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Update old links to bitbucket to point to gitlab.com

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This commit is contained in:
Gael Guennebaud 2019-12-04 10:57:07 +01:00
parent 114a15c66a
commit 8fbe0e4699
3 changed files with 3 additions and 5 deletions
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2
Eigen/src/Core/arch/SSE/MathFunctions.h
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@ -168,7 +168,7 @@ double sqrt(const double &x)
{ {
#if EIGEN_COMP_GNUC_STRICT #if EIGEN_COMP_GNUC_STRICT
// This works around a GCC bug generating poor code for _mm_sqrt_pd // This works around a GCC bug generating poor code for _mm_sqrt_pd
// See https://bitbucket.org/eigen/eigen/commits/14f468dba4d350d7c19c9b93072e19f7b3df563b // See https://gitlab.com/libeigen/eigen/commit/8dca9f97e38970
return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x)))); return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x))));
#else #else
return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x)))); return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x))));

4
README.md
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@ -2,6 +2,4 @@
For more information go to http://eigen.tuxfamily.org/. For more information go to http://eigen.tuxfamily.org/.
For ***pull request*** please only use the official repository at https://bitbucket.org/eigen/eigen. For ***pull request***, ***bug reports***, and ***feature requests***, go to https://gitlab.com/libeigen/eigen.
For ***bug reports*** and ***feature requests*** go to http://eigen.tuxfamily.org/bz.

2
doc/DenseDecompositionBenchmark.dox
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@ -35,7 +35,7 @@ Timings are in \b milliseconds, and factors are relative to the LLT decompositio
+ For large problem sizes, only the decomposition implementing a cache-friendly blocking strategy scale well. Those include LLT, PartialPivLU, HouseholderQR, and BDCSVD. This explain why for a 4k x 4k matrix, HouseholderQR is faster than LDLT. In the future, LDLT and ColPivHouseholderQR will also implement blocking strategies. + For large problem sizes, only the decomposition implementing a cache-friendly blocking strategy scale well. Those include LLT, PartialPivLU, HouseholderQR, and BDCSVD. This explain why for a 4k x 4k matrix, HouseholderQR is faster than LDLT. In the future, LDLT and ColPivHouseholderQR will also implement blocking strategies.
+ CompleteOrthogonalDecomposition is based on ColPivHouseholderQR and they thus achieve the same level of performance. + CompleteOrthogonalDecomposition is based on ColPivHouseholderQR and they thus achieve the same level of performance.
The above table has been generated by the <a href="https://bitbucket.org/eigen/eigen/raw/default/bench/dense_solvers.cpp">bench/dense_solvers.cpp</a> file, feel-free to hack it to generate a table matching your hardware, compiler, and favorite problem sizes. The above table has been generated by the <a href="https://gitlab.com/libeigen/eigen/raw/master/bench/dense_solvers.cpp">bench/dense_solvers.cpp</a> file, feel-free to hack it to generate a table matching your hardware, compiler, and favorite problem sizes.
*/ */