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