I am implementing a sparse matrix class for real doubles (finite precision real numbers.) The storage format I am using is called compressed sparse column. This is the standard format used by all sparse matrix libraries as well as matlab
http://www.netlib.org/linalg/html_templates/node92.html It is not specifically designed for block diagonal matrices though. As for matrix vector multiplication, it is very slow. One problem with this is that currently sparsity is treated as a mathematical property so when multiplying a sparse matrix and a dense vector you have to coerce the dense vector into a sparse vector. This behaviour should be changed, I was going to propose that when doing arithmetic between sparse and dense objects coercion should not be necessary. Josh On Jun 1, 11:07 pm, Michel <[EMAIL PROTECTED]> wrote: > Hi, > > Something related. A while ago I was using sparse matrices > to compute the page ranks of a small web. The computation > was *much* too slow. So I implemented my own method > for "matrix" x "vector" as a simple loop through the non-zero entries > of "martrix" which was *much* faster. > > So: question: could it be that the multiplication of sparse matrices > is not as optimized as it should be? I looked in > "matrix_generic_sparse.pyx" > but I don't even see a _mul_ method. Where is multiplication > of sparse matrices implemented? > > Michel > > On Jun 2, 4:23 am, "Mike Hansen" <[EMAIL PROTECTED]> wrote: > > > I thought I recalled someone mentioning this before, but is someone > > working on or thinking about working on implementing "sparse" block > > diagonal matrices where you would only store the dense blocks? > > > If someone is working on it, I'd be willing to help out a bit since it > > would be incredibly useful for some of my research. > > > --Mike --~--~---------~--~----~------------~-------~--~----~ To post to this group, send email to sage-devel@googlegroups.com To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/sage-devel URLs: http://sage.scipy.org/sage/ and http://modular.math.washington.edu/sage/ -~----------~----~----~----~------~----~------~--~---
