Two iterations for the eigen estimate is too low and gmres converges slowly. I'm surprised this does not diverge, or just die, for a Laplacian because you need to get an upper bound. Cheby will scale the estimate up by some safety factor (is it really large now?). Try: -mg_levels_esteig_ksp_max_it 10 (the old default). I usually use 5.
Also, I would suggest using cg (-mg_levels_esteig_ksp_type cg), it converges much faster. If your problem is not very asymmetric, it is fine. On Wed, Sep 13, 2017 at 11:35 AM, Hong <hzh...@mcs.anl.gov> wrote: > Federico : > >> >> Coarse grid solver -- level ------------------------------- >> KSP Object: (mg_levels_0_) 128 MPI processes >> type: chebyshev >> Chebyshev: eigenvalue estimates: min = 0.223549, max = 2.45903 >> Chebyshev: eigenvalues estimated using gmres with translations >> [0. 0.1; 0. 1.1] >> KSP Object: (mg_levels_0_esteig_) 128 MPI processes >> type: gmres >> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt >> Orthogonalization with no iterative refinement >> GMRES: happy breakdown tolerance 1e-30 >> maximum iterations=10, initial guess is zero >> *tolerances: relative=1e-12*, absolute=1e-50, >> divergence=10000. >> left preconditioning >> *using PRECONDITIONED norm type for convergence test* >> maximum iterations=2, initial guess is zero >> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >> left preconditioning >> using NONE norm type for convergence test >> > > Chebyshev requires an estimate of operator eigenvalues, for which we use > few gmres iterations. These default options are used for eigenvalue > estimates. > > Hong > >