Does anyone here know of any fast algorithms to diagonize large, symmetric matrices, that are mostly zeros? (by large I mean 300x300 up to several million by several million)
For more general matrix types, you might look at LAPACK/BLAS or the Intel MKL.
I'm looking at them now. I'm trying to get them to work for me with C++, but not having any luck. I'm using Dev C++ on windows xp. If you have any advice on how to get it working, that would be very helpful.
I'm trying to find the lowest eigenvalue (for now, eventually I may need other eigenvalues.). I figured that diagonalizing was the easiest way to go.
If they don't, finding an associated eigenvector is an easy problem: it's just a nullvector of (A - vI).I'm working further now, and it seems that I will be needing the eigenvectors corresponding to the lowest eigenvalues. Do these algorithms return these as well? or is there another algorithm to get them from it?
I'm going to look at the algorithms suggested now.
If they don't, finding an associated eigenvector is an easy problem: it's just a nullvector of (A - vI).