And don't use -pc_gamg_parallel_coarse_grid_solver You can use that in production but for debugging use -mg_coarse_pc_type svd Also, use -options_left and remove anything that is not used. (I am puzzled, I see -pc_type gamg not -pc_type fieldsplit)
Mark On Mon, Jun 16, 2025 at 6:40 AM Matthew Knepley <knep...@gmail.com> wrote: > On Sun, Jun 15, 2025 at 9:46 PM hexioafeng <hexiaof...@buaa.edu.cn> wrote: > >> Hello, >> >> Here are the options and outputs: >> >> options: >> >> -ksp_type cg -pc_type gamg -pc_gamg_parallel_coarse_grid_solver >> -pc_fieldsplit_detect_saddle_point -pc_fieldsplit_type schur >> -pc_fieldsplit_schur_precondition selfp >> -fieldsplit_1_mat_schur_complement_ainv_type lump >> -pc_fieldsplit_schur_fact_type full -fieldsplit_0_ksp_type preonly >> -fieldsplit_0_pc_type gamg -fieldsplit_0_mg_coarse_pc_type_type svd >> -fieldsplit_1_ksp_type preonly -fieldsplit_1_pc_type bjacobi >> -fieldsplit_1_sub_pc_type sor -ksp_view -ksp_monitor_true_residual >> -ksp_converged_reason -fieldsplit_0_mg_levels_ksp_monitor_true_residual >> -fieldsplit_0_mg_levels_ksp_converged_reason >> -fieldsplit_1_ksp_monitor_true_residual >> -fieldsplit_1_ksp_converged_reason >> > > This option was wrong: > > -fieldsplit_0_mg_coarse_pc_type_type svd > > from the output, we can see that it should have been > > -fieldsplit_0_mg_coarse_sub_pc_type_type svd > > THanks, > > Matt > > >> output: >> >> 0 KSP unpreconditioned resid norm 2.777777777778e+01 true resid norm >> 2.777777777778e+01 ||r(i)||/||b|| 1.000000000000e+00 >> Linear solve did not converge due to DIVERGED_PC_FAILED iterations 0 >> PC failed due to SUBPC_ERROR >> KSP Object: 1 MPI processes >> type: cg >> maximum iterations=200, initial guess is zero >> tolerances: relative=1e-06, absolute=1e-12, divergence=1e+30 >> left preconditioning >> using UNPRECONDITIONED norm type for convergence test >> PC Object: 1 MPI processes >> type: gamg >> type is MULTIPLICATIVE, levels=2 cycles=v >> Cycles per PCApply=1 >> Using externally compute Galerkin coarse grid matrices >> GAMG specific options >> Threshold for dropping small values in graph on each level = >> Threshold scaling factor for each level not specified = 1. >> AGG specific options >> Symmetric graph false >> Number of levels to square graph 1 >> Number smoothing steps 1 >> Complexity: grid = 1.00176 >> Coarse grid solver -- level ------------------------------- >> KSP Object: (mg_coarse_) 1 MPI processes >> type: preonly >> maximum iterations=10000, initial guess is zero >> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >> left preconditioning >> using NONE norm type for convergence test >> PC Object: (mg_coarse_) 1 MPI processes >> type: bjacobi >> number of blocks = 1 >> Local solver is the same for all blocks, as in the following KSP >> and PC objects on rank 0: >> KSP Object: (mg_coarse_sub_) 1 MPI processes >> type: preonly >> maximum iterations=1, initial guess is zero >> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >> left preconditioning >> using NONE norm type for convergence test >> PC Object: (mg_coarse_sub_) 1 MPI processes >> type: lu >> out-of-place factorization >> tolerance for zero pivot 2.22045e-14 >> using diagonal shift on blocks to prevent zero pivot [INBLOCKS] >> matrix ordering: nd >> factor fill ratio given 5., needed 1. >> Factored matrix follows: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=7, cols=7 >> package used to perform factorization: petsc >> total: nonzeros=45, allocated nonzeros=45 >> using I-node routines: found 3 nodes, limit used is 5 >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=7, cols=7 >> total: nonzeros=45, allocated nonzeros=45 >> total number of mallocs used during MatSetValues calls=0 >> using I-node routines: found 3 nodes, limit used is 5 >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=7, cols=7 >> total: nonzeros=45, allocated nonzeros=45 >> total number of mallocs used during MatSetValues calls=0 >> using nonscalable MatPtAP() implementation >> using I-node (on process 0) routines: found 3 nodes, limit used >> is 5 >> Down solver (pre-smoother) on level 1 ------------------------------- >> KSP Object: (mg_levels_1_) 1 MPI processes >> type: chebyshev >> eigenvalue estimates used: min = 0., max = 0. >> eigenvalues estimate via gmres min 0., max 0. >> eigenvalues estimated using gmres with translations [0. 