Thank you. I have used the -ksp_converged_reason option. The result says: Linear solve did not converge due to DIVERGED_INDEFINITE_PC iterations 2 I then further checked the row sum matrix, it has negative eigenvalues. So I guess it does not work at all. Thank you all for your help.
-- Shi Jin, PhD ----- Original Message ---- > From: Matthew Knepley <knepley at gmail.com> > To: petsc-users at mcs.anl.gov > Sent: Wednesday, April 9, 2008 2:50:29 PM > Subject: Re: Further question about PC with Jaocbi Row Sum > > On Wed, Apr 9, 2008 at 3:25 PM, Shi Jin wrote: > > Thank you very much. > > > > > > > > > > Is there something particular about this rowsum method? > > > > > > No. If you use a -ksp_rtol of 1.e-12 and still get different > > > answers, this needs to be investigated. > > > > > > > > > > I have tried even with -ksp_rtol 1.e-20 but still got different results. > > > > Here is what I got when solving the mass matrix with > > > > -pc_type jacobi > > -pc_jacobi_rowsum 1 > > -ksp_type cg > > -sub_pc_type icc > > -ksp_rtol 1.e-20 > > -ksp_monitor > > -ksp_view > > > > 0 KSP Residual norm 2.975203858623e+00 > > 1 KSP Residual norm 2.674371671721e-01 > > 2 KSP Residual norm 1.841074927355e-01 > > KSP Object: > > type: cg > > maximum iterations=10000, initial guess is zero > > tolerances: relative=1e-20, absolute=1e-50, divergence=10000 > > left preconditioning > > PC Object: > > type: jacobi > > linear system matrix = precond matrix: > > Matrix Object: > > type=seqaij, rows=8775, cols=8775 > > total: nonzeros=214591, allocated nonzeros=214591 > > not using I-node routines > > > > I realize that the iteration ended when the residual norm is quite large. > > Do you think this indicates something wrong here? > > Can you run with > > -ksp_converged_reason > > It appears that the solve fails rather than terminates with an answer. Is it > possible that your matrix is not SPD? > > Matt > > > Thank you again. > > > > Shi > > > > > > > > __________________________________________________ > > Do You Yahoo!? > > Tired of spam? Yahoo! Mail has the best spam protection around > > http://mail.yahoo.com > > > > > > > > -- > What most experimenters take for granted before they begin their > experiments is infinitely more interesting than any results to which > their experiments lead. > -- Norbert Wiener > > __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com
