On 10/19/12 8:05 AM, Dima Pasechnik wrote:
On Friday, 19 October 2012 20:35:07 UTC+8, jason wrote:
On 10/19/12 7:25 AM, Dima Pasechnik wrote:
> While some ARPACK functionality is available in Sage from scipy
(more is
> implemented in scipy 0.11, than in currently available in Sage scipy
> 0.9, but this is taken care of in #13541, which I'm reviewing
presently),
> the functionality allowing to implement matrices as callback
functions
> (sometimes a big win in memory/speed) seems to be missing.
>
> Any idea how can this be handled?
>
Do you mean a sparse matrix class where A[i,j] actually calls some
function that you supply?
right.
I'd make a subclass of sparse matrices (or
maybe even dense matrices??), and just override the unsafe indexing
method. I think all indexing ends up going through that function.
well, it does not look that obvious (it's sparse scipy/numpy matrices:
http://docs.scipy.org/doc/scipy/reference/sparse.html) and they have
several member functions, such as __getitem__, _get_row_slice, etc...
Oh, you're talking Scipy matrices, not Sage matrices. You're right;
that is different. I'd suggest posting to the scipy-dev mailing list.
It seems like it would make a nice addition to the sparse matrix classes
already there.
Do the various fortran libraries support matrices with callable
functions? If Scipy is just handing off the matrix to a fortran
library, then it's just handing off a C (or Fortran) array, not a python
callable object, of course.
Hmm, I actually thought they are way more optimized, than what I saw in
sage/scipy/sparse/csr.py
I think most of the optimization is just in storage (various ways of
compressing) and in using sparse fortran packages. I'm sure they would
appreciate pull requests if you had some ideas for making them better,
though!
Thanks,
Jason
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