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| 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 /doc | |
| parent | 114a15c66ad0af1ea15250b988a9040afa6211ef (diff) | |
Update old links to bitbucket to point to gitlab.com
Diffstat (limited to 'doc')
| -rw-r--r-- | doc/DenseDecompositionBenchmark.dox | 2 |
1 files changed, 1 insertions, 1 deletions
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. */ |