On 29 Jul 2014, at 13:37, Jed Brown <j...@jedbrown.org> wrote: > Please always use "reply-all" so that your messages go to the list.
Sorry, fat-fingered the buttons. > > Lawrence Mitchell <lawrence.mitch...@imperial.ac.uk> writes: > >>> On 28 Jul 2014, at 23:27, Jed Brown <j...@jedbrown.org> wrote: >>> >>> Lawrence Mitchell <lawrence.mitch...@imperial.ac.uk> writes: >>>> Bog-standard P1 on a pretty much regularly meshed square domain (i.e. no >>>> reentrant corners or bad elements). >>> >>> What interpolation is being used? The finite-element embedding should >>> work well. >> >> Coarse to fine is just identity. Fine to coarse a lumped L2 projection. > > By "identity", do you mean in terms of continuous functions (the > finite-element embedding) or something on C-points? Fine-to-coarse is > generally taken to be the transpose of the natural prolongation, which > is integration. So my coarse space is spanned by the fine one, so I copy coarse dofs to the corresponding fine ones and then linearly interpolate to get the coefficient value at the missing fine dofs. >> >> Switching from GMRES to CG has no real effect. However bumping the number of >> iterations used for eigenvalue estimation from 10 to 20 on the finest grid >> gives me much better convergence on 2 processes. With fewer iterations the >> largest eigenvalue estimate gets stick around 1, once I jump to 20 it goes >> up to around 1.33, more in line with the exact 1.39. >> >> So this looks to be the original culprit. Thanks. > > It's odd for the estimates to require that many iterations unless the > RHS is somehow degenerate. I was hoping that a random right hand side > would excite the higher mode sooner. I had another look and it turns out this does work, I was driving the options database incorrectly the first time round. I needed to run with both estimate_eigenvalues and estimate_eigenvalues_random. If I run with: -pc_type mg -ksp_max_it 6 -pc_mg_levels 2 -ksp_monitor -mg_levels_1_ksp_chebyshev_estimate_eigenvalues_random -mg_levels_1_ksp_chebyshev_estimate_eigenvalues Then I get decent estimation of the higher modes and good convergence. Thanks, Lawrence
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