0.1; 0. >> 1.1] >> KSP Object: (mg_levels_1_esteig_) 1 MPI processes >> type: gmres >> restart=30, using Classical (unmodified) Gram-Schmidt >> Orthogonalization with no iterative refinement >> 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 >> PC Object: (mg_levels_1_) 1 MPI processes >> type: sor >> type = local_symmetric, iterations = 1, local iterations = 1, >> omega = 1. >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=624, cols=624 >> total: nonzeros=25536, allocated nonzeros=25536 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 336 nodes, >> limit used is 5 >> estimating eigenvalues using noisy right hand side >> maximum iterations=2, nonzero initial guess >> tolerances: relative=1e-05, absolute=1e-50, divergence=10000. >> left preconditioning >> using NONE norm type for convergence test >> PC Object: (mg_levels_1_) 1 MPI processes >> type: sor >> type = local_symmetric, iterations = 1, local iterations = 1, >> omega = 1. linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=624, cols=624 >> total: nonzeros=25536, allocated nonzeros=25536 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 336 nodes, limit >> used is 5 Up solver (post-smoother) same as down solver (pre-smoother) >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=624, cols=624 >> total: nonzeros=25536, allocated nonzeros=25536 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 336 nodes, limit used >> is 5 >> >> >> Best regards, >> >> Xiaofeng >> >> >> On Jun 14, 2025, at 07:28, Barry Smith <bsm...@petsc.dev> wrote: >> >> >> Matt, >> >> Perhaps we should add options -ksp_monitor_debug and >> -snes_monitor_debug that turn on all possible monitoring for the (possibly) >> nested solvers and all of their converged reasons also? Note this is not >> completely trivial because each preconditioner will have to supply its list >> based on the current solver options for it. >> >> Then we won't need to constantly list a big string of problem specific >> monitor options to ask the user to use. >> >> Barry >> >> >> >> >> On Jun 13, 2025, at 9:09 AM, Matthew Knepley <knep...@gmail.com> wrote: >> >> On Thu, Jun 12, 2025 at 10:55 PM hexioafeng <hexiaof...@buaa.edu.cn> >> wrote: >> >>> Dear authors, >>> >>> I tried *-pc_type game -pc_gamg_parallel_coarse_grid_solver* and *-pc_type >>> field split -pc_fieldsplit_detect_saddle_point -fieldsplit_0_ksp_type >>> pronely -fieldsplit_0_pc_type game -fieldsplit_0_mg_coarse_pc_type sad >>> -fieldsplit_1_ksp_type pronely -fieldsplit_1_pc_type Jacobi >>> _fieldsplit_1_sub_pc_type for* , both options got the >>> KSP_DIVERGE_PC_FAILED error. >>> >> >> With any question about convergence, we need to see the output of >> >> -ksp_view -ksp_monitor_true_residual -ksp_converged_reason >> -fieldsplit_0_mg_levels_ksp_monitor_true_residual >> -fieldsplit_0_mg_levels_ksp_converged_reason >> -fieldsplit_1_ksp_monitor_true_residual -fieldsplit_1_ksp_converged_reason >> >> and all the error output. >> >> Thanks, >> >> Matt >> >> >>> Thanks, >>> >>> Xiaofeng >>> >>> >>> On Jun 12, 2025, at 20:50, Mark Adams <mfad...@lbl.gov> wrote: >>> >>> >>> >>> On Thu, Jun 12, 2025 at 8:44 AM Matthew Knepley <knep...@gmail.com> >>> wrote: >>> >>>> On Thu, Jun 12, 2025 at 4:58 AM Mark Adams <mfad...@lbl.gov> wrote: >>>> >>>>> Adding this to the PETSc mailing list, >>>>> >>>>> On Thu, Jun 12, 2025 at 3:43 AM hexioafeng <hexiaof...@buaa.edu.cn> >>>>> wrote: >>>>> >>>>>> >>>>>> Dear Professor, >>>>>> >>>>>> I hope this message finds you well. >>>>>> >>>>>> I am an employee at a CAE company and a heavy user of the PETSc >>>>>> library. I would like to thank you for your contributions to PETSc and >>>>>> express my deep appreciation for your work. >>>>>> >>>>>> Recently, I encountered some difficulties when using PETSc to solve >>>>>> structural mechanics problems with Lagrange multiplier constraints. After >>>>>> searching extensively online and reviewing several papers, I found your >>>>>> previous paper titled "*Algebraic multigrid methods for constrained >>>>>> linear systems with applications to contact problems in solid mechanics*" >>>>>> seems to be the most relevant and helpful. >>>>>> >>>>>> The stiffness matrix I'm working with, *K*, is a block saddle-point >>>>>> matrix