Eigen (C++ library): Difference between revisions
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*<code>-DEIGEN_NO_DEBUG</code> Set preprocessor define for eigen optimizations | *<code>-DEIGEN_NO_DEBUG</code> Set preprocessor define for eigen optimizations | ||
*<code>-fopenmp</code> OpenMP parallel execution | *<code>-fopenmp</code> OpenMP parallel execution | ||
==Math== | |||
===SVD=== | |||
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> | |||
[https://github.com/kazuotani14/RandomizedSvd Github Implementation]<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] | |||
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.
Github Implementation
Finding structure with randomness paper
Facebook Blog post