On 8/16/06, Albert Strasheim <[EMAIL PROTECTED]> wrote:
Cool, thanks for the info.Hello all
> -----Original Message-----
> From: [EMAIL PROTECTED] [mailto:numpy-
> [EMAIL PROTECTED]] 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 <[EMAIL PROTECTED]> wrote:
>
> On 8/15/06, David Grant < [EMAIL PROTECTED]> 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
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