[SciPy-dev] Sparse SVD in SciPy -- SVDPACK[C] made available

Stéfan van der Walt stefan@sun.ac...
Fri Apr 24 09:54:38 CDT 2009


2009/4/24 David Warde-Farley <dwf@cs.toronto.edu>:
> Hmm, I thought that sparse SVD was provided by ARPACK, already
> included in SciPy, and it was just a matter of writing the wrappers.
> Is that still the case, or is SVDPACK for some reason a substantially
> easier route/is SVDPACK better or more featureful?

I guess there are trade-offs between the algorithms, but I can't give
you any details yet.

When I mailed Michael Berry, the author of SVDPACKC, a while ago, he wrote:

"""
We used Lanczos, Subspace Iteration, and Trace Minimization algorithms
for developing svdpack and svdpackc.  The Lanczos routines (las1 and
las2) are fairly robust for computing extremal s-triplets - not the
entire spectrum.  You can also evaluate ARPACK which is also in NETLIB
and is based on Arnoldi methods (similar to Lanczos).
"""

What I do know is that the ARPACK in SciPy currently does not contain
code for the SVD.  The code can be found in

http://www.caam.rice.edu/software/ARPACK/SRC/arpack96.tar.gz

as ARPACK/EXAMPLES/SVD/ssvd.f.

Regards
Stéfan


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