On Thu, Mar 6, 2008 at 2:03 PM, jose luis Lopez Martinez <lopmart@gmail.com> wrote:

hi

the size of matrix sparse is 128^4(268,435,456 rows)x 128^4(268,435,456 colums).

the matrix have diagonald dominancies that is a sufficient condition for convergence of Jacobi iteration , gauss seidel and xor(relaxion method), but is slow for converge.

it exist a command in scipy that solve sparse matrix using at the Gauss-Seidel iteration method o krylov iterative methods?

Is your matrix symmetric? If so, try the conjugate gradient (CG) method. It will only require matrix-vector products. You can even get around storing your sparse matrix by writing a function that evaluates the result of a matrix-vector product. This can be interesting if the density is high. If your matrix is not symmetric, try Bi-Cgstab. It also only requires matrix-vector products with the matrix (and not with its transpose). Same comment as above regarding storage. You can try Bi-Cgstab in either case, but if your matrix is symmetric, it will come out as more expensive than CG, although equivalent on paper. Specifically, it will perform twice as many matrix-vector products. For both, you need to specify a max number of iteration that is *at least* the order of the matrix. If it isn't too ill conditioned, chances are you'll need much less iterations than that. See scipy.linalg.cg and scipy.linalg.bicgstab Hope this helps Dominique