On Mon, Feb 9, 2009 at 12:32 AM, Xiaoyu Chu <[email protected]> wrote:
> Hey all, > I am currently working on a large matrix, and I already have a > specific eigen value that I want to use in order to find out its > corresponding eigen vector. Is there an easy way to do so? > > I have tried with linalg.solve(a, b), where I put a as the > Matrix A - eigen value* unit matrix, and b as the zero matrix. But the > solution returned is a zero matrix, which I really find disappointing. > > I have also tried with eig(A), which finds out all the eigen > vectors of matrix A, but it takes too long to run especially the order > of my matrix is like 10,000. > > > So right now, I really find myself stuck. Is there anyone who can help > me? > The usual trick for this is inverse iteration, http://en.wikipedia.org/wiki/Inverse_iteration, which involves solving the almost singular equation A - eig*I = rhs for an appropriately chosen rhs. The required eigenvector will dominate the solution and will grow rapidly in amplitude over a few iterations. Chuck
_______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
