src, typename Func, typename Device>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_assignment_no_alias(DeviceWrapper<Dst, Device> dst,
-                                                                                    const Src& src, const Func& func) {
-  enum {
-    NeedToTranspose = ((int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1) ||
-                       (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)) &&
-                      int(Dst::SizeAtCompileTime) != 1
-  };
-
-  using ActualDstTypeCleaned = std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst>;
-  using ActualDstType = std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&>;
-  ActualDstType actualDst(dst.derived());
-
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned, Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func, typename ActualDstTypeCleaned::Scalar, typename Src::Scalar);
-
-  // this provides a mechanism for specializing simple assignments, matrix products, etc
-  AssignmentWithDevice<ActualDstTypeCleaned, Src, Func, Device>::run(actualDst, src, func, dst.device());
-}
-
-// copy and pasted from AssignEvaluator except forward device to kernel
-template <typename DstXprType, typename SrcXprType, typename Functor, typename Device>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_dense_assignment_loop(DstXprType& dst,
-                                                                                      const SrcXprType& src,
-                                                                                      const Functor& func,
-                                                                                      Device& device) {
-  using DstEvaluatorType = evaluator<DstXprType>;
-  using SrcEvaluatorType = evaluator<SrcXprType>;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
-  // we need to resize the destination after the source evaluator has been created.
-  resize_if_allowed(dst, src, func);
-
-  DstEvaluatorType dstEvaluator(dst);
-
-  using Kernel = generic_dense_assignment_kernel<DstEvaluatorType, SrcEvaluatorType, Functor>;
-
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  dense_assignment_loop_with_device<Kernel, Device>::run(kernel, device);
-}
-
-}  // namespace internal
-
-template <typename Derived>
-template <typename Device>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<Derived, Device> EigenBase<Derived>::device(Device& device) {
-  return DeviceWrapper<Derived, Device>(derived(), device);
-}
-
-template <typename Derived>
-template <typename Device>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<const Derived, Device> EigenBase<Derived>::device(
-    Device& device) const {
-  return DeviceWrapper<const Derived, Device>(derived(), device);
-}
-}  // namespace Eigen
-#endif

Eigen/src/Core/Diagonal.h

@@ -2,220 +2,226 @@
 // for linear algebra.
 //
 // Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_DIAGONAL_H
 #define EIGEN_DIAGONAL_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 /** \class Diagonal
- * \ingroup Core_Module
- *
- * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
- *
- * \tparam MatrixType the type of the object in which we are taking a sub/main/super diagonal
- * \tparam DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
- *              A positive value means a superdiagonal, a negative value means a subdiagonal.
- *              You can also use DynamicIndex so the index can be set at runtime.
- *
- * The matrix is not required to be square.
- *
- * This class represents an expression of the main diagonal, or any sub/super diagonal
- * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
- * time this is the only way it is used.
- *
- * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
- */
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
+  *
+  * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
+  * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
+  *              A positive value means a superdiagonal, a negative value means a subdiagonal.
+  *              You can also use Dynamic so the index can be set at runtime.
+  *
+  * The matrix is not required to be square.
+  *
+  * This class represents an expression of the main diagonal, or any sub/super diagonal
+  * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
+  * time this is the only way it is used.
+  *
+  * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
+  */
 
 namespace internal {
-template <typename MatrixType, int DiagIndex>
-struct traits<Diagonal<MatrixType, DiagIndex> > : traits<MatrixType> {
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+template<typename MatrixType, int DiagIndex>
+struct traits<Diagonal<MatrixType,DiagIndex> >
+ : traits<MatrixType>
+{
+  typedef typename nested<MatrixType>::type MatrixTypeNested;
+  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
   typedef typename MatrixType::StorageKind StorageKind;
   enum {
-    RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic)
-                            ? Dynamic
-                            : (plain_enum_min(MatrixType::RowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
-                                              MatrixType::ColsAtCompileTime - plain_enum_max(DiagIndex, 0))),
+    AbsDiagIndex = DiagIndex<0 ? -DiagIndex : DiagIndex, // only used if DiagIndex != Dynamic
+    // FIXME these computations are broken in the case where the matrix is rectangular and DiagIndex!=0
+    RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
+                      : (EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime,
+                                        MatrixType::ColsAtCompileTime) - AbsDiagIndex),
     ColsAtCompileTime = 1,
-    MaxRowsAtCompileTime =
-        int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
-        : DiagIndex == DynamicIndex
-            ? min_size_prefer_fixed(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime)
-            : (plain_enum_min(MatrixType::MaxRowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
-                              MatrixType::MaxColsAtCompileTime - plain_enum_max(DiagIndex, 0))),
+    MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
+                         : DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
+                                                                    MatrixType::MaxColsAtCompileTime)
+                         : (EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime) - AbsDiagIndex),
     MaxColsAtCompileTime = 1,
     MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = (unsigned int)MatrixTypeNested_::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) &
-            ~RowMajorBit,  // FIXME DirectAccessBit should not be handled by expressions
+    Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
+    CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
     MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
-    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride + 1,
+    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
     OuterStrideAtCompileTime = 0
   };
 };
-}  // namespace internal
+}
 
-template <typename MatrixType, int DiagIndex_>
-class Diagonal : public internal::dense_xpr_base<Diagonal<MatrixType, DiagIndex_> >::type {
- public:
-  enum { DiagIndex = DiagIndex_ };
-  typedef typename internal::dense_xpr_base<Diagonal>::type Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
+template<typename MatrixType, int DiagIndex> class Diagonal
+   : public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex> >::type
+{
+  public:
 
-  EIGEN_DEVICE_FUNC explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex)
-      : m_matrix(matrix), m_index(a_index) {
-    eigen_assert(a_index <= m_matrix.cols() && -a_index <= m_matrix.rows());
-  }
+    typedef typename internal::dense_xpr_base<Diagonal>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
 
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
+    inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {}
 
-  EIGEN_DEVICE_FUNC inline Index rows() const {
-    return m_index.value() < 0 ? numext::mini<Index>(m_matrix.cols(), m_matrix.rows() + m_index.value())
-                               : numext::mini<Index>(m_matrix.rows(), m_matrix.cols() - m_index.value());
-  }
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return 1; }
+    inline Index rows() const
+    { return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const EIGEN_NOEXCEPT {
-    return m_matrix.outerStride() + 1;
-  }
+    inline Index cols() const { return 1; }
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const EIGEN_NOEXCEPT { return 0; }
+    inline Index innerStride() const
+    {
+      return m_matrix.outerStride() + 1;
+    }
 
-  typedef std::conditional_t<internal::is_lvalue<MatrixType>::value, Scalar, const Scalar> ScalarWithConstIfNotLvalue;
+    inline Index outerStride() const
+    {
+      return 0;
+    }
 
-  EIGEN_DEVICE_FUNC inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
-  EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
+    inline Scalar& coeffRef(Index row, Index)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+      return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
+    }
 
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index) {
-    EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-    return m_matrix.coeffRef(row + rowOffset(), row + colOffset());
-  }
+    inline const Scalar& coeffRef(Index row, Index) const
+    {
+      return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index) const {
-    return m_matrix.coeffRef(row + rowOffset(), row + colOffset());
-  }
+    inline CoeffReturnType coeff(Index row, Index) const
+    {
+      return m_matrix.coeff(row+rowOffset(), row+colOffset());
+    }
 
-  EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index row, Index) const {
-    return m_matrix.coeff(row + rowOffset(), row + colOffset());
-  }
+    inline Scalar& coeffRef(Index index)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+      return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
+    }
 
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index idx) {
-    EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-    return m_matrix.coeffRef(idx + rowOffset(), idx + colOffset());
-  }
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
+    }
 
-  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index idx) const {
-    return m_matrix.coeffRef(idx + rowOffset(), idx + colOffset());
-  }
+    inline CoeffReturnType coeff(Index index) const
+    {
+      return m_matrix.coeff(index+rowOffset(), index+colOffset());
+    }
 
-  EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index idx) const {
-    return m_matrix.coeff(idx + rowOffset(), idx + colOffset());
-  }
+  protected:
+    const typename MatrixType::Nested m_matrix;
+    const internal::variable_if_dynamic<Index, DiagIndex> m_index;
 
-  EIGEN_DEVICE_FUNC inline const internal::remove_all_t<typename MatrixType::Nested>& nestedExpression() const {
-    return m_matrix;
-  }
-
-  EIGEN_DEVICE_FUNC inline Index index() const { return m_index.value(); }
-
- protected:
-  typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
-  const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
-
- private:
-  // some compilers may fail to optimize std::max etc in case of compile-time constants...
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index absDiagIndex() const EIGEN_NOEXCEPT {
-    return m_index.value() > 0 ? m_index.value() : -m_index.value();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index rowOffset() const EIGEN_NOEXCEPT {
-    return m_index.value() > 0 ? 0 : -m_index.value();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR Index colOffset() const EIGEN_NOEXCEPT {
-    return m_index.value() > 0 ? m_index.value() : 0;
-  }
-  // trigger a compile-time error if someone try to call packet
-  template <int LoadMode>
-  typename MatrixType::PacketReturnType packet(Index) const;
-  template <int LoadMode>
-  typename MatrixType::PacketReturnType packet(Index, Index) const;
+  private:
+    // some compilers may fail to optimize std::max etc in case of compile-time constants...
+    EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
+    EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); }
+    EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; }
+    // triger a compile time error is someone try to call packet
+    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
+    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
 };
 
 /** \returns an expression of the main diagonal of the matrix \c *this
- *
- * \c *this is not required to be square.
- *
- * Example: \include MatrixBase_diagonal.cpp
- * Output: \verbinclude MatrixBase_diagonal.out
- *
- * \sa class Diagonal */
-template <typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalReturnType MatrixBase<Derived>::diagonal() {
-  return DiagonalReturnType(derived());
+  *
+  * \c *this is not required to be square.
+  *
+  * Example: \include MatrixBase_diagonal.cpp
+  * Output: \verbinclude MatrixBase_diagonal.out
+  *
+  * \sa class Diagonal */
+template<typename Derived>
+inline typename MatrixBase<Derived>::DiagonalReturnType
+MatrixBase<Derived>::diagonal()
+{
+  return derived();
 }
 
 /** This is the const version of diagonal(). */
-template <typename Derived>
-EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::ConstDiagonalReturnType MatrixBase<Derived>::diagonal()
-    const {
+template<typename Derived>
+inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
+MatrixBase<Derived>::diagonal() const
+{
   return ConstDiagonalReturnType(derived());
 }
 
 /** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
- *
- * \c *this is not required to be square.
- *
- * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
- * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
- *
- * Example: \include MatrixBase_diagonal_int.cpp
- * Output: \verbinclude MatrixBase_diagonal_int.out
- *
- * \sa MatrixBase::diagonal(), class Diagonal */
-template <typename Derived>
-EIGEN_DEVICE_FUNC inline Diagonal<Derived, DynamicIndex> MatrixBase<Derived>::diagonal(Index index) {
-  return Diagonal<Derived, DynamicIndex>(derived(), index);
+  *
+  * \c *this is not required to be square.
+  *
+  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
+  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
+  *
+  * Example: \include MatrixBase_diagonal_int.cpp
+  * Output: \verbinclude MatrixBase_diagonal_int.out
+  *
+  * \sa MatrixBase::diagonal(), class Diagonal */
+template<typename Derived>
+inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Dynamic>::Type
+MatrixBase<Derived>::diagonal(Index index)
+{
+  return typename DiagonalIndexReturnType<Dynamic>::Type(derived(), index);
 }
 
 /** This is the const version of diagonal(Index). */
-template <typename Derived>
-EIGEN_DEVICE_FUNC inline const Diagonal<const Derived, DynamicIndex> MatrixBase<Derived>::diagonal(Index index) const {
-  return Diagonal<const Derived, DynamicIndex>(derived(), index);
+template<typename Derived>
+inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Dynamic>::Type
+MatrixBase<Derived>::diagonal(Index index) const
+{
+  return typename ConstDiagonalIndexReturnType<Dynamic>::Type(derived(), index);
 }
 
 /** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
- *
- * \c *this is not required to be square.
- *
- * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
- * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
- *
- * Example: \include MatrixBase_diagonal_template_int.cpp
- * Output: \verbinclude MatrixBase_diagonal_template_int.out
- *
- * \sa MatrixBase::diagonal(), class Diagonal */
-template <typename Derived>
-template <int Index_>
-EIGEN_DEVICE_FUNC inline Diagonal<Derived, Index_> MatrixBase<Derived>::diagonal() {
-  return Diagonal<Derived, Index_>(derived());
+  *
+  * \c *this is not required to be square.
+  *
+  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
+  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
+  *
+  * Example: \include MatrixBase_diagonal_template_int.cpp
+  * Output: \verbinclude MatrixBase_diagonal_template_int.out
+  *
+  * \sa MatrixBase::diagonal(), class Diagonal */
+template<typename Derived>
+template<int Index>
+inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type
+MatrixBase<Derived>::diagonal()
+{
+  return derived();
 }
 
 /** This is the const version of diagonal<int>(). */
-template <typename Derived>
-template <int Index_>
-EIGEN_DEVICE_FUNC inline const Diagonal<const Derived, Index_> MatrixBase<Derived>::diagonal() const {
-  return Diagonal<const Derived, Index_>(derived());
+template<typename Derived>
+template<int Index>
+inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type
+MatrixBase<Derived>::diagonal() const
+{
+  return derived();
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_DIAGONAL_H
+#endif // EIGEN_DIAGONAL_H

Eigen/src/Core/DiagonalMatrix.h

@@ -4,411 +4,303 @@
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_DIAGONALMATRIX_H
 #define EIGEN_DIAGONALMATRIX_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename Derived>
+class DiagonalBase : public EigenBase<Derived>
+{
+  public:
+    typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
+    typedef typename DiagonalVectorType::Scalar Scalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Index Index;
 
-namespace Eigen {
+    enum {
+      RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+      ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+      MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+      MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+      IsVectorAtCompileTime = 0,
+      Flags = 0
+    };
 
