[Numpy-discussion] some work on arpack

[David Grant]

On 8/16/06, Albert Strasheim <fullung at gmail.com> wrote:
>
> Hello all
>
> > -----Original Message-----
> > From: numpy-discussion-bounces at lists.sourceforge.net [mailto:numpy-
> > discussion-bounces at lists.sourceforge.net] On Behalf Of David Grant
> > Sent: 16 August 2006 17:11
> > To: Discussion of Numerical Python
> > Subject: Re: [Numpy-discussion] some work on arpack
> >
> >
> >
> > On 8/16/06, Keith Goodman <kwgoodman at gmail.com> wrote:
> >
> >       On 8/15/06, David Grant <davidgrant at gmail.com> wrote:
> >
> >       > My idea is (if I have time) to write an eigs-like function in
> > python
> >       > that will only perform a subset of what Matlab's eigs does for.
> It
> >       > will, for example, compute a certain number of eigenvalues and
> >       > eigenvectors for a real, sparse, symmetric matrix (the case I'm
> >       > interested in)
> >
> >       Will it also work for a real, dense, symmetric matrix? That's the
> > case
> >       I'm interested in. But even if it doesn't, your work is great news
> > for
> >       numpy.
> >
> > Real, dense, symmetric, well doesn't scipy already have something for
> > this? I'm honestly not sure on the arpack side of things, I thought
> arpack
> > was only useful (over other tools) for sparse matrices, I could be
> wrong.
>
> Maybe SciPy can also do this, but what makes ARPACK useful is that it can
> get you a few eigenvalues and eigenvectors of a massive matrix without
> having to have the whole thing in memory. Instead, you provide ARPACK with
> a
> function that does A*x on your matrix. ARPACK passes a few x's to your
> function and a few eigenvalues and eigenvectors fall out.


Cool, thanks for the info.

-- 
David Grant
http://www.davidgrant.ca
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