I have been looking at the following set of notes:
and specifically this quote in those notes:
"If SIGMA is a real or complex scalar including 0, EIGS finds the eigenvalues closest to SIGMA. For scalar SIGMA, and when SIGMA = ’SM’, B need only be symmetric (or Hermitian) positive semi-definite since it is not Cholesky factored as in the other cases."
I have a Hermitian positive-semidefinite matrix A, of which I want to find the 3 smallest eigenvalues. The Cholesky-decomposition is too memory intensive for the matrices I am working with. Please, is there a way to use eigs() without having to perform the Cholesky decomposition either in eigs() or outside of it?
Thank you very much.