Well, my portion of this project is done, but thanks for the response. I used a naive version of Lanczos, but will need somebody who has a better understanding of linear algebra to implement a reorthogonalization technique. The reason I couldn't use a pre-written routine is because I was...
Thanks. I have been doing that recently. Is simple lanczos even good for more than the extremal eigenvalue? It seems that I'd have to reorthogonalize if I wanted more than one, but I'm not 100% sure. Basically I am wondering if it will preserve say the top x eigenvalues where x << matrix...
So I implemented a tridiagonalization algorithm, however I don't know if the result that I am receiving is accurate. Does anyone know of an easy way of testing this? I suppose it is possible to scour around for code, but it would be difficult to find matching code. So if anyone knows anything...
Yeah, I figured this out as well.
So, the new plan after discussing some things with my adviser is to go a slightly different route, which will hopefully still involve a speedup. Instead of directly speeding up the eigen decomposition, the plan is to hopefully speedup the preconditioning...
Do you happen to have any resources (such as a good website) that describe this algorithm? The matrix will be square/symmetric so it should converge fine.
Edit: I found some resources on it, but would still find others helpful if you know of any.
Edit2: This method is probably not going to...
Hi, this is my first post here, so bare with me.
So I need to compute the eigenvectors of a large matrix (1000x1000) to (10000x10000x) or so. However, I already have the eigenvalues and diagonal/superdiagonal form of the matrix. The equation (A-lambda*I)*v = 0, where A is the matrix, lambda...