Im trying to solve sparse symmetry eigenvalue problem using MKL LAPACK.
My matrix leads from PDE and has 5 non-zero diagonals (with wide band), it is symmetry and not positive-define. Matrix is large (order ~ 10^4 and more) or very large (order ~ 10^7 and more).
I need to find all eigenvalies and all eigenvectors. I would like to get all eigenvectors not simultaneous, but gradually at several portions because of large size of matrix.
Now I use dsbev() or such sequence of routines:
- dsbtrd(), vect = `V`,
- dgemm() for getting eigenvectors of A from eigenvectors of tridiagonal T.
My questions are:
- Is it possible to store orthogonal matrix Q from dsbtrd() in some compact storage sheme?
- Is there another way to get all eigenvectors at several steps for large (sparse band) matrixes?
Thank you in advance for help,