On 8/16/06, **Albert Strasheim** <fullung@gmail.com> wrote:

Cool, thanks for the info.Hello all

> -----Original Message-----

> From: numpy-discussion-bounces@lists.sourceforge.net [mailto:numpy-

> discussion-bounces@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@gmail.com> wrote:

>

> On 8/15/06, David Grant < davidgrant@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.

--

David Grant

http://www.davidgrant.ca