Re: [Pysparse-users] Computing largest eigenvalues?

[Roman G. <rom...@gm...>]

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
>
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