From: Roman G. <rom...@gm...> - 2010-03-29 13:15:12
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Hi Rob I have made good experiences with ARPACK in the past. There seems to be a Python wrapper for it as well (though I have never used it). Regards, Roman On Thu, Mar 25, 2010 at 6:21 PM, Rob Speer <rs...@mi...> wrote: > Got any pointers? Up until now, I've been working with an old, clunky > C library called SVDLIBC that implements Lanczos. Pysparse's jdsym is > the first thing I've seen that can find eigenvectors and presents an > interface that can actually work with Python objects. > > There's the stuff in scipy.sparse, of course, but that's been stalled > in the development for years now, and it doesn't often compile from > SVN. > > -- Rob > > On Thu, Mar 25, 2010 at 3:17 AM, Roman Geus <rom...@gm...> wrote: >> Hi Rob >> >> If you set tau to some very large number, but still far away from the >> actual largest eigenvalue, you might experience very slow convergence. >> There are certainly better and simpler algorithms than JDSYM for >> computing a few of the largest eigenvalues. >> >> -- Roman >> >> On Wed, Mar 24, 2010 at 8:05 PM, Rob Speer <rs...@mi...> wrote: >>> It looks like it should be possible to compute the truncated spectral >>> decomposition of a sparse, symmetric matrix using pysparse.jdsym. This >>> is the key step in computing a truncated SVD, which is the next thing >>> to do, and it would be great to be able to do it entirely within >>> Pysparse. >>> >>> There's just one thing I'm unsure about: how do I ask for the >>> *largest* eigenvalues? jdsym is set up to return eigenvalues around >>> some value tau, defaulting to 0, so it seems this is set up for >>> finding the smallest eigenvalues. Do I just set tau to some very large >>> number, or would that cause numerical stability issues? Is this the >>> wrong problem for jdsym to solve? >>> >>> -- Rob >>> >>> ------------------------------------------------------------------------------ >>> Download Intel® Parallel Studio Eval >>> Try the new software tools for yourself. Speed compiling, find bugs >>> proactively, and fine-tune applications for parallel performance. >>> See why Intel Parallel Studio got high marks during beta. >>> http://p.sf.net/sfu/intel-sw-dev >>> _______________________________________________ >>> Pysparse-users mailing list >>> Pys...@li... >>> https://lists.sourceforge.net/lists/listinfo/pysparse-users >>> >> > |