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/ -~----------~----~----~----~------~----~------~--~---
