Eigen (C++ library): Difference between revisions
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Eigen comes with a few SVD implementations in its [https://eigen.tuxfamily.org/dox/group__SVD__Module.html SVD Module].<br> | Eigen comes with a few SVD implementations in its [https://eigen.tuxfamily.org/dox/group__SVD__Module.html SVD Module].<br> | ||
If you only need low-rank approximations then you may be interested in randomized SVD.<br> | If you only need low-rank approximations then you may be interested in randomized SVD.<br> | ||
This can be 10-20x faster when calculating low-rank approximations on large matrices.<br> | |||
[https://github.com/kazuotani14/RandomizedSvd Github Implementation]<br> | [https://github.com/kazuotani14/RandomizedSvd Github Implementation]<br> | ||
[https://arxiv.org/abs/0909.4061 Finding structure with randomness paper]<br> | [https://arxiv.org/abs/0909.4061 Finding structure with randomness paper]<br> | ||
[https://research.fb.com/blog/2014/09/fast-randomized-svd/ Facebook Blog post] | [https://research.fb.com/blog/2014/09/fast-randomized-svd/ Facebook Blog post] | ||
Revision as of 19:37, 14 October 2019
Eigen is a template header-only C++ linear algebra library. It is one of the fastest and most popular.
Usage
Compilation
Reference
For optimal performance, I recommend using the following flags when compiling.
GCC
-mfmaEnable fused multiply add-mavx2Enable avx2 vector instructions-DEIGEN_NO_DEBUGSet preprocessor define for eigen optimizations-fopenmpOpenMP parallel execution
Math
SVD
Eigen comes with a few SVD implementations in its SVD Module.
If you only need low-rank approximations then you may be interested in randomized SVD.
This can be 10-20x faster when calculating low-rank approximations on large matrices.
Github Implementation
Finding structure with randomness paper
Facebook Blog post