[Pysparse-users] Computing largest eigenvalues?

[Rob S. <rs...@MI...>]

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