[Matrix-SIG] Optimization algorithms, etc.

[Travis Oliphant]

Robin,

I remember your posting about Burke and Xu's LCP method and was
interested, but October was a busy paper-writing month for me and so I
spent all my time using Python and very little developing and
communicating and none packaging.

My vision for multipack is that it would be a community effort and your
algorithm could find a very nice home there.  Development of multipack
takes place on a CVS server graciously provided by Pearu Peterson who
has also contributed significantly to Multipack that I could get you
access to. Alternatively,  you could just send a tarball to me.  But I
have to admit that I'd prefer if authors took care of incorporating their
own packages, as it would be more likely to show up in future editions.
Multipack is very modular, so your wrapping could be dropped into the
overall umbrella quite easily.  There is documentation about how to do it
in the release.

I haven't checked this recently but you should be able to check out
multipack from CVS anonymously (it's pretty big right now as it includes
some alpha wrappings of all of the lapack and blas -- auto generated by
Pearu's f2py code) so you might just want to get the tarball from my site.

I'm very willing to receive improvements to Multipack from individuals
anxious to see Numeric Python become a top-notch environment for
general-purpose number crunching and algorithm development.

I have not worked on Multipack actively for several months, and
so my plan of incorporating all of my modules (signaltools,
cephes, etc. into the multipack structure has not been
realized...perhaps this is not such a good plan anyway
(thoughts?) )  

Sorry I did not get contact you sooner, it can be frustrating when you
make this really neat algorithm available to Python and nobody seems to
care.  My experience is that people care quite a bit, but are pretty busy
and don't have time to help so they are reluctant to comment.


--Travis Oliphant

P.S.

For those on matrix-sig interested in the sparse matrix development:

Currently, I'm improving the sparse matrix package that I discussed on
this list several months ago so that it is actually useful to solve
partial differential equations using finite elements and finite
differences.  I'm working on incorporating the package SuperLU for 
directly solving a sparse linear system and implementing at least the
conjugate gradient algorithm (in Python) for iteratively solving the
system.