of the form (A00 A01; A10 0), where *A00 is singular*—just as >>>>>> described in your paper, and different from many other articles . I have >>>>>> a >>>>>> few questions regarding your work and would greatly appreciate your >>>>>> insights: >>>>>> >>>>>> 1. Is the *AMG/KKT* method presented in your paper available in >>>>>> PETSc? I tried using *CG+GAMG* directly but received a >>>>>> *KSP_DIVERGED_PC_FAILED* error. I also attempted to use >>>>>> *CG+PCFIELDSPLIT* with the following options: >>>>>> >>>>> >>>>> No >>>>> >>>>> >>>>>> >>>>>> -pc_type fieldsplit -pc_fieldsplit_detect_saddle_point >>>>>> -pc_fieldsplit_type schur -pc_fieldsplit_schur_precondition selfp >>>>>> -pc_fieldsplit_schur_fact_type full -fieldsplit_0_ksp_type preonly >>>>>> -fieldsplit_0_pc_type gamg -fieldsplit_1_ksp_type preonly >>>>>> -fieldsplit_1_pc_type bjacobi >>>>>> >>>>>> Unfortunately, this also resulted in a *KSP_DIVERGED_PC_FAILED* error. >>>>>> Do you have any suggestions? >>>>>> >>>>>> 2. In your paper, you compare the method with *Uzawa*-type >>>>>> approaches. To my understanding, Uzawa methods typically require A00 to >>>>>> be >>>>>> invertible. How did you handle the singularity of A00 to construct an >>>>>> M-matrix that is invertible? >>>>>> >>>>>> >>>>> You add a regularization term like A01 * A10 (like springs). See the >>>>> paper or any reference to augmented lagrange or Uzawa >>>>> >>>>> >>>>> 3. Can i implement the AMG/KKT method in your paper using existing *AMG >>>>>> APIs*? Implementing a production-level AMG solver from scratch would >>>>>> be quite challenging for me, so I’m hoping to utilize existing AMG >>>>>> interfaces within PETSc or other packages. >>>>>> >>>>>> >>>>> You can do Uzawa and make the regularization matrix with matrix-matrix >>>>> products. Just use AMG for the A00 block. >>>>> >>>>> >>>>> >>>>>> 4. For saddle-point systems where A00 is singular, can you recommend >>>>>> any more robust or efficient solutions? Alternatively, are you aware of >>>>>> any >>>>>> open-source software packages that can handle such cases out-of-the-box? >>>>>> >>>>>> >>>>> No, and I don't think PETSc can do this out-of-the-box, but others may >>>>> be able to give you a better idea of what PETSc can do. >>>>> I think PETSc can do Uzawa or other similar algorithms but it will not >>>>> do the regularization automatically (it is a bit more complicated than >>>>> just >>>>> A01 * A10) >>>>> >>>> >>>> One other trick you can use is to have >>>> >>>> -fieldsplit_0_mg_coarse_pc_type svd >>>> >>>> This will use SVD on the coarse grid of GAMG, which can handle the null >>>> space in A00 as long as the prolongation does not put it back in. I have >>>> used this for the Laplacian with Neumann conditions and for freely floating >>>> elastic problems. >>>> >>>> >>> Good point. >>> You can also use -pc_gamg_parallel_coarse_grid_solver to get GAMG to use >>> a on level iterative solver for the coarse grid. >>> >>> >>>> Thanks, >>>> >>>> Matt >>>> >>>> >>>>> Thanks, >>>>> Mark >>>>> >>>>>> >>>>>> Thank you very much for taking the time to read my email. Looking >>>>>> forward to hearing from you. >>>>>> >>>>>> >>>>>> >>>>>> Sincerely, >>>>>> >>>>>> Xiaofeng He >>>>>> ----------------------------------------------------- >>>>>> >>>>>> Research Engineer >>>>>> >>>>>> Internet Based Engineering, Beijing, China >>>>>> >>>>>> >>>> >>>> -- >>>> 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 >>>> >>>> https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!fyBp94uNH39ug71rmxdPNy-fg09OxzGTv8H1_ulFiPksc9CZAblBrgYK7D2QWSJMYv-jihmxWTifwLLc1Sn2DSo$ >>>> >>>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!f-YJSzthRa7atIa1xs1GPHW53hGIqSenvp1eO2kDsSyf4jv1_Vp0kL9Lg8pyyPeG8al4Im8XlLqGRHw1FxYh$> >>>> >>> >>> >> >> -- >> What most experimenters take for granted before they begin their >> experiments is infinitely more interesting than any results to which their >> experiments lead. >> -- Norbert Wiener >> >> https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!fyBp94uNH39ug71rmxdPNy-fg09OxzGTv8H1_ulFiPksc9CZAblBrgYK7D2QWSJMYv-jihmxWTifwLLc1Sn2DSo$ >> >> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!f-YJSzthRa7atIa1xs1GPHW53hGIqSenvp1eO2kDsSyf4jv1_Vp0kL9Lg8pyyPeG8al4Im8XlLqGRHw1FxYh$> >> >> >> >> > > -- > What most experimenters take