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| author |  Benoit Jacob <jacob.benoit.1@gmail.com> | 2009-08-12 02:35:07 -0400 |
|---|---|---|
| committer | Benoit Jacob <jacob.benoit.1@gmail.com> | 2009-08-12 02:35:07 -0400 |
| commit | 22d65d47d085a9b693d7777f646a87cb3d51a06a (patch) | |
| tree | a1e4827c64a31a622492d2384961880c782d7828 /bench | |
| parent | ce033ebdfe817a21e648cad3f2bb6db76cb8fa6a (diff) | |
finally, the good approach was two-sided Jacobi. Indeed, it allows
to guarantee the precision of the output, which is very valuable.
Here, we guarantee that the diagonal matrix returned by the SVD is
actually diagonal, to machine precision.
Performance isn't bad at all at 50% of the current householder SVD
performance for a 200x200 matrix (no vectorization) and we have
lots of room for improvement.
Diffstat (limited to 'bench')
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