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author | Yi Kong <yikong@google.com> | 2022-02-25 16:32:14 +0800 |
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committer | Yi Kong <yikong@google.com> | 2022-02-25 15:08:55 +0000 |
commit | 2aab794c004027d008d6b0b64165bf1961d5d2bb (patch) | |
tree | 83bb8f19c67bcafdb2ca4a98414af1b17392ec36 /doc/DenseDecompositionBenchmark.dox | |
parent | ca5aa72016f062fd0712bcb86370478de332bca3 (diff) | |
download | eigen-2aab794c004027d008d6b0b64165bf1961d5d2bb.tar.gz |
Upgrade eigen to 3.4.0
Steps:
* Removed common files between Android copy and the matching upstream copy
* Obtained latest upstream tarball (see README.version)
* Extracted over the directory
Bug: 148287349
Test: presubmit
Change-Id: Iee2744719075fdf000b315e973645923da766111
Diffstat (limited to 'doc/DenseDecompositionBenchmark.dox')
-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. */ |