-/** \class DiagonalBase
- * \ingroup Core_Module
- *
- * \brief Base class for diagonal matrices and expressions
- *
- * This is the base class that is inherited by diagonal matrix and related expression
- * types, which internally use a vector for storing the diagonal entries. Diagonal
- * types always represent square matrices.
- *
- * \tparam Derived is the derived type, a DiagonalMatrix or DiagonalWrapper.
- *
- * \sa class DiagonalMatrix, class DiagonalWrapper
- */
-template <typename Derived>
-class DiagonalBase : public EigenBase<Derived> {
- public:
-  typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
-  typedef typename DiagonalVectorType::Scalar Scalar;
-  typedef typename DiagonalVectorType::RealScalar RealScalar;
-  typedef typename internal::traits<Derived>::StorageKind StorageKind;
-  typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+    typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
+    typedef DenseMatrixType DenseType;
+    typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
 
-  enum {
-    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    IsVectorAtCompileTime = 0,
-    Flags = NoPreferredStorageOrderBit
-  };
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
 
-  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime>
-      DenseMatrixType;
-  typedef DenseMatrixType DenseType;
-  typedef DiagonalMatrix<Scalar, DiagonalVectorType::SizeAtCompileTime, DiagonalVectorType::MaxSizeAtCompileTime>
-      PlainObject;
+    DenseMatrixType toDenseMatrix() const { return derived(); }
+    template<typename DenseDerived>
+    void evalTo(MatrixBase<DenseDerived> &other) const;
+    template<typename DenseDerived>
+    void addTo(MatrixBase<DenseDerived> &other) const
+    { other.diagonal() += diagonal(); }
+    template<typename DenseDerived>
+    void subTo(MatrixBase<DenseDerived> &other) const
+    { other.diagonal() -= diagonal(); }
 
-  /** \returns a reference to the derived object. */
-  EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-  /** \returns a const reference to the derived object. */
-  EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
+    inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
+    inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
 
-  /**
-   * Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
-   * not an expression.
-   * \returns A dense matrix, with its diagonal entries set from the the derived object. */
-  EIGEN_DEVICE_FUNC DenseMatrixType toDenseMatrix() const { return derived(); }
+    inline Index rows() const { return diagonal().size(); }
+    inline Index cols() const { return diagonal().size(); }
 
-  /** \returns a reference to the derived object's vector of diagonal coefficients. */
-  EIGEN_DEVICE_FUNC inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
-  /** \returns a const reference to the derived object's vector of diagonal coefficients. */
-  EIGEN_DEVICE_FUNC inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
+    template<typename MatrixDerived>
+    const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
+    operator*(const MatrixBase<MatrixDerived> &matrix) const;
 
-  /** \returns the value of the coefficient as if \c *this was a dense matrix. */
-  EIGEN_DEVICE_FUNC inline Scalar coeff(Index row, Index col) const {
-    eigen_assert(row >= 0 && col >= 0 && row < rows() && col <= cols());
-    return row == col ? diagonal().coeff(row) : Scalar(0);
-  }
-
-  /** \returns the number of rows. */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const { return diagonal().size(); }
-  /** \returns the number of columns. */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const { return diagonal().size(); }
-
-  /** \returns the diagonal matrix product of \c *this by the dense matrix, \a matrix */
-  template <typename MatrixDerived>
-  EIGEN_DEVICE_FUNC const Product<Derived, MatrixDerived, LazyProduct> operator*(
-      const MatrixBase<MatrixDerived>& matrix) const {
-    return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
-  }
-
-  template <typename OtherDerived>
-  using DiagonalProductReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
-      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, product)>;
-
-  /** \returns the diagonal matrix product of \c *this by the diagonal matrix \a other */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC const DiagonalProductReturnType<OtherDerived> operator*(
-      const DiagonalBase<OtherDerived>& other) const {
-    return diagonal().cwiseProduct(other.diagonal()).asDiagonal();
-  }
-
-  using DiagonalInverseReturnType =
-      DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType>>;
-
-  /** \returns the inverse \c *this. Computed as the coefficient-wise inverse of the diagonal. */
-  EIGEN_DEVICE_FUNC inline const DiagonalInverseReturnType inverse() const {
-    return diagonal().cwiseInverse().asDiagonal();
-  }
-
-  using DiagonalScaleReturnType =
-      DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType, Scalar, product)>;
-
-  /** \returns the product of \c *this by the scalar \a scalar */
-  EIGEN_DEVICE_FUNC inline const DiagonalScaleReturnType operator*(const Scalar& scalar) const {
-    return (diagonal() * scalar).asDiagonal();
-  }
-
-  using ScaleDiagonalReturnType =
-      DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, DiagonalVectorType, product)>;
-
-  /** \returns the product of a scalar and the diagonal matrix \a other */
-  EIGEN_DEVICE_FUNC friend inline const ScaleDiagonalReturnType operator*(const Scalar& scalar,
-                                                                          const DiagonalBase& other) {
-    return (scalar * other.diagonal()).asDiagonal();
-  }
-
-  template <typename OtherDerived>
-  using DiagonalSumReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
-      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, sum)>;
-
-  /** \returns the sum of \c *this and the diagonal matrix \a other */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline const DiagonalSumReturnType<OtherDerived> operator+(
-      const DiagonalBase<OtherDerived>& other) const {
-    return (diagonal() + other.diagonal()).asDiagonal();
-  }
-
-  template <typename OtherDerived>
-  using DiagonalDifferenceReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
-      DiagonalVectorType, typename OtherDerived::DiagonalVectorType, difference)>;
-
-  /** \returns the difference of \c *this and the diagonal matrix \a other */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline const DiagonalDifferenceReturnType<OtherDerived> operator-(
-      const DiagonalBase<OtherDerived>& other) const {
-    return (diagonal() - other.diagonal()).asDiagonal();
-  }
+    inline const DiagonalWrapper<CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
+    inverse() const
+    {
+      return diagonal().cwiseInverse();
+    }
+    
+    #ifdef EIGEN2_SUPPORT
+    template<typename OtherDerived>
+    bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
+    {
+      return diagonal().isApprox(other.diagonal(), precision);
+    }
+    template<typename OtherDerived>
+    bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
+    {
+      return toDenseMatrix().isApprox(other, precision);
+    }
+    #endif
 };
 
+template<typename Derived>
+template<typename DenseDerived>
+void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
+{
+  other.setZero();
+  other.diagonal() = diagonal();
+}
+#endif
+
 /** \class DiagonalMatrix
- * \ingroup Core_Module
- *
- * \brief Represents a diagonal matrix with its storage
- *
- * \tparam Scalar_ the type of coefficients
- * \tparam SizeAtCompileTime the dimension of the matrix, or Dynamic
- * \tparam MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
- *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
- *
- * \sa class DiagonalBase, class DiagonalWrapper
- */
+  * \ingroup Core_Module
+  *
+  * \brief Represents a diagonal matrix with its storage
+  *
+  * \param _Scalar the type of coefficients
+  * \param SizeAtCompileTime the dimension of the matrix, or Dynamic
+  * \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
+  *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
+  *
+  * \sa class DiagonalWrapper
+  */
 
 namespace internal {
-template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-struct traits<DiagonalMatrix<Scalar_, SizeAtCompileTime, MaxSizeAtCompileTime>>
-    : traits<Matrix<Scalar_, SizeAtCompileTime, SizeAtCompileTime, 0, MaxSizeAtCompileTime, MaxSizeAtCompileTime>> {
-  typedef Matrix<Scalar_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> DiagonalVectorType;
-  typedef DiagonalShape StorageKind;
-  enum { Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit };
+template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
+ : traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
+{
+  typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
+  typedef Dense StorageKind;
+  typedef DenseIndex Index;
+  enum {
+    Flags = LvalueBit
+  };
 };
-}  // namespace internal
-template <typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-class DiagonalMatrix : public DiagonalBase<DiagonalMatrix<Scalar_, SizeAtCompileTime, MaxSizeAtCompileTime>> {
- public:
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
-  typedef const DiagonalMatrix& Nested;
-  typedef Scalar_ Scalar;
-  typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
-  typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
-#endif
+}
+template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+class DiagonalMatrix
+  : public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
+{
+  public:
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
+    typedef const DiagonalMatrix& Nested;
+    typedef _Scalar Scalar;
+    typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
+    typedef typename internal::traits<DiagonalMatrix>::Index Index;
+    #endif
 
- protected:
-  DiagonalVectorType m_diagonal;
+  protected:
 
- public:
-  /** const version of diagonal(). */
-  EIGEN_DEVICE_FUNC inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
-  /** \returns a reference to the stored vector of diagonal coefficients. */
-  EIGEN_DEVICE_FUNC inline DiagonalVectorType& diagonal() { return m_diagonal; }
+    DiagonalVectorType m_diagonal;
 
-  /** Default constructor without initialization */
-  EIGEN_DEVICE_FUNC inline DiagonalMatrix() {}
+  public:
 
-  /** Constructs a diagonal matrix with given dimension  */
-  EIGEN_DEVICE_FUNC explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
+    /** const version of diagonal(). */
+    inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
+    /** \returns a reference to the stored vector of diagonal coefficients. */
+    inline DiagonalVectorType& diagonal() { return m_diagonal; }
 
-  /** 2D constructor. */
-  EIGEN_DEVICE_FUNC inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x, y) {}
+    /** Default constructor without initialization */
+    inline DiagonalMatrix() {}
 
-  /** 3D constructor. */
-  EIGEN_DEVICE_FUNC inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x, y, z) {}
+    /** Constructs a diagonal matrix with given dimension  */
+    inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
 
-  /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients.
-   *
-   * \warning To construct a diagonal matrix of fixed size, the number of values passed to this
-   * constructor must match the fixed dimension of \c *this.
-   *
-   * \sa DiagonalMatrix(const Scalar&, const Scalar&)
-   * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&)
-   */
-  template <typename... ArgTypes>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,
-                                                       const ArgTypes&... args)
-      : m_diagonal(a0, a1, a2, args...) {}
+    /** 2D constructor. */
+    inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
 
-  /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer
-   * lists \cpp11
-   */
-  EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE DiagonalMatrix(
-      const std::initializer_list<std::initializer_list<Scalar>>& list)
-      : m_diagonal(list) {}
+    /** 3D constructor. */
+    inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
 
-  /** \brief Constructs a DiagonalMatrix from an r-value diagonal vector type */
-  EIGEN_DEVICE_FUNC explicit inline DiagonalMatrix(DiagonalVectorType&& diag) : m_diagonal(std::move(diag)) {}
+    /** Copy constructor. */
+    template<typename OtherDerived>
+    inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
 
-  /** Copy constructor. */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
+    inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
+    #endif
 
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
-  inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
-#endif
+    /** generic constructor from expression of the diagonal coefficients */
+    template<typename OtherDerived>
+    explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
+    {}
 
-  /** generic constructor from expression of the diagonal coefficients */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other) {}
+    /** Copy operator. */
+    template<typename OtherDerived>
+    DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
+    {
+      m_diagonal = other.diagonal();
+      return *this;
+    }
 
-  /** Copy operator. */
-  template <typename OtherDerived>
-  EIGEN_DEVICE_FUNC DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other) {
-    m_diagonal = other.diagonal();
-    return *this;
-  }
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    DiagonalMatrix& operator=(const DiagonalMatrix& other)
+    {
+      m_diagonal = other.diagonal();
+      return *this;
+    }
+    #endif
 
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  /** This is a special case of the templated operator=. Its purpose is to
-   * prevent a default operator= from hiding the templated operator=.
-   */
-  EIGEN_DEVICE_FUNC DiagonalMatrix& operator=(const DiagonalMatrix& other) {
-    m_diagonal = other.diagonal();
-    return *this;
-  }
-#endif
-
-  typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, DiagonalVectorType>>
-      InitializeReturnType;
-
-  /** Initializes a diagonal matrix of size SizeAtCompileTime with coefficients set to zero */
-  EIGEN_DEVICE_FUNC static const InitializeReturnType Zero() { return DiagonalVectorType::Zero().asDiagonal(); }
-  /** Initializes a diagonal matrix of size dim with coefficients set to zero */
-  EIGEN_DEVICE_FUNC static const InitializeReturnType Zero(Index size) {
-    return DiagonalVectorType::Zero(size).asDiagonal();
-  }
-  /** Initializes a identity matrix of size SizeAtCompileTime */
-  EIGEN_DEVICE_FUNC static const InitializeReturnType Identity() { return DiagonalVectorType::Ones().asDiagonal(); }
-  /** Initializes a identity matrix of size dim */
-  EIGEN_DEVICE_FUNC static const InitializeReturnType Identity(Index size) {
-    return DiagonalVectorType::Ones(size).asDiagonal();
-  }
-
-  /** Resizes to given size. */
-  EIGEN_DEVICE_FUNC inline void resize(Index size) { m_diagonal.resize(size); }
-  /** Sets all coefficients to zero. */
-  EIGEN_DEVICE_FUNC inline void setZero() { m_diagonal.setZero(); }
-  /** Resizes and sets all coefficients to zero. */
-  EIGEN_DEVICE_FUNC inline void setZero(Index size) { m_diagonal.setZero(size); }
-  /** Sets this matrix to be the identity matrix of the current size. */
-  EIGEN_DEVICE_FUNC inline void setIdentity() { m_diagonal.setOnes(); }
-  /** Sets this matrix to be the identity matrix of the given size. */
-  EIGEN_DEVICE_FUNC inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
+    /** Resizes to given size. */
+    inline void resize(Index size) { m_diagonal.resize(size); }
+    /** Sets all coefficients to zero. */
+    inline void setZero() { m_diagonal.setZero(); }
+    /** Resizes and sets all coefficients to zero. */
+    inline void setZero(Index size) { m_diagonal.setZero(size); }
+    /** Sets this matrix to be the identity matrix of the current size. */
+    inline void setIdentity() { m_diagonal.setOnes(); }
+    /** Sets this matrix to be the identity matrix of the given size. */
+    inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
 };
 
 /** \class DiagonalWrapper
- * \ingroup Core_Module
- *
- * \brief Expression of a diagonal matrix
- *
- * \tparam DiagonalVectorType_ the type of the vector of diagonal coefficients
- *
- * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
- * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
- * and most of the time this is the only way that it is used.
- *
- * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
- */
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a diagonal matrix
+  *
+  * \param _DiagonalVectorType the type of the vector of diagonal coefficients
+  *
+  * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
+  * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
+  */
 
 namespace internal {
-template <typename DiagonalVectorType_>
-struct traits<DiagonalWrapper<DiagonalVectorType_>> {
-  typedef DiagonalVectorType_ DiagonalVectorType;
+template<typename _DiagonalVectorType>
+struct traits<DiagonalWrapper<_DiagonalVectorType> >
+{
+  typedef _DiagonalVectorType DiagonalVectorType;
   typedef typename DiagonalVectorType::Scalar Scalar;
-  typedef typename DiagonalVectorType::StorageIndex StorageIndex;
-  typedef DiagonalShape StorageKind;
-  typedef typename traits<DiagonalVectorType>::XprKind XprKind;
+  typedef typename DiagonalVectorType::Index Index;
+  typedef typename DiagonalVectorType::StorageKind StorageKind;
   enum {
     RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
     ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    Flags = (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
+    MaxRowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    MaxColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    Flags =  traits<DiagonalVectorType>::Flags & LvalueBit
   };
 };
-}  // namespace internal
+}
 
-template <typename DiagonalVectorType_>
-class DiagonalWrapper : public DiagonalBase<DiagonalWrapper<DiagonalVectorType_>>, internal::no_assignment_operator {
- public:
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  typedef DiagonalVectorType_ DiagonalVectorType;
-  typedef DiagonalWrapper Nested;
-#endif
+template<typename _DiagonalVectorType>
+class DiagonalWrapper
+  : public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
+{
+  public:
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef _DiagonalVectorType DiagonalVectorType;
+    typedef DiagonalWrapper Nested;
+    #endif
 
-  /** Constructor from expression of diagonal coefficients to wrap. */
-  EIGEN_DEVICE_FUNC explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
+    /** Constructor from expression of diagonal coefficients to wrap. */
+    inline DiagonalWrapper(const DiagonalVectorType& diagonal) : m_diagonal(diagonal) {}
 
-  /** \returns a const reference to the wrapped expression of diagonal coefficients. */
-  EIGEN_DEVICE_FUNC const DiagonalVectorType& diagonal() const { return m_diagonal; }
+    /** \returns a const reference to the wrapped expression of diagonal coefficients. */
+    const DiagonalVectorType& diagonal() const { return m_diagonal; }
 
- protected:
-  typename DiagonalVectorType::Nested m_diagonal;
+  protected:
+    const typename DiagonalVectorType::Nested m_diagonal;
 };
 
 /** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
- *
- * \only_for_vectors
- *
- * Example: \include MatrixBase_asDiagonal.cpp
- * Output: \verbinclude MatrixBase_asDiagonal.out
- *
- * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
- **/
-template <typename Derived>
-EIGEN_DEVICE_FUNC inline const DiagonalWrapper<const Derived> MatrixBase<Derived>::asDiagonal() const {
-  return DiagonalWrapper<const Derived>(derived());
+  *
+  * \only_for_vectors
+  *
+  * Example: \include MatrixBase_asDiagonal.cpp
+  * Output: \verbinclude MatrixBase_asDiagonal.out
+  *
+  * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
+  **/
+template<typename Derived>
+inline const DiagonalWrapper<const Derived>
+MatrixBase<Derived>::asDiagonal() const
+{
+  return derived();
 }
 
 /** \returns true if *this is approximately equal to a diagonal matrix,
- *          within the precision given by \a prec.
- *
- * Example: \include MatrixBase_isDiagonal.cpp
- * Output: \verbinclude MatrixBase_isDiagonal.out
- *
- * \sa asDiagonal()
- */
-template <typename Derived>
-bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const {
-  if (cols() != rows()) return false;
+  *          within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isDiagonal.cpp
+  * Output: \verbinclude MatrixBase_isDiagonal.out
+  *
+  * \sa asDiagonal()
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
+{
+  if(cols() != rows()) return false;
   RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
-  for (Index j = 0; j < cols(); ++j) {
-    RealScalar absOnDiagonal = numext::abs(coeff(j, j));
-    if (absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
+  for(Index j = 0; j < cols(); ++j)
+  {
+    RealScalar absOnDiagonal = internal::abs(coeff(j,j));
+    if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
   }
-  for (Index j = 0; j < cols(); ++j)
-    for (Index i = 0; i < j; ++i) {
-      if (!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
-      if (!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
+  for(Index j = 0; j < cols(); ++j)
+    for(Index i = 0; i < j; ++i)
+    {
+      if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
+      if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
     }
   return true;
 }
 
-namespace internal {
-
-template <>
-struct storage_kind_to_shape<DiagonalShape> {
-  typedef DiagonalShape Shape;
-};
-
-struct Diagonal2Dense {};
-
-template <>
-struct AssignmentKind<DenseShape, DiagonalShape> {
-  typedef Diagonal2Dense Kind;
-};
-
-// Diagonal matrix to Dense assignment
-template <typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense> {
-  static void run(DstXprType& dst, const SrcXprType& src,
-                  const internal::assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if ((dst.rows() != dstRows) || (dst.cols() != dstCols)) dst.resize(dstRows, dstCols);
-
-    dst.setZero();
-    dst.diagonal() = src.diagonal();
-  }
-
-  static void run(DstXprType& dst, const SrcXprType& src,
-                  const internal::add_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
-    dst.diagonal() += src.diagonal();
-  }
-
-  static void run(DstXprType& dst, const SrcXprType& src,
-                  const internal::sub_assign_op<typename DstXprType::Scalar, typename SrcXprType::Scalar>& /*func*/) {
-    dst.diagonal() -= src.diagonal();
-  }
-};
-
-}  // namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_DIAGONALMATRIX_H
+#endif // EIGEN_DIAGONALMATRIX_H

Eigen/src/Core/DiagonalProduct.h

@@ -4,27 +4,132 @@
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_DIAGONALPRODUCT_H
 #define EIGEN_DIAGONALPRODUCT_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
+namespace internal {
+template<typename MatrixType, typename DiagonalType, int ProductOrder>
+struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
+ : traits<MatrixType>
+{
+  typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
 
-namespace Eigen {
+    _StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
+    _PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
+                    ||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
+    _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
+    // FIXME currently we need same types, but in the future the next rule should be the one
+    //_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))),
+    _Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
 
-/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
- */
-template <typename Derived>
-template <typename DiagonalDerived>
-EIGEN_DEVICE_FUNC inline const Product<Derived, DiagonalDerived, LazyProduct> MatrixBase<Derived>::operator*(
-    const DiagonalBase<DiagonalDerived> &a_diagonal) const {
-  return Product<Derived, DiagonalDerived, LazyProduct>(derived(), a_diagonal.derived());
+    Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
+    CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
+  };
+};
 }
 
-}  // end namespace Eigen
+template<typename MatrixType, typename DiagonalType, int ProductOrder>
+class DiagonalProduct : internal::no_assignment_operator,
+                        public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
+{
+  public:
 
-#endif  // EIGEN_DIAGONALPRODUCT_H
+    typedef MatrixBase<DiagonalProduct> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct)
+
+    inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal)
+      : m_matrix(matrix), m_diagonal(diagonal)
+    {
+      eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
+    }
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+    const Scalar coeff(Index row, Index col) const
+    {
+      return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
+    }
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
+    {
+      enum {
+        StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
+      };
+      const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
+
+      return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
+        ((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
+       ||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
+    }
+
+  protected:
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
+    {
+      return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
+                     internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
+    }
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
+    {
+      enum {
+        InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
+        DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
+      };
+      return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
+                     m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
+    }
+
+    const typename MatrixType::Nested m_matrix;
+    const typename DiagonalType::Nested m_diagonal;
+};
+
+/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
+  */
+template<typename Derived>
+template<typename DiagonalDerived>
+inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
+MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &diagonal) const
+{
+  return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), diagonal.derived());
+}
+
+/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
+  */
+template<typename DiagonalDerived>
+template<typename MatrixDerived>
+inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
+DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
+{
+  return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
+}
+
+
+#endif // EIGEN_DIAGONALPRODUCT_H

Eigen/src/Core/Dot.h

@@ -3,266 +3,270 @@
 //
 // Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_DOT_H
 #define EIGEN_DOT_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 namespace internal {
 
-template <typename Derived, typename Scalar = typename traits<Derived>::Scalar>
-struct squared_norm_impl {
-  using Real = typename NumTraits<Scalar>::Real;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Real run(const Derived& a) {
-    Scalar result = a.unaryExpr(squared_norm_functor<Scalar>()).sum();
-    return numext::real(result) + numext::imag(result);
+// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
+// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
+// looking at the static assertions. Thus this is a trick to get better compile errors.
+template<typename T, typename U,
+// the NeedToTranspose condition here is taken straight from Assign.h
+         bool NeedToTranspose = T::IsVectorAtCompileTime
+                && U::IsVectorAtCompileTime
+                && ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
+                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
+                         // revert to || as soon as not needed anymore.
+                    (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
+>
+struct dot_nocheck
+{
+  typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
+  static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
+  {
+    return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
   }
 };
 
-template <typename Derived>
-struct squared_norm_impl<Derived, bool> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(const Derived& a) { return a.any(); }
+template<typename T, typename U>
+struct dot_nocheck<T, U, true>
+{
+  typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
+  static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
+  {
+    return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
+  }
 };
 
-}  // end namespace internal
+} // end namespace internal
 
-/** \fn MatrixBase::dot
- * \returns the dot product of *this with other.
- *
- * \only_for_vectors
- *
- * \note If the scalar type is complex numbers, then this function returns the hermitian
- * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
- * second variable.
- *
- * \sa squaredNorm(), norm()
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,
-                                  typename internal::traits<OtherDerived>::Scalar>::ReturnType
-    MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const {
-  return internal::dot_impl<Derived, OtherDerived>::run(derived(), other.derived());
+/** \returns the dot product of *this with other.
+  *
+  * \only_for_vectors
+  *
+  * \note If the scalar type is complex numbers, then this function returns the hermitian
+  * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
+  * second variable.
+  *
+  * \sa squaredNorm(), norm()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
+MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
+  EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
+
+  eigen_assert(size() == other.size());
+
+  return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
 }
 
+#ifdef EIGEN2_SUPPORT
+/** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable
+  * (conjugating the second variable). Of course this only makes a difference in the complex case.
+  *
+  * This method is only available in EIGEN2_SUPPORT mode.
+  *
+  * \only_for_vectors
+  *
+  * \sa dot()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+
+  return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this);
+}
+#endif
+
+
 //---------- implementation of L2 norm and related functions ----------
 
-/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
- * In both cases, it consists in the sum of the square of all the matrix entries.
- * For vectors, this is also equals to the dot product of \c *this with itself.
- *
- * \sa dot(), norm(), lpNorm()
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::squaredNorm() const {
-  return internal::squared_norm_impl<Derived>::run(derived());
+/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
+  * In both cases, it consists in the sum of the square of all the matrix entries.
+  * For vectors, this is also equals to the dot product of \c *this with itself.
+  *
+  * \sa dot(), norm()
+  */
+template<typename Derived>
+EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
+{
+  return internal::real((*this).cwiseAbs2().sum());
 }
 
 /** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
- * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
- * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
- *
- * \sa lpNorm(), dot(), squaredNorm()
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::norm() const {
-  return numext::sqrt(squaredNorm());
+  * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
+  * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
+  *
+  * \sa dot(), squaredNorm()
+  */
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
+{
+  return internal::sqrt(squaredNorm());
 }
 
-/** \returns an expression of the quotient of \c *this by its own norm.
- *
- * \warning If the input vector is too small (i.e., this->norm()==0),
- *          then this function returns a copy of the input.
- *
- * \only_for_vectors
- *
- * \sa norm(), normalize()
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject MatrixBase<Derived>::normalized()
-    const {
-  typedef typename internal::nested_eval<Derived, 2>::type Nested_;
-  Nested_ n(derived());
-  RealScalar z = n.squaredNorm();
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
-  if (z > RealScalar(0))
-    return n / numext::sqrt(z);
-  else
-    return n;
+/** \returns an expression of the quotient of *this by its own norm.
+  *
+  * \only_for_vectors
+  *
+  * \sa norm(), normalize()
+  */
+template<typename Derived>
+inline const typename MatrixBase<Derived>::PlainObject
+MatrixBase<Derived>::normalized() const
+{
+  typedef typename internal::nested<Derived>::type Nested;
+  typedef typename internal::remove_reference<Nested>::type _Nested;
+  _Nested n(derived());
+  return n / n.norm();
 }
 
 /** Normalizes the vector, i.e. divides it by its own norm.
- *
- * \only_for_vectors
- *
- * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
- *
- * \sa norm(), normalized()
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize() {
-  RealScalar z = squaredNorm();
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
-  if (z > RealScalar(0)) derived() /= numext::sqrt(z);
-}
-
-/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
- *
- * \only_for_vectors
- *
- * This method is analogue to the normalized() method, but it reduces the risk of
- * underflow and overflow when computing the norm.
- *
- * \warning If the input vector is too small (i.e., this->norm()==0),
- *          then this function returns a copy of the input.
- *
- * \sa stableNorm(), stableNormalize(), normalized()
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::stableNormalized() const {
-  typedef typename internal::nested_eval<Derived, 3>::type Nested_;
-  Nested_ n(derived());
-  RealScalar w = n.cwiseAbs().maxCoeff();
-  RealScalar z = (n / w).squaredNorm();
-  if (z > RealScalar(0))
-    return n / (numext::sqrt(z) * w);
-  else
-    return n;
-}
-
-/** Normalizes the vector while avoid underflow and overflow
- *
- * \only_for_vectors
- *
- * This method is analogue to the normalize() method, but it reduces the risk of
- * underflow and overflow when computing the norm.
- *
- * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
- *
- * \sa stableNorm(), stableNormalized(), normalize()
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize() {
-  RealScalar w = cwiseAbs().maxCoeff();
-  RealScalar z = (derived() / w).squaredNorm();
-  if (z > RealScalar(0)) derived() /= numext::sqrt(z) * w;
+  *
+  * \only_for_vectors
+  *
+  * \sa norm(), normalized()
+  */
+template<typename Derived>
+inline void MatrixBase<Derived>::normalize()
+{
+  *this /= norm();
 }
 
 //---------- implementation of other norms ----------
 
 namespace internal {
 
-template <typename Derived, int p>
-struct lpNorm_selector {
+template<typename Derived, int p>
+struct lpNorm_selector
+{
   typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC static inline RealScalar run(const MatrixBase<Derived>& m) {
-    EIGEN_USING_STD(pow)
-    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1) / p);
+  inline static RealScalar run(const MatrixBase<Derived>& m)
+  {
+    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
   }
 };
 
-template <typename Derived>
-struct lpNorm_selector<Derived, 1> {
-  EIGEN_DEVICE_FUNC static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(
-      const MatrixBase<Derived>& m) {
+template<typename Derived>
+struct lpNorm_selector<Derived, 1>
+{
+  inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
     return m.cwiseAbs().sum();
   }
 };
 
-template <typename Derived>
-struct lpNorm_selector<Derived, 2> {
-  EIGEN_DEVICE_FUNC static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(
-      const MatrixBase<Derived>& m) {
+template<typename Derived>
+struct lpNorm_selector<Derived, 2>
+{
+  inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
     return m.norm();
   }
 };
 
-template <typename Derived>
-struct lpNorm_selector<Derived, Infinity> {
-  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC static inline RealScalar run(const MatrixBase<Derived>& m) {
-    if (Derived::SizeAtCompileTime == 0 || (Derived::SizeAtCompileTime == Dynamic && m.size() == 0))
-      return RealScalar(0);
+template<typename Derived>
+struct lpNorm_selector<Derived, Infinity>
+{
+  inline static typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
     return m.cwiseAbs().maxCoeff();
   }
 };
 
-}  // end namespace internal
+} // end namespace internal
 
-/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the
- * p-th powers of the absolute values of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity,
- * this function returns the \f$ \ell^\infty \f$ norm, that is the maximum of the absolute values of the coefficients of
- * \c *this.
- *
- * In all cases, if \c *this is empty, then the value 0 is returned.
- *
- * \note For matrices, this function does not compute the <a
- * href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its
- * coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm
- * matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
- *
- * \sa norm()
- */
-template <typename Derived>
-template <int p>
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_DEVICE_FUNC inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-#else
-EIGEN_DEVICE_FUNC MatrixBase<Derived>::RealScalar
-#endif
-MatrixBase<Derived>::lpNorm() const {
+/** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
+  *          of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
+  *          norm, that is the maximum of the absolute values of the coefficients of *this.
+  *
+  * \sa norm()
+  */
+template<typename Derived>
+template<int p>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::lpNorm() const
+{
   return internal::lpNorm_selector<Derived, p>::run(*this);
 }
 
 //---------- implementation of isOrthogonal / isUnitary ----------
 
 /** \returns true if *this is approximately orthogonal to \a other,
- *          within the precision given by \a prec.
- *
- * Example: \include MatrixBase_isOrthogonal.cpp
- * Output: \verbinclude MatrixBase_isOrthogonal.out
- */
-template <typename Derived>
-template <typename OtherDerived>
-bool MatrixBase<Derived>::isOrthogonal(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const {
-  typename internal::nested_eval<Derived, 2>::type nested(derived());
-  typename internal::nested_eval<OtherDerived, 2>::type otherNested(other.derived());
-  return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
+  *          within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isOrthogonal.cpp
+  * Output: \verbinclude MatrixBase_isOrthogonal.out
+  */
+template<typename Derived>
+template<typename OtherDerived>
+bool MatrixBase<Derived>::isOrthogonal
+(const MatrixBase<OtherDerived>& other, RealScalar prec) const
+{
+  typename internal::nested<Derived,2>::type nested(derived());
+  typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
+  return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
 }
 
 /** \returns true if *this is approximately an unitary matrix,
- *          within the precision given by \a prec. In the case where the \a Scalar
- *          type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
- *
- * \note This can be used to check whether a family of vectors forms an orthonormal basis.
- *       Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
- *       orthonormal basis.
- *
- * Example: \include MatrixBase_isUnitary.cpp
- * Output: \verbinclude MatrixBase_isUnitary.out
- */
-template <typename Derived>
-bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const {
-  typename internal::nested_eval<Derived, 1>::type self(derived());
-  for (Index i = 0; i < cols(); ++i) {
-    if (!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec)) return false;
-    for (Index j = 0; j < i; ++j)
-      if (!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec)) return false;
+  *          within the precision given by \a prec. In the case where the \a Scalar
+  *          type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
+  *
+  * \note This can be used to check whether a family of vectors forms an orthonormal basis.
+  *       Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
+  *       orthonormal basis.
+  *
+  * Example: \include MatrixBase_isUnitary.cpp
+  * Output: \verbinclude MatrixBase_isUnitary.out
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
+{
+  typename Derived::Nested nested(derived());
+  for(Index i = 0; i < cols(); ++i)
+  {
+    if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
+      return false;
+    for(Index j = 0; j < i; ++j)
+      if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
+        return false;
   }
   return true;
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_DOT_H
+#endif // EIGEN_DOT_H

Eigen/src/Core/EigenBase.h

@@ -4,146 +4,169 @@
 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_EIGENBASE_H
 #define EIGEN_EIGENBASE_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
 
-namespace Eigen {
+/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
+  *
+  * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
+  *
+  * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
+  *
+  * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
+  *
+  * \sa \ref TopicClassHierarchy
+  */
+template<typename Derived> struct EigenBase
+{
+//   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
 
-/** \class EigenBase
- * \ingroup Core_Module
- *
- * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
- *
- * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
- *
- * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
- *
- * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
- *
- * \sa \blank \ref TopicClassHierarchy
- */
-template <typename Derived>
-struct EigenBase {
-  //   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
-
-  /** \brief The interface type of indices
-   * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
-   * \sa StorageIndex, \ref TopicPreprocessorDirectives.
-   * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
-   * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation
-   * attribute.
-   */
-  typedef Eigen::Index Index;
-
-  // FIXME is it needed?
   typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Index Index;
 
   /** \returns a reference to the derived object */
-  EIGEN_DEVICE_FUNC Derived& derived() { return *static_cast<Derived*>(this); }
+  Derived& derived() { return *static_cast<Derived*>(this); }
   /** \returns a const reference to the derived object */
-  EIGEN_DEVICE_FUNC const Derived& derived() const { return *static_cast<const Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
 
-  EIGEN_DEVICE_FUNC inline Derived& const_cast_derived() const {
-    return *static_cast<Derived*>(const_cast<EigenBase*>(this));
-  }
-  EIGEN_DEVICE_FUNC inline const Derived& const_derived() const { return *static_cast<const Derived*>(this); }
+  inline Derived& const_cast_derived() const
+  { return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
+  inline const Derived& const_derived() const
+  { return *static_cast<const Derived*>(this); }
 
   /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
+  inline Index rows() const { return derived().rows(); }
   /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
+  inline Index cols() const { return derived().cols(); }
   /** \returns the number of coefficients, which is rows()*cols().
-   * \sa rows(), cols(), SizeAtCompileTime. */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index size() const EIGEN_NOEXCEPT { return rows() * cols(); }
+    * \sa rows(), cols(), SizeAtCompileTime. */
+  inline Index size() const { return rows() * cols(); }
 
   /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
-  template <typename Dest>
-  EIGEN_DEVICE_FUNC inline void evalTo(Dest& dst) const {
-    derived().evalTo(dst);
-  }
+  template<typename Dest> inline void evalTo(Dest& dst) const
+  { derived().evalTo(dst); }
 
   /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
-  template <typename Dest>
-  EIGEN_DEVICE_FUNC inline void addTo(Dest& dst) const {
+  template<typename Dest> inline void addTo(Dest& dst) const
+  {
     // This is the default implementation,
     // derived class can reimplement it in a more optimized way.
-    typename Dest::PlainObject res(rows(), cols());
+    typename Dest::PlainObject res(rows(),cols());
     evalTo(res);
     dst += res;
   }
 
   /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
-  template <typename Dest>
-  EIGEN_DEVICE_FUNC inline void subTo(Dest& dst) const {
+  template<typename Dest> inline void subTo(Dest& dst) const
+  {
     // This is the default implementation,
     // derived class can reimplement it in a more optimized way.
-    typename Dest::PlainObject res(rows(), cols());
+    typename Dest::PlainObject res(rows(),cols());
     evalTo(res);
     dst -= res;
   }
 
   /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
-  template <typename Dest>
-  EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const {
+  template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
+  {
     // This is the default implementation,
     // derived class can reimplement it in a more optimized way.
     dst = dst * this->derived();
   }
 
   /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
-  template <typename Dest>
-  EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const {
+  template<typename Dest> inline void applyThisOnTheLeft(Dest& dst) const
+  {
     // This is the default implementation,
     // derived class can reimplement it in a more optimized way.
     dst = this->derived() * dst;
   }
 
-  template <typename Device>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<Derived, Device> device(Device& device);
-  template <typename Device>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceWrapper<const Derived, Device> device(Device& device) const;
 };
 
 /***************************************************************************
- * Implementation of matrix base methods
- ***************************************************************************/
+* Implementation of matrix base methods
+***************************************************************************/
 
 /** \brief Copies the generic expression \a other into *this.
- *
- * \details The expression must provide a (templated) evalTo(Derived& dst) const
- * function which does the actual job. In practice, this allows any user to write
- * its own special matrix without having to modify MatrixBase
- *
- * \returns a reference to *this.
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived());
+  *
+  * \details The expression must provide a (templated) evalTo(Derived& dst) const
+  * function which does the actual job. In practice, this allows any user to write
+  * its own special matrix without having to modify MatrixBase
+  *
+  * \returns a reference to *this.
+  */
+template<typename Derived>
+template<typename OtherDerived>
+Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
+{
+  other.derived().evalTo(derived());
   return derived();
 }
 
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+template<typename Derived>
+template<typename OtherDerived>
+Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
+{
+  other.derived().addTo(derived());
   return derived();
 }
 
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived>& other) {
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+template<typename Derived>
+template<typename OtherDerived>
+Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
+{
+  other.derived().subTo(derived());
   return derived();
 }
 
-}  // end namespace Eigen
+/** replaces \c *this by \c *this * \a other.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+inline Derived&
+MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
+{
+  other.derived().applyThisOnTheRight(derived());
+  return derived();
+}
 
-#endif  // EIGEN_EIGENBASE_H
+/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
+template<typename Derived>
+template<typename OtherDerived>
+inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
+{
+  other.derived().applyThisOnTheRight(derived());
+}
+
+/** replaces \c *this by \c *this * \a other. */
+template<typename Derived>
+template<typename OtherDerived>
+inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
+{
+  other.derived().applyThisOnTheLeft(derived());
+}
+
+#endif // EIGEN_EIGENBASE_H

Eigen/src/Core/Flagged.h

@@ -0,0 +1,151 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_FLAGGED_H
+#define EIGEN_FLAGGED_H
+
+/** \class Flagged
+  * \ingroup Core_Module
+  *
+  * \brief Expression with modified flags
+  *
+  * \param ExpressionType the type of the object of which we are modifying the flags
+  * \param Added the flags added to the expression
+  * \param Removed the flags removed from the expression (has priority over Added).
+  *
+  * This class represents an expression whose flags have been modified.
+  * It is the return type of MatrixBase::flagged()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::flagged()
+  */
+
+namespace internal {
+template<typename ExpressionType, unsigned int Added, unsigned int Removed>
+struct traits<Flagged<ExpressionType, Added, Removed> > : traits<ExpressionType>
+{
+  enum { Flags = (ExpressionType::Flags | Added) & ~Removed };
+};
+}
+
+template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged
+  : public MatrixBase<Flagged<ExpressionType, Added, Removed> >
+{
+  public:
+
+    typedef MatrixBase<Flagged> Base;
+    
+    EIGEN_DENSE_PUBLIC_INTERFACE(Flagged)
+    typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
+        ExpressionType, const ExpressionType&>::type ExpressionTypeNested;
+    typedef typename ExpressionType::InnerIterator InnerIterator;
+
+    inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {}
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+    inline Index outerStride() const { return m_matrix.outerStride(); }
+    inline Index innerStride() const { return m_matrix.innerStride(); }
+
+    inline CoeffReturnType coeff(Index row, Index col) const
+    {
+      return m_matrix.coeff(row, col);
+    }
+
+    inline CoeffReturnType coeff(Index index) const
+    {
+      return m_matrix.coeff(index);
+    }
+    
+    inline const Scalar& coeffRef(Index row, Index col) const
+    {
+      return m_matrix.const_cast_derived().coeffRef(row, col);
+    }
+
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_matrix.const_cast_derived().coeffRef(index);
+    }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_matrix.const_cast_derived().coeffRef(row, col);
+    }
+
+    inline Scalar& coeffRef(Index index)
+    {
+      return m_matrix.const_cast_derived().coeffRef(index);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index row, Index col) const
+    {
+      return m_matrix.template packet<LoadMode>(row, col);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
+    {
+      m_matrix.const_cast_derived().template writePacket<LoadMode>(row, col, x);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index index) const
+    {
+      return m_matrix.template packet<LoadMode>(index);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index index, const PacketScalar& x)
+    {
+      m_matrix.const_cast_derived().template writePacket<LoadMode>(index, x);
+    }
+
+    const ExpressionType& _expression() const { return m_matrix; }
+
+    template<typename OtherDerived>
+    typename ExpressionType::PlainObject solveTriangular(const MatrixBase<OtherDerived>& other) const;
+
+    template<typename OtherDerived>
+    void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const;
+
+  protected:
+    ExpressionTypeNested m_matrix;
+};
+
+/** \returns an expression of *this with added and removed flags
+  *
+  * This is mostly for internal use.
+  *
+  * \sa class Flagged
+  */
+template<typename Derived>
+template<unsigned int Added,unsigned int Removed>
+inline const Flagged<Derived, Added, Removed>
+DenseBase<Derived>::flagged() const
+{
+  return derived();
+}
+
+#endif // EIGEN_FLAGGED_H

Eigen/src/Core/ForceAlignedAccess.h

@@ -3,129 +3,155 @@
 //
 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_FORCEALIGNEDACCESS_H
 #define EIGEN_FORCEALIGNEDACCESS_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
 /** \class ForceAlignedAccess
- * \ingroup Core_Module
- *
- * \brief Enforce aligned packet loads and stores regardless of what is requested
- *
- * \param ExpressionType the type of the object of which we are forcing aligned packet access
- *
- * This class is the return type of MatrixBase::forceAlignedAccess()
- * and most of the time this is the only way it is used.
- *
- * \sa MatrixBase::forceAlignedAccess()
- */
+  * \ingroup Core_Module
+  *
+  * \brief Enforce aligned packet loads and stores regardless of what is requested
+  *
+  * \param ExpressionType the type of the object of which we are forcing aligned packet access
+  *
+  * This class is the return type of MatrixBase::forceAlignedAccess()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::forceAlignedAccess()
+  */
 
 namespace internal {
-template <typename ExpressionType>
-struct traits<ForceAlignedAccess<ExpressionType>> : public traits<ExpressionType> {};
-}  // namespace internal
+template<typename ExpressionType>
+struct traits<ForceAlignedAccess<ExpressionType> > : public traits<ExpressionType>
+{};
+}
 
-template <typename ExpressionType>
-class ForceAlignedAccess : public internal::dense_xpr_base<ForceAlignedAccess<ExpressionType>>::type {
- public:
-  typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
+template<typename ExpressionType> class ForceAlignedAccess
+  : public internal::dense_xpr_base< ForceAlignedAccess<ExpressionType> >::type
+{
+  public:
 
-  EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
+    typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
 
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const EIGEN_NOEXCEPT {
-    return m_expression.outerStride();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const EIGEN_NOEXCEPT {
-    return m_expression.innerStride();
-  }
+    inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
 
-  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const {
-    return m_expression.coeff(row, col);
-  }
+    inline Index rows() const { return m_expression.rows(); }
+    inline Index cols() const { return m_expression.cols(); }
+    inline Index outerStride() const { return m_expression.outerStride(); }
+    inline Index innerStride() const { return m_expression.innerStride(); }
 
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col) {
-    return m_expression.const_cast_derived().coeffRef(row, col);
-  }
+    inline const CoeffReturnType coeff(Index row, Index col) const
+    {
+      return m_expression.coeff(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const { return m_expression.coeff(index); }
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_expression.const_cast_derived().coeffRef(row, col);
+    }
 
-  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) { return m_expression.const_cast_derived().coeffRef(index); }
+    inline const CoeffReturnType coeff(Index index) const
+    {
+      return m_expression.coeff(index);
+    }
 
-  template <int LoadMode>
-  inline const PacketScalar packet(Index row, Index col) const {
-    return m_expression.template packet<Aligned>(row, col);
-  }
+    inline Scalar& coeffRef(Index index)
+    {
+      return m_expression.const_cast_derived().coeffRef(index);
+    }
 
-  template <int LoadMode>
-  inline void writePacket(Index row, Index col, const PacketScalar& x) {
-    m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
-  }
+    template<int LoadMode>
+    inline const PacketScalar packet(Index row, Index col) const
+    {
+      return m_expression.template packet<Aligned>(row, col);
+    }
 
-  template <int LoadMode>
-  inline const PacketScalar packet(Index index) const {
-    return m_expression.template packet<Aligned>(index);
-  }
+    template<int LoadMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
+    }
 
-  template <int LoadMode>
-  inline void writePacket(Index index, const PacketScalar& x) {
-    m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
-  }
+    template<int LoadMode>
+    inline const PacketScalar packet(Index index) const
+    {
+      return m_expression.template packet<Aligned>(index);
+    }
 
-  EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
+    template<int LoadMode>
+    inline void writePacket(Index index, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
+    }
 
- protected:
-  const ExpressionType& m_expression;
+    operator const ExpressionType&() const { return m_expression; }
 
- private:
-  ForceAlignedAccess& operator=(const ForceAlignedAccess&);
+  protected:
+    const ExpressionType& m_expression;
+
+  private:
+    ForceAlignedAccess& operator=(const ForceAlignedAccess&);
 };
 
 /** \returns an expression of *this with forced aligned access
- * \sa forceAlignedAccessIf(),class ForceAlignedAccess
- */
-template <typename Derived>
-inline const ForceAlignedAccess<Derived> MatrixBase<Derived>::forceAlignedAccess() const {
+  * \sa forceAlignedAccessIf(),class ForceAlignedAccess
+  */
+template<typename Derived>
+inline const ForceAlignedAccess<Derived>
+MatrixBase<Derived>::forceAlignedAccess() const
+{
   return ForceAlignedAccess<Derived>(derived());
 }
 
 /** \returns an expression of *this with forced aligned access
- * \sa forceAlignedAccessIf(), class ForceAlignedAccess
- */
-template <typename Derived>
-inline ForceAlignedAccess<Derived> MatrixBase<Derived>::forceAlignedAccess() {
+  * \sa forceAlignedAccessIf(), class ForceAlignedAccess
+  */
+template<typename Derived>
+inline ForceAlignedAccess<Derived>
+MatrixBase<Derived>::forceAlignedAccess()
+{
   return ForceAlignedAccess<Derived>(derived());
 }
 
 /** \returns an expression of *this with forced aligned access if \a Enable is true.
- * \sa forceAlignedAccess(), class ForceAlignedAccess
- */
-template <typename Derived>
-template <bool Enable>
-inline add_const_on_value_type_t<std::conditional_t<Enable, ForceAlignedAccess<Derived>, Derived&>>
-MatrixBase<Derived>::forceAlignedAccessIf() const {
-  return derived();  // FIXME This should not work but apparently is never used
+  * \sa forceAlignedAccess(), class ForceAlignedAccess
+  */
+template<typename Derived>
+template<bool Enable>
+inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
+MatrixBase<Derived>::forceAlignedAccessIf() const
+{
+  return derived();
 }
 
 /** \returns an expression of *this with forced aligned access if \a Enable is true.
- * \sa forceAlignedAccess(), class ForceAlignedAccess
- */
-template <typename Derived>
-template <bool Enable>
-inline std::conditional_t<Enable, ForceAlignedAccess<Derived>, Derived&> MatrixBase<Derived>::forceAlignedAccessIf() {
-  return derived();  // FIXME This should not work but apparently is never used
+  * \sa forceAlignedAccess(), class ForceAlignedAccess
+  */
+template<typename Derived>
+template<bool Enable>
+inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
+MatrixBase<Derived>::forceAlignedAccessIf()
+{
+  return derived();
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_FORCEALIGNEDACCESS_H
+#endif // EIGEN_FORCEALIGNEDACCESS_H

Eigen/src/Core/Functors.h

@@ -0,0 +1,942 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_FUNCTORS_H
+#define EIGEN_FUNCTORS_H
+
+namespace internal {
+
+// associative functors:
+
+/** \internal
+  * \brief Template functor to compute the sum of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, MatrixBase::sum()
+  */
+template<typename Scalar> struct scalar_sum_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::padd(a,b); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
+  { return internal::predux(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_sum_op<Scalar> > {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasAdd
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the product of two scalars
+  *
+  * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
+  */
+template<typename LhsScalar,typename RhsScalar> struct scalar_product_op {
+  enum {
+    // TODO vectorize mixed product
+    Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
+  };
+  typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
+  EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::pmul(a,b); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
+  { return internal::predux_mul(a); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate!
+    PacketAccess = scalar_product_op<LhsScalar,RhsScalar>::Vectorizable
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the conjugate product of two scalars
+  *
+  * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
+  */
+template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op {
+
+  enum {
+    Conj = NumTraits<LhsScalar>::IsComplex
+  };
+  
+  typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
+  
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
+  EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
+  { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); }
+  
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = NumTraits<LhsScalar>::MulCost,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the min of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
+  */
+template<typename Scalar> struct scalar_min_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return (min)(a, b); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::pmin(a,b); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
+  { return internal::predux_min(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_min_op<Scalar> > {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasMin
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the max of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
+  */
+template<typename Scalar> struct scalar_max_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return (max)(a, b); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::pmax(a,b); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
+  { return internal::predux_max(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_max_op<Scalar> > {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasMax
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the hypot of two scalars
+  *
+  * \sa MatrixBase::stableNorm(), class Redux
+  */
+template<typename Scalar> struct scalar_hypot_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
+//   typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const
+  {
+    using std::max;
+    using std::min;
+    Scalar p = (max)(_x, _y);
+    Scalar q = (min)(_x, _y);
+    Scalar qp = q/p;
+    return p * sqrt(Scalar(1) + qp*qp);
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_hypot_op<Scalar> > {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess=0 };
+};
+
+// other binary functors:
+
+/** \internal
+  * \brief Template functor to compute the difference of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::operator-
+  */
+template<typename Scalar> struct scalar_difference_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::psub(a,b); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_difference_op<Scalar> > {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasSub
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the quotient of two scalars
+  *
+  * \sa class CwiseBinaryOp, Cwise::operator/()
+  */
+template<typename Scalar> struct scalar_quotient_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a / b; }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::pdiv(a,b); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_quotient_op<Scalar> > {
+  enum {
+    Cost = 2 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasDiv
+  };
+};
+
+// unary functors:
+
+/** \internal
+  * \brief Template functor to compute the opposite of a scalar
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::operator-
+  */
+template<typename Scalar> struct scalar_opposite_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pnegate(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_opposite_op<Scalar> >
+{ enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasNegate };
+};
+
+/** \internal
+  * \brief Template functor to compute the absolute value of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::abs
+  */
+template<typename Scalar> struct scalar_abs_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op)
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs(a); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pabs(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_abs_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasAbs
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the squared absolute value of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::abs2
+  */
+template<typename Scalar> struct scalar_abs2_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op)
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs2(a); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a,a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_abs2_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; };
+
+/** \internal
+  * \brief Template functor to compute the conjugate of a complex value
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::conjugate()
+  */
+template<typename Scalar> struct scalar_conjugate_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op)
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return conj(a); }
+  template<typename Packet>
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_conjugate_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::IsComplex ? NumTraits<Scalar>::AddCost : 0,
+    PacketAccess = packet_traits<Scalar>::HasConj
+  };
+};
+
+/** \internal
+  * \brief Template functor to cast a scalar to another type
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::cast()
+  */
+template<typename Scalar, typename NewType>
+struct scalar_cast_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
+  typedef NewType result_type;
+  EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); }
+};
+template<typename Scalar, typename NewType>
+struct functor_traits<scalar_cast_op<Scalar,NewType> >
+{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the real part of a complex
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::real()
+  */
+template<typename Scalar>
+struct scalar_real_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op)
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return real(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_real_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the imaginary part of a complex
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::imag()
+  */
+template<typename Scalar>
+struct scalar_imag_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op)
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return imag(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_imag_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the real part of a complex as a reference
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::real()
+  */
+template<typename Scalar>
+struct scalar_real_ref_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op)
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return real_ref(*const_cast<Scalar*>(&a)); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_real_ref_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the imaginary part of a complex as a reference
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::imag()
+  */
+template<typename Scalar>
+struct scalar_imag_ref_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op)
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return imag_ref(*const_cast<Scalar*>(&a)); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_imag_ref_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  *
+  * \brief Template functor to compute the exponential of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::exp()
+  */
+template<typename Scalar> struct scalar_exp_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op)
+  inline const Scalar operator() (const Scalar& a) const { return exp(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_exp_op<Scalar> >
+{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasExp }; };
+
+/** \internal
+  *
+  * \brief Template functor to compute the logarithm of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::log()
+  */
+template<typename Scalar> struct scalar_log_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op)
+  inline const Scalar operator() (const Scalar& a) const { return log(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_log_op<Scalar> >
+{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; };
+
+/** \internal
+  * \brief Template functor to multiply a scalar by a fixed other one
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/
+  */
+/* NOTE why doing the pset1() in packetOp *is* an optimization ?
+ * indeed it seems better to declare m_other as a Packet and do the pset1() once
+ * in the constructor. However, in practice:
+ *  - GCC does not like m_other as a Packet and generate a load every time it needs it
+ *  - on the other hand GCC is able to moves the pset1() away the loop :)
+ *  - simpler code ;)
+ * (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y)
+ */
+template<typename Scalar>
+struct scalar_multiple_op {
+  typedef typename packet_traits<Scalar>::type Packet;
+  // FIXME default copy constructors seems bugged with std::complex<>
+  EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { }
+  EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a, pset1<Packet>(m_other)); }
+  typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
+};
+template<typename Scalar>
+struct functor_traits<scalar_multiple_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
+
+template<typename Scalar1, typename Scalar2>
+struct scalar_multiple2_op {
+  typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type;
+  EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { }
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; }
+  typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other;
+};
+template<typename Scalar1,typename Scalar2>
+struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> >
+{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
+
+template<typename Scalar, bool IsInteger>
+struct scalar_quotient1_impl {
+  typedef typename packet_traits<Scalar>::type Packet;
+  // FIXME default copy constructors seems bugged with std::complex<>
+  EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(static_cast<Scalar>(1) / other) {}
+  EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
+  EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a, pset1<Packet>(m_other)); }
+  const Scalar m_other;
+};
+template<typename Scalar>
+struct functor_traits<scalar_quotient1_impl<Scalar,false> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
+
+template<typename Scalar>
+struct scalar_quotient1_impl<Scalar,true> {
+  // FIXME default copy constructors seems bugged with std::complex<>
+  EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(other) {}
+  EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
+  typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
+};
+template<typename Scalar>
+struct functor_traits<scalar_quotient1_impl<Scalar,true> >
+{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to divide a scalar by a fixed other one
+  *
+  * This functor is used to implement the quotient of a matrix by
+  * a scalar where the scalar type is not necessarily a floating point type.
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::operator/
+  */
+template<typename Scalar>
+struct scalar_quotient1_op : scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger > {
+  EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other)
+    : scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger >(other) {}
+};
+template<typename Scalar>
+struct functor_traits<scalar_quotient1_op<Scalar> >
+: functor_traits<scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger> >
+{};
+
+// nullary functors
+
+template<typename Scalar>
+struct scalar_constant_op {
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { }
+  EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { }
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { return m_other; }
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Packet packetOp(Index, Index = 0) const { return internal::pset1<Packet>(m_other); }
+  const Scalar m_other;
+};
+template<typename Scalar>
+struct functor_traits<scalar_constant_op<Scalar> >
+// FIXME replace this packet test by a safe one
+{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
+
+template<typename Scalar> struct scalar_identity_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op)
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { return row==col ? Scalar(1) : Scalar(0); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_identity_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
+
+template <typename Scalar, bool RandomAccess> struct linspaced_op_impl;
+
+// linear access for packet ops:
+// 1) initialization
+//   base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0])
+// 2) each step
+//   base += [size*step, ..., size*step]
+template <typename Scalar>
+struct linspaced_op_impl<Scalar,false>
+{
+  typedef typename packet_traits<Scalar>::type Packet;
+
+  linspaced_op_impl(Scalar low, Scalar step) :
+  m_low(low), m_step(step),
+  m_packetStep(pset1<Packet>(packet_traits<Scalar>::size*step)),
+  m_base(padd(pset1<Packet>(low),pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {}
+
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); }
+
+  const Scalar m_low;
+  const Scalar m_step;
+  const Packet m_packetStep;
+  mutable Packet m_base;
+};
+
+// random access for packet ops:
+// 1) each step
+//   [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
+template <typename Scalar>
+struct linspaced_op_impl<Scalar,true>
+{
+  typedef typename packet_traits<Scalar>::type Packet;
+
+  linspaced_op_impl(Scalar low, Scalar step) :
+  m_low(low), m_step(step),
+  m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Scalar>(0)) {}
+
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
+
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Packet packetOp(Index i) const
+  { return internal::padd(m_lowPacket, pmul(m_stepPacket, padd(pset1<Packet>(i),m_interPacket))); }
+
+  const Scalar m_low;
+  const Scalar m_step;
+  const Packet m_lowPacket;
+  const Packet m_stepPacket;
+  const Packet m_interPacket;
+};
+
+// ----- Linspace functor ----------------------------------------------------------------
+
+// Forward declaration (we default to random access which does not really give
+// us a speed gain when using packet access but it allows to use the functor in
+// nested expressions).
+template <typename Scalar, bool RandomAccess = true> struct linspaced_op;
+template <typename Scalar, bool RandomAccess> struct functor_traits< linspaced_op<Scalar,RandomAccess> >
+{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::HasSetLinear, IsRepeatable = true }; };
+template <typename Scalar, bool RandomAccess> struct linspaced_op
+{
+  typedef typename packet_traits<Scalar>::type Packet;
+  linspaced_op(Scalar low, Scalar high, int num_steps) : impl(low, (high-low)/(num_steps-1)) {}
+
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); }
+
+  // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since
+  // there row==0 and col is used for the actual iteration.
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const 
+  {
+    eigen_assert(col==0 || row==0);
+    return impl(col + row);
+  }
+
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return impl.packetOp(i); }
+
+  // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since
+  // there row==0 and col is used for the actual iteration.
+  template<typename Index>
+  EIGEN_STRONG_INLINE const Packet packetOp(Index row, Index col) const
+  {
+    eigen_assert(col==0 || row==0);
+    return impl.packetOp(col + row);
+  }
+
+  // This proxy object handles the actual required temporaries, the different
+  // implementations (random vs. sequential access) as well as the
+  // correct piping to size 2/4 packet operations.
+  const linspaced_op_impl<Scalar,RandomAccess> impl;
+};
+
+// all functors allow linear access, except scalar_identity_op. So we fix here a quick meta
+// to indicate whether a functor allows linear access, just always answering 'yes' except for
+// scalar_identity_op.
+// FIXME move this to functor_traits adding a functor_default
+template<typename Functor> struct functor_has_linear_access { enum { ret = 1 }; };
+template<typename Scalar> struct functor_has_linear_access<scalar_identity_op<Scalar> > { enum { ret = 0 }; };
+
+// in CwiseBinaryOp, we require the Lhs and Rhs to have the same scalar type, except for multiplication
+// where we only require them to have the same _real_ scalar type so one may multiply, say, float by complex<float>.
+// FIXME move this to functor_traits adding a functor_default
+template<typename Functor> struct functor_allows_mixing_real_and_complex { enum { ret = 0 }; };
+template<typename LhsScalar,typename RhsScalar> struct functor_allows_mixing_real_and_complex<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
+template<typename LhsScalar,typename RhsScalar> struct functor_allows_mixing_real_and_complex<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
+
+
+/** \internal
+  * \brief Template functor to add a scalar to a fixed other one
+  * \sa class CwiseUnaryOp, Array::operator+
+  */
+/* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */
+template<typename Scalar>
+struct scalar_add_op {
+  typedef typename packet_traits<Scalar>::type Packet;
+  // FIXME default copy constructors seems bugged with std::complex<>
+  inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { }
+  inline scalar_add_op(const Scalar& other) : m_other(other) { }
+  inline Scalar operator() (const Scalar& a) const { return a + m_other; }
+  inline const Packet packetOp(const Packet& a) const
+  { return internal::padd(a, pset1<Packet>(m_other)); }
+  const Scalar m_other;
+};
+template<typename Scalar>
+struct functor_traits<scalar_add_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
+
+/** \internal
+  * \brief Template functor to compute the square root of a scalar
+  * \sa class CwiseUnaryOp, Cwise::sqrt()
+  */
+template<typename Scalar> struct scalar_sqrt_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
+  inline const Scalar operator() (const Scalar& a) const { return sqrt(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_sqrt_op<Scalar> >
+{ enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasSqrt
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the cosine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::cos()
+  */
+template<typename Scalar> struct scalar_cos_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op)
+  inline Scalar operator() (const Scalar& a) const { return cos(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_cos_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasCos
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the sine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::sin()
+  */
+template<typename Scalar> struct scalar_sin_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op)
+  inline const Scalar operator() (const Scalar& a) const { return sin(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_sin_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasSin
+  };
+};
+
+
+/** \internal
+  * \brief Template functor to compute the tan of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::tan()
+  */
+template<typename Scalar> struct scalar_tan_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op)
+  inline const Scalar operator() (const Scalar& a) const { return tan(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_tan_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasTan
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the arc cosine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::acos()
+  */
+template<typename Scalar> struct scalar_acos_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op)
+  inline const Scalar operator() (const Scalar& a) const { return acos(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_acos_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasACos
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the arc sine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::asin()
+  */
+template<typename Scalar> struct scalar_asin_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op)
+  inline const Scalar operator() (const Scalar& a) const { return asin(a); }
+  typedef typename packet_traits<Scalar>::type Packet;
+  inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_asin_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasASin
+  };
+};
+
+/** \internal
+  * \brief Template functor to raise a scalar to a power
+  * \sa class CwiseUnaryOp, Cwise::pow
+  */
+template<typename Scalar>
+struct scalar_pow_op {
+  // FIXME default copy constructors seems bugged with std::complex<>
+  inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { }
+  inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
+  inline Scalar operator() (const Scalar& a) const { return internal::pow(a, m_exponent); }
+  const Scalar m_exponent;
+};
+template<typename Scalar>
+struct functor_traits<scalar_pow_op<Scalar> >
+{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to compute the inverse of a scalar
+  * \sa class CwiseUnaryOp, Cwise::inverse()
+  */
+template<typename Scalar>
+struct scalar_inverse_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op)
+  inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; }
+  template<typename Packet>
+  inline const Packet packetOp(const Packet& a) const
+  { return internal::pdiv(pset1<Packet>(Scalar(1)),a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_inverse_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; };
+
+/** \internal
+  * \brief Template functor to compute the square of a scalar
+  * \sa class CwiseUnaryOp, Cwise::square()
+  */
+template<typename Scalar>
+struct scalar_square_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
+  inline Scalar operator() (const Scalar& a) const { return a*a; }
+  template<typename Packet>
+  inline const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a,a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_square_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
+
+/** \internal
+  * \brief Template functor to compute the cube of a scalar
+  * \sa class CwiseUnaryOp, Cwise::cube()
+  */
+template<typename Scalar>
+struct scalar_cube_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
+  inline Scalar operator() (const Scalar& a) const { return a*a*a; }
+  template<typename Packet>
+  inline const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a,pmul(a,a)); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_cube_op<Scalar> >
+{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
+
+// default functor traits for STL functors:
+
+template<typename T>
+struct functor_traits<std::multiplies<T> >
+{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::divides<T> >
+{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::plus<T> >
+{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::minus<T> >
+{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::negate<T> >
+{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::logical_or<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::logical_and<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::logical_not<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::greater<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::less<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::greater_equal<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::less_equal<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::equal_to<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::not_equal_to<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::binder2nd<T> >
+{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::binder1st<T> >
+{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::unary_negate<T> >
+{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::binary_negate<T> >
+{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
+
+#ifdef EIGEN_STDEXT_SUPPORT
+
+template<typename T0,typename T1>
+struct functor_traits<std::project1st<T0,T1> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::project2nd<T0,T1> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::select2nd<std::pair<T0,T1> > >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::select1st<std::pair<T0,T1> > >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::unary_compose<T0,T1> >
+{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; };
+
+template<typename T0,typename T1,typename T2>
+struct functor_traits<std::binary_compose<T0,T1,T2> >
+{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; };
+
+#endif // EIGEN_STDEXT_SUPPORT
+
+// allow to add new functors and specializations of functor_traits from outside Eigen.
+// this macro is really needed because functor_traits must be specialized after it is declared but before it is used...
+#ifdef EIGEN_FUNCTORS_PLUGIN
+#include EIGEN_FUNCTORS_PLUGIN
+#endif
+
+} // end namespace internal
+
+#endif // EIGEN_FUNCTORS_H

Eigen/src/Core/Fuzzy.h

@@ -4,129 +4,158 @@
 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_FUZZY_H
 #define EIGEN_FUZZY_H
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
+namespace internal
+{
 
-namespace Eigen {
-
-namespace internal {
-
-template <typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isApprox_selector {
-  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) {
-    typename internal::nested_eval<Derived, 2>::type nested(x);
-    typename internal::nested_eval<OtherDerived, 2>::type otherNested(y);
-    return (nested.matrix() - otherNested.matrix()).cwiseAbs2().sum() <=
-           prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
+template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isApprox_selector
+{
+  static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
+  {
+    using std::min;
+    const typename internal::nested<Derived,2>::type nested(x);
+    const typename internal::nested<OtherDerived,2>::type otherNested(y);
+    return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
   }
 };
 
-template <typename Derived, typename OtherDerived>
-struct isApprox_selector<Derived, OtherDerived, true> {
-  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&) {
+template<typename Derived, typename OtherDerived>
+struct isApprox_selector<Derived, OtherDerived, true>
+{
+  static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar)
+  {
     return x.matrix() == y.matrix();
   }
 };
 
-template <typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isMuchSmallerThan_object_selector {
-  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) {
-    return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
+template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isMuchSmallerThan_object_selector
+{
+  static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
+  {
+    return x.cwiseAbs2().sum() <= abs2(prec) * y.cwiseAbs2().sum();
   }
 };
 
-template <typename Derived, typename OtherDerived>
-struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true> {
-  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&) {
+template<typename Derived, typename OtherDerived>
+struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
+{
+  static bool run(const Derived& x, const OtherDerived&, typename Derived::RealScalar)
+  {
     return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
   }
 };
 
-template <typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isMuchSmallerThan_scalar_selector {
-  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const typename Derived::RealScalar& y,
-                                    const typename Derived::RealScalar& prec) {
-    return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
+template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isMuchSmallerThan_scalar_selector
+{
+  static bool run(const Derived& x, const typename Derived::RealScalar& y, typename Derived::RealScalar prec)
+  {
+    return x.cwiseAbs2().sum() <= abs2(prec * y);
   }
 };
 
-template <typename Derived>
-struct isMuchSmallerThan_scalar_selector<Derived, true> {
-  EIGEN_DEVICE_FUNC static bool run(const Derived& x, const typename Derived::RealScalar&,
-                                    const typename Derived::RealScalar&) {
+template<typename Derived>
+struct isMuchSmallerThan_scalar_selector<Derived, true>
+{
+  static bool run(const Derived& x, const typename Derived::RealScalar&, typename Derived::RealScalar)
+  {
     return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
   }
 };
 
-}  // end namespace internal
+} // end namespace internal
+
 
 /** \returns \c true if \c *this is approximately equal to \a other, within the precision
- * determined by \a prec.
- *
- * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
- * are considered to be approximately equal within precision \f$ p \f$ if
- * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
- * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
- * L2 norm).
- *
- * \note Because of the multiplicativeness of this comparison, one can't use this function
- * to check whether \c *this is approximately equal to the zero matrix or vector.
- * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
- * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
- * RealScalar&, RealScalar) instead.
- *
- * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApprox(const DenseBase<OtherDerived>& other,
-                                                    const RealScalar& prec) const {
+  * determined by \a prec.
+  *
+  * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
+  * are considered to be approximately equal within precision \f$ p \f$ if
+  * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
+  * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
+  * L2 norm).
+  *
+  * \note Because of the multiplicativeness of this comparison, one can't use this function
+  * to check whether \c *this is approximately equal to the zero matrix or vector.
+  * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
+  * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
+  * RealScalar&, RealScalar) instead.
+  *
+  * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
+  */
+template<typename Derived>
+template<typename OtherDerived>
+bool DenseBase<Derived>::isApprox(
+  const DenseBase<OtherDerived>& other,
+  RealScalar prec
+) const
+{
   return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
 }
 
 /** \returns \c true if the norm of \c *this is much smaller than \a other,
- * within the precision determined by \a prec.
- *
- * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
- * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
- * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
- *
- * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason,
- * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
- * of a reference matrix of same dimensions.
- *
- * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
- */
-template <typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(const typename NumTraits<Scalar>::Real& other,
-                                                             const RealScalar& prec) const {
+  * within the precision determined by \a prec.
+  *
+  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
+  * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
+  * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
+  *
+  * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason,
+  * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
+  * of a reference matrix of same dimensions.
+  *
+  * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
+  */
+template<typename Derived>
+bool DenseBase<Derived>::isMuchSmallerThan(
+  const typename NumTraits<Scalar>::Real& other,
+  RealScalar prec
+) const
+{
   return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
 }
 
 /** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
- * within the precision determined by \a prec.
- *
- * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
- * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
- * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
- * For matrices, the comparison is done using the Hilbert-Schmidt norm.
- *
- * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(const DenseBase<OtherDerived>& other,
-                                                             const RealScalar& prec) const {
+  * within the precision determined by \a prec.
+  *
+  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
+  * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
+  * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
+  * For matrices, the comparison is done using the Hilbert-Schmidt norm.
+  *
+  * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
+  */
+template<typename Derived>
+template<typename OtherDerived>
+bool DenseBase<Derived>::isMuchSmallerThan(
+  const DenseBase<OtherDerived>& other,
+  RealScalar prec
+) const
+{
   return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
 }
 
-}  // end namespace Eigen
-
-#endif  // EIGEN_FUZZY_H
+#endif // EIGEN_FUZZY_H

Eigen/src/Core/GeneralProduct.h

@@ -1,517 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_PRODUCT_H
-#define EIGEN_GENERAL_PRODUCT_H
-
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
-
-namespace Eigen {
-
-enum { Large = 2, Small = 3 };
-
-// Define the threshold value to fallback from the generic matrix-matrix product
-// implementation (heavy) to the lightweight coeff-based product one.
-// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
-// in products/GeneralMatrixMatrix.h for more details.
-// TODO This threshold should also be used in the compile-time selector below.
-#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
-// This default value has been obtained on a Haswell architecture.
-#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20
-#endif
-
-namespace internal {
-
-template <int Rows, int Cols, int Depth>
-struct product_type_selector;
-
-template <int Size, int MaxSize>
-struct product_size_category {
-  enum {
-#ifndef EIGEN_GPU_COMPILE_PHASE
-    is_large = MaxSize == Dynamic || Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
-               (Size == Dynamic && MaxSize >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
-#else
-    is_large = 0,
-#endif
-    value = is_large    ? Large
-            : Size == 1 ? 1
-                        : Small
-  };
-};
-
-template <typename Lhs, typename Rhs>
-struct product_type {
-  typedef remove_all_t<Lhs> Lhs_;
-  typedef remove_all_t<Rhs> Rhs_;
-  enum {
-    MaxRows = traits<Lhs_>::MaxRowsAtCompileTime,
-    Rows = traits<Lhs_>::RowsAtCompileTime,
-    MaxCols = traits<Rhs_>::MaxColsAtCompileTime,
-    Cols = traits<Rhs_>::ColsAtCompileTime,
-    MaxDepth = min_size_prefer_fixed(traits<Lhs_>::MaxColsAtCompileTime, traits<Rhs_>::MaxRowsAtCompileTime),
-    Depth = min_size_prefer_fixed(traits<Lhs_>::ColsAtCompileTime, traits<Rhs_>::RowsAtCompileTime)
-  };
-
-  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
-  // is to work around an internal compiler error with gcc 4.1 and 4.2.
- private:
-  enum {
-    rows_select = product_size_category<Rows, MaxRows>::value,
-    cols_select = product_size_category<Cols, MaxCols>::value,
-    depth_select = product_size_category<Depth, MaxDepth>::value
-  };
-  typedef product_type_selector<rows_select, cols_select, depth_select> selector;
-
- public:
-  enum { value = selector::ret, ret = selector::ret };
-#ifdef EIGEN_DEBUG_PRODUCT
-  static void debug() {
-    EIGEN_DEBUG_VAR(Rows);
-    EIGEN_DEBUG_VAR(Cols);
-    EIGEN_DEBUG_VAR(Depth);
-    EIGEN_DEBUG_VAR(rows_select);
-    EIGEN_DEBUG_VAR(cols_select);
-    EIGEN_DEBUG_VAR(depth_select);
-    EIGEN_DEBUG_VAR(value);
-  }
-#endif
-};
-
-/* The following allows to select the kind of product at compile time
- * based on the three dimensions of the product.
- * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
-// FIXME I'm not sure the current mapping is the ideal one.
-template <int M, int N>
-struct product_type_selector<M, N, 1> {
-  enum { ret = OuterProduct };
-};
-template <int M>
-struct product_type_selector<M, 1, 1> {
-  enum { ret = LazyCoeffBasedProductMode };
-};
-template <int N>
-struct product_type_selector<1, N, 1> {
-  enum { ret = LazyCoeffBasedProductMode };
-};
-template <int Depth>
-struct product_type_selector<1, 1, Depth> {
-  enum { ret = InnerProduct };
-};
-template <>
-struct product_type_selector<1, 1, 1> {
-  enum { ret = InnerProduct };
-};
-template <>
-struct product_type_selector<Small, 1, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<1, Small, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Small, Small, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Small, Small, 1> {
-  enum { ret = LazyCoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Small, Large, 1> {
-  enum { ret = LazyCoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Large, Small, 1> {
-  enum { ret = LazyCoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<1, Large, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<1, Large, Large> {
-  enum { ret = GemvProduct };
-};
-template <>
-struct product_type_selector<1, Small, Large> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Large, 1, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Large, 1, Large> {
-  enum { ret = GemvProduct };
-};
-template <>
-struct product_type_selector<Small, 1, Large> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Small, Small, Large> {
-  enum { ret = GemmProduct };
-};
-template <>
-struct product_type_selector<Large, Small, Large> {
-  enum { ret = GemmProduct };
-};
-template <>
-struct product_type_selector<Small, Large, Large> {
-  enum { ret = GemmProduct };
-};
-template <>
-struct product_type_selector<Large, Large, Large> {
-  enum { ret = GemmProduct };
-};
-template <>
-struct product_type_selector<Large, Small, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Small, Large, Small> {
-  enum { ret = CoeffBasedProductMode };
-};
-template <>
-struct product_type_selector<Large, Large, Small> {
-  enum { ret = GemmProduct };
-};
-
-}  // end namespace internal
-
-/***********************************************************************
- *  Implementation of Inner Vector Vector Product
- ***********************************************************************/
-
-// FIXME : maybe the "inner product" could return a Scalar
-// instead of a 1x1 matrix ??
-// Pro: more natural for the user
-// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
-// product ends up to a row-vector times col-vector product... To tackle this use
-// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
-
-/***********************************************************************
- *  Implementation of Outer Vector Vector Product
- ***********************************************************************/
-
-/***********************************************************************
- *  Implementation of General Matrix Vector Product
- ***********************************************************************/
-
-/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
- *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
- *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
- *   3 - all other cases are handled using a simple loop along the outer-storage direction.
- *  Therefore we need a lower level meta selector.
- *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
- */
-namespace internal {
-
-template <int Side, int StorageOrder, bool BlasCompatible>
-struct gemv_dense_selector;
-
-}  // end namespace internal
-
-namespace internal {
-
-template <typename Scalar, int Size, int MaxSize, bool Cond>
-struct gemv_static_vector_if;
-
-template <typename Scalar, int Size, int MaxSize>
-struct gemv_static_vector_if<Scalar, Size, MaxSize, false> {
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Scalar* data() {
-    eigen_internal_assert(false && "should never be called");
-    return 0;
-  }
-};
-
-template <typename Scalar, int Size>
-struct gemv_static_vector_if<Scalar, Size, Dynamic, true> {
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Scalar* data() { return 0; }
-};
-
-template <typename Scalar, int Size, int MaxSize>
-struct gemv_static_vector_if<Scalar, Size, MaxSize, true> {
-#if EIGEN_MAX_STATIC_ALIGN_BYTES != 0
-  internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize), 0, AlignedMax> m_data;
-  EIGEN_STRONG_INLINE constexpr Scalar* data() { return m_data.array; }
-#else
-  // Some architectures cannot align on the stack,
-  // => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
-  internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize) + EIGEN_MAX_ALIGN_BYTES, 0> m_data;
-  EIGEN_STRONG_INLINE constexpr Scalar* data() {
-    return reinterpret_cast<Scalar*>((std::uintptr_t(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES - 1))) +
-                                     EIGEN_MAX_ALIGN_BYTES);
-  }
-#endif
-};
-
-// The vector is on the left => transposition
-template <int StorageOrder, bool BlasCompatible>
-struct gemv_dense_selector<OnTheLeft, StorageOrder, BlasCompatible> {
-  template <typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
-    Transpose<Dest> destT(dest);
-    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
-    gemv_dense_selector<OnTheRight, OtherStorageOrder, BlasCompatible>::run(rhs.transpose(), lhs.transpose(), destT,
-                                                                            alpha);
-  }
-};
-
-template <>
-struct gemv_dense_selector<OnTheRight, ColMajor, true> {
-  template <typename Lhs, typename Rhs, typename Dest>
-  static inline void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
-    typedef typename Lhs::Scalar LhsScalar;
-    typedef typename Rhs::Scalar RhsScalar;
-    typedef typename Dest::Scalar ResScalar;
-
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-
-    typedef Map<Matrix<ResScalar, Dynamic, 1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)>
-        MappedDest;
-
-    ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
-    ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
-
-    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
-
-    // make sure Dest is a compile-time vector type (bug 1166)
-    typedef std::conditional_t<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr> ActualDest;
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime == 1),
-      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
-      MightCannotUseDest = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime != 0)
-    };
-
-    typedef const_blas_data_mapper<LhsScalar, Index, ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar, Index, RowMajor> RhsMapper;
-    RhsScalar compatibleAlpha = get_factor<ResScalar, RhsScalar>::run(actualAlpha);
-
-    if (!MightCannotUseDest) {
-      // shortcut if we are sure to be able to use dest directly,
-      // this ease the compiler to generate cleaner and more optimzized code for most common cases
-      general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, LhsBlasTraits::NeedToConjugate, RhsScalar,
-                                    RhsMapper, RhsBlasTraits::NeedToConjugate>::run(actualLhs.rows(), actualLhs.cols(),
-                                                                                    LhsMapper(actualLhs.data(),
-                                                                                              actualLhs.outerStride()),
-                                                                                    RhsMapper(actualRhs.data(),
-                                                                                              actualRhs.innerStride()),
-                                                                                    dest.data(), 1, compatibleAlpha);
-    } else {
-      gemv_static_vector_if<ResScalar, ActualDest::SizeAtCompileTime, ActualDest::MaxSizeAtCompileTime,
-                            MightCannotUseDest>
-          static_dest;
-
-      const bool alphaIsCompatible = (!ComplexByReal) || (numext::is_exactly_zero(numext::imag(actualAlpha)));
-      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
-
-      ei_declare_aligned_stack_constructed_variable(ResScalar, actualDestPtr, dest.size(),
-                                                    evalToDest ? dest.data() : static_dest.data());
-
-      if (!evalToDest) {
-#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-        Index size = dest.size();
-        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-#endif
-        if (!alphaIsCompatible) {
-          MappedDest(actualDestPtr, dest.size()).setZero();
-          compatibleAlpha = RhsScalar(1);
-        } else
-          MappedDest(actualDestPtr, dest.size()) = dest;
-      }
-
-      general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, LhsBlasTraits::NeedToConjugate, RhsScalar,
-                                    RhsMapper, RhsBlasTraits::NeedToConjugate>::run(actualLhs.rows(), actualLhs.cols(),
-                                                                                    LhsMapper(actualLhs.data(),
-                                                                                              actualLhs.outerStride()),
-                                                                                    RhsMapper(actualRhs.data(),
-                                                                                              actualRhs.innerStride()),
-                                                                                    actualDestPtr, 1, compatibleAlpha);
-
-      if (!evalToDest) {
-        if (!alphaIsCompatible)
-          dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
-        else
-          dest = MappedDest(actualDestPtr, dest.size());
-      }
-    }
-  }
-};
-
-template <>
-struct gemv_dense_selector<OnTheRight, RowMajor, true> {
-  template <typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
-    typedef typename Lhs::Scalar LhsScalar;
-    typedef typename Rhs::Scalar RhsScalar;
-    typedef typename Dest::Scalar ResScalar;
-
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
-
-    std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
-    std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
-
-    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      DirectlyUseRhs =
-          ActualRhsTypeCleaned::InnerStrideAtCompileTime == 1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime == 0
-    };
-
-    gemv_static_vector_if<RhsScalar, ActualRhsTypeCleaned::SizeAtCompileTime,
-                          ActualRhsTypeCleaned::MaxSizeAtCompileTime, !DirectlyUseRhs>
-        static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(
-        RhsScalar, actualRhsPtr, actualRhs.size(),
-        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
-
-    if (!DirectlyUseRhs) {
-#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = actualRhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-#endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    }
-
-    typedef const_blas_data_mapper<LhsScalar, Index, RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar, Index, ColMajor> RhsMapper;
-    general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, LhsBlasTraits::NeedToConjugate, RhsScalar,
-                                  RhsMapper, RhsBlasTraits::NeedToConjugate>::
-        run(actualLhs.rows(), actualLhs.cols(), LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-            RhsMapper(actualRhsPtr, 1), dest.data(),
-            dest.col(0).innerStride(),  // NOTE  if dest is not a vector at compile-time, then dest.innerStride() might
-                                        // be wrong. (bug 1166)
-            actualAlpha);
-  }
-};
-
-template <>
-struct gemv_dense_selector<OnTheRight, ColMajor, false> {
-  template <typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
-    EIGEN_STATIC_ASSERT((!nested_eval<Lhs, 1>::Evaluate),
-                        EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
-    // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory,
-    // otherwise use a temp
-    typename nested_eval<Rhs, 1>::type actual_rhs(rhs);
-    const Index size = rhs.rows();
-    for (Index k = 0; k < size; ++k) dest += (alpha * actual_rhs.coeff(k)) * lhs.col(k);
-  }
-};
-
-template <>
-struct gemv_dense_selector<OnTheRight, RowMajor, false> {
-  template <typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha) {
-    EIGEN_STATIC_ASSERT((!nested_eval<Lhs, 1>::Evaluate),
-                        EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
-    typename nested_eval<Rhs, Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
-    const Index rows = dest.rows();
-    for (Index i = 0; i < rows; ++i)
-      dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
-  }
-};
-
-}  // end namespace internal
-
-/***************************************************************************
- * Implementation of matrix base methods
- ***************************************************************************/
-
-/** \returns the matrix product of \c *this and \a other.
- *
- * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
- *
- * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Product<Derived, OtherDerived> MatrixBase<Derived>::operator*(
-    const MatrixBase<OtherDerived>& other) const {
-  // A note regarding the function declaration: In MSVC, this function will sometimes
-  // not be inlined since DenseStorage is an unwindable object for dynamic
-  // matrices and product types are holding a member to store the result.
-  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
-  enum {
-    ProductIsValid = Derived::ColsAtCompileTime == Dynamic || OtherDerived::RowsAtCompileTime == Dynamic ||
-                     int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime),
-    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
-    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived, OtherDerived)
-  };
-  // note to the lost user:
-  //    * for a dot product use: v1.dot(v2)
-  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
-  EIGEN_STATIC_ASSERT(
-      ProductIsValid || !(AreVectors && SameSizes),
-      INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-                      INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-#ifdef EIGEN_DEBUG_PRODUCT
-  internal::product_type<Derived, OtherDerived>::debug();
-#endif
-
-  return Product<Derived, OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
- *
- * The returned product will behave like any other expressions: the coefficients of the product will be
- * computed once at a time as requested. This might be useful in some extremely rare cases when only
- * a small and no coherent fraction of the result's coefficients have to be computed.
- *
- * \warning This version of the matrix product can be much much slower. So use it only if you know
- * what you are doing and that you measured a true speed improvement.
- *
- * \sa operator*(const MatrixBase&)
- */
-template <typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Product<Derived, OtherDerived, LazyProduct>
-MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived>& other) const {
-  enum {
-    ProductIsValid = Derived::ColsAtCompileTime == Dynamic || OtherDerived::RowsAtCompileTime == Dynamic ||
-                     int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime),
-    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
-    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived, OtherDerived)
-  };
-  // note to the lost user:
-  //    * for a dot product use: v1.dot(v2)
-  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
-  EIGEN_STATIC_ASSERT(
-      ProductIsValid || !(AreVectors && SameSizes),
-      INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-                      INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-
-  return Product<Derived, OtherDerived, LazyProduct>(derived(), other.derived());
-}
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_PRODUCT_H

Eigen/src/Core/GlobalFunctions.h

@@ -1,229 +1,95 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
 // Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
 
 #ifndef EIGEN_GLOBAL_FUNCTIONS_H
 #define EIGEN_GLOBAL_FUNCTIONS_H
 
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME, FUNCTOR, DOC_OP, DOC_DETAILS)                                    \
-  /** \returns an expression of the coefficient-wise DOC_OP of \a x                                             \
-                                                                                                              \ \
-    DOC_DETAILS                                                                                                 \
-                                                                                                              \ \
-    \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp   \
-    */                                                                                                          \
-  template <typename Derived>                                                                                   \
-  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> NAME(     \
-      const Eigen::ArrayBase<Derived>& x);
-
-#else
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME, FUNCTOR, DOC_OP, DOC_DETAILS)                                    \
-  template <typename Derived>                                                                                   \
-  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(NAME)(    \
-      const Eigen::ArrayBase<Derived>& x) {                                                                     \
-    return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
+#define EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(NAME,FUNCTOR) \
+  template<typename Derived> \
+  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
+  NAME(const Eigen::ArrayBase<Derived>& x) { \
+    return x.derived(); \
   }
 
-#endif  // EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME, FUNCTOR)                                                  \
-                                                                                                               \
-  template <typename Derived>                                                                                  \
-  struct NAME##_retval<ArrayBase<Derived> > {                                                                  \
+#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
+  \
+  template<typename Derived> \
+  struct NAME##_retval<ArrayBase<Derived> > \
+  { \
     typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \
-  };                                                                                                           \
-  template <typename Derived>                                                                                  \
-  struct NAME##_impl<ArrayBase<Derived> > {                                                                    \
-    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) {  \
-      return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived());                                   \
-    }                                                                                                          \
+  }; \
+  template<typename Derived> \
+  struct NAME##_impl<ArrayBase<Derived> > \
+  { \
+    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
+    { \
+      return x.derived(); \
+    } \
   };
 
-// IWYU pragma: private
-#include "./InternalHeaderCheck.h"
 
-namespace Eigen {
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real, scalar_real_op, real part,\sa ArrayBase::real)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag, scalar_imag_op, imaginary part,\sa ArrayBase::imag)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj, scalar_conjugate_op, complex conjugate,\sa ArrayBase::conjugate)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse, scalar_inverse_op, inverse,\sa ArrayBase::inverse)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin, scalar_sin_op, sine,\sa ArrayBase::sin)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos, scalar_cos_op, cosine,\sa ArrayBase::cos)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan, scalar_tan_op, tangent,\sa ArrayBase::tan)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan, scalar_atan_op, arc - tangent,\sa ArrayBase::atan)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin, scalar_asin_op, arc - sine,\sa ArrayBase::asin)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos, scalar_acos_op, arc - consine,\sa ArrayBase::acos)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh, scalar_sinh_op, hyperbolic sine,\sa ArrayBase::sinh)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh, scalar_cosh_op, hyperbolic cosine,\sa ArrayBase::cosh)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh, scalar_tanh_op, hyperbolic tangent,\sa ArrayBase::tanh)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh, scalar_asinh_op, inverse hyperbolic sine,\sa ArrayBase::asinh)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh, scalar_acosh_op, inverse hyperbolic cosine,\sa ArrayBase::acosh)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh, scalar_atanh_op, inverse hyperbolic tangent,\sa ArrayBase::atanh)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic, scalar_logistic_op, logistic function,\sa ArrayBase::logistic)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma, scalar_lgamma_op,
-                                 natural logarithm of the gamma function,\sa ArrayBase::lgamma)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma, scalar_digamma_op, derivative of lgamma,\sa ArrayBase::digamma)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf, scalar_erf_op, error function,\sa ArrayBase::erf)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc, scalar_erfc_op, complement error function,\sa ArrayBase::erfc)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ndtri, scalar_ndtri_op, inverse normal distribution function,\sa ArrayBase::ndtri)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp, scalar_exp_op, exponential,\sa ArrayBase::exp)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(expm1, scalar_expm1_op, exponential of a value minus 1,\sa ArrayBase::expm1)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log, scalar_log_op, natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p, scalar_log1p_op, natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10, scalar_log10_op, base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log10)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log2, scalar_log2_op, base 2 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log2)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs, scalar_abs_op, absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2, scalar_abs2_op,
-                                 squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg, scalar_arg_op, complex argument,\sa ArrayBase::arg DOXCOMMA MatrixBase::cwiseArg)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(carg, scalar_carg_op,
-                                 complex argument, \sa ArrayBase::carg DOXCOMMA MatrixBase::cwiseCArg)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt, scalar_sqrt_op, square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cbrt, scalar_cbrt_op, cube root,\sa ArrayBase::cbrt DOXCOMMA MatrixBase::cwiseCbrt)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt, scalar_rsqrt_op, reciprocal square root,\sa ArrayBase::rsqrt)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square, scalar_square_op,
-                                 square(power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube, scalar_cube_op, cube(power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rint, scalar_rint_op,
-                                 nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round, scalar_round_op,
-                                 nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
-    floor, scalar_floor_op, nearest integer not greater than the given value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
-    ceil, scalar_ceil_op, nearest integer not less than the given value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(trunc, scalar_trunc_op,
-                                 nearest integer not greater in magnitude than the given value,\sa Eigen::trunc DOXCOMMA
-                                     ArrayBase::trunc)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
-    isnan, scalar_isnan_op, not -a - number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(
-    isinf, scalar_isinf_op, infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite, scalar_isfinite_op,
-                                 finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
-EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign, scalar_sign_op, sign(or 0),\sa ArrayBase::sign)
+namespace std
+{
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,scalar_real_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,scalar_imag_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,scalar_sin_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,scalar_cos_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(asin,scalar_asin_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(acos,scalar_acos_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(tan,scalar_tan_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,scalar_exp_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,scalar_log_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,scalar_abs_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,scalar_sqrt_op)
 
-template <typename Derived, typename ScalarExponent>
-using GlobalUnaryPowReturnType = std::enable_if_t<
-    !internal::is_arithmetic<typename NumTraits<Derived>::Real>::value &&
-        internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
-    CwiseUnaryOp<internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>, const Derived> >;
-
-/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
- *
- * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given
- * expression (\c Derived::Scalar).
- *
- * \sa ArrayBase::pow()
- *
- * \relates ArrayBase
- */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-template <typename Derived, typename ScalarExponent>
-EIGEN_DEVICE_FUNC inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(const Eigen::ArrayBase<Derived>& x,
-                                                                                     const ScalarExponent& exponent);
-#else
-template <typename Derived, typename ScalarExponent>
-EIGEN_DEVICE_FUNC inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(const Eigen::ArrayBase<Derived>& x,
-                                                                                     const ScalarExponent& exponent) {
-  return GlobalUnaryPowReturnType<Derived, ScalarExponent>(
-      x.derived(), internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>(exponent));
-}
-#endif
-
-/** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
- *
- * This function computes the coefficient-wise power.
- *
- * Example: \include Cwise_array_power_array.cpp
- * Output: \verbinclude Cwise_array_power_array.out
- *
- * \sa ArrayBase::pow()
- *
- * \relates ArrayBase
- */
-template <typename Derived, typename ExponentDerived>
-inline const Eigen::CwiseBinaryOp<
-    Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived,
-    const ExponentDerived>
-pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents) {
-  return Eigen::CwiseBinaryOp<
-      Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived,
-      const ExponentDerived>(x.derived(), exponents.derived());
+  template<typename Derived>
+  inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
+  pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) { \
+    return x.derived().pow(exponent); \
+  }
 }
 
-/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
- *
- * This function computes the coefficient-wise power between a scalar and an array of exponents.
- *
- * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression
- * (\c Derived::Scalar).
- *
- * Example: \include Cwise_scalar_power_array.cpp
- * Output: \verbinclude Cwise_scalar_power_array.out
- *
- * \sa ArrayBase::pow()
- *
- * \relates ArrayBase
- */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-template <typename Scalar, typename Derived>
-inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar, Derived::Scalar>, Constant<Scalar>, Derived> pow(
-    const Scalar& x, const Eigen::ArrayBase<Derived>& x);
-#else
-template <typename Scalar, typename Derived>
-EIGEN_DEVICE_FUNC inline const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(
-    typename internal::promote_scalar_arg<typename Derived::Scalar EIGEN_COMMA Scalar EIGEN_COMMA
-                                              EIGEN_SCALAR_BINARY_SUPPORTED(pow, Scalar,
-                                                                            typename Derived::Scalar)>::type,
-    Derived, pow) pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents) {
-  typedef
-      typename internal::promote_scalar_arg<typename Derived::Scalar, Scalar,
-                                            EIGEN_SCALAR_BINARY_SUPPORTED(pow, Scalar, typename Derived::Scalar)>::type
-          PromotedScalar;
-  return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedScalar, Derived, pow)(
-      typename internal::plain_constant_type<Derived, PromotedScalar>::type(
-          exponents.derived().rows(), exponents.derived().cols(), internal::scalar_constant_op<PromotedScalar>(x)),
-      exponents.derived());
-}
-#endif
-
-/** \returns an expression of the coefficient-wise atan2(\a x, \a y). \a x and \a y must be of the same type.
- *
- * This function computes the coefficient-wise atan2().
- *
- * \sa ArrayBase::atan2()
- *
- * \relates ArrayBase
- */
-template <typename LhsDerived, typename RhsDerived>
-inline const std::enable_if_t<
-    std::is_same<typename LhsDerived::Scalar, typename RhsDerived::Scalar>::value,
-    Eigen::CwiseBinaryOp<Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>,
-                         const LhsDerived, const RhsDerived> >
-atan2(const Eigen::ArrayBase<LhsDerived>& x, const Eigen::ArrayBase<RhsDerived>& exponents) {
-  return Eigen::CwiseBinaryOp<
-      Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>, const LhsDerived,
-      const RhsDerived>(x.derived(), exponents.derived());
+namespace Eigen
+{
+  namespace internal
+  {
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sin,scalar_sin_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(cos,scalar_cos_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(asin,scalar_asin_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(acos,scalar_acos_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(tan,scalar_tan_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(exp,scalar_exp_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(log,scalar_log_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs,scalar_abs_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sqrt,scalar_sqrt_op)
+  }
 }
 
-namespace internal {
-EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real, scalar_real_op)
-EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag, scalar_imag_op)
-EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2, scalar_abs2_op)
-}  // namespace internal
-}  // namespace Eigen
+// TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...)
 
-// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random,
-// internal::isApprox...)
-
-#endif  // EIGEN_GLOBAL_FUNCTIONS_H
+#endif // EIGEN_GLOBAL_FUNCTIONS_H