Hello All, I'm trying to use the SLEPc Krylov-Schur implementation to solve a general eigenvalue problem. I have a monitor on my solver and the solutions appear to converge correctly when using the approximation for the residual norm in the algorithm. However, when the solutions are displayed and I retrieve the actual residual norm it is very large and increases with the size of the matrices I am working with. In some cases it may be 10^17 times as large as the approximate norm. I also don't get the eigenvalues I would expect for the system I am studying.
When I turn on the option "true residual" the solver fails to converge. The residual norm shrinks to some limit (~10^-3) and then sits there for the remaining iterations. As a note, I am solving for the eigenvalues with the smallest real part. I have also tried the RQCG solver on the same problems and appear to get the correct results using it, but I'm looking to use the better scaling of the Krylov-Schur solver. Does anyone know what could be causing this behavior? Thanks, Chris Pierce WPI Center for Computational Nanoscience
