diff options
| author |  Gael Guennebaud <g.gael@free.fr> | 2019-12-04 10:57:07 +0100 |
|---|---|---|
| committer | Gael Guennebaud <g.gael@free.fr> | 2019-12-04 10:57:07 +0100 |
| commit | 8fbe0e4699b4c03dd62b266371f23b103319ec36 (patch) | |
| tree | 6bbe2dbe4d0e5519eb99b673d367af09c0ad3e4e | |
| parent | 114a15c66ad0af1ea15250b988a9040afa6211ef (diff) | |
Update old links to bitbucket to point to gitlab.com
| -rw-r--r-- | Eigen/src/Core/arch/SSE/MathFunctions.h | 2 | ||||
| -rw-r--r-- | README.md | 4 | ||||
| -rw-r--r-- | doc/DenseDecompositionBenchmark.dox | 2 |
3 files changed, 3 insertions, 5 deletions
diff --git a/Eigen/src/Core/arch/SSE/MathFunctions.h b/Eigen/src/Core/arch/SSE/MathFunctions.h index 85255ad23..92c1eecc7 100644 --- a/Eigen/src/Core/arch/SSE/MathFunctions.h +++ b/Eigen/src/Core/arch/SSE/MathFunctions.h @@ -168,7 +168,7 @@ double sqrt(const double &x) { #if EIGEN_COMP_GNUC_STRICT // 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)))); #else return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x)))); @@ -2,6 +2,4 @@ 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 ***bug reports*** and ***feature requests*** go to http://eigen.tuxfamily.org/bz. +For ***pull request***, ***bug reports***, and ***feature requests***, go to https://gitlab.com/libeigen/eigen. diff --git a/doc/DenseDecompositionBenchmark.dox b/doc/DenseDecompositionBenchmark.dox index 7be9c70cd..8f9570b7a 100644 --- a/doc/DenseDecompositionBenchmark.dox +++ b/doc/DenseDecompositionBenchmark.dox @@ -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. + 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. */ |