[petsc-users] Krylov-Schur Tolerance

[Christopher Pierce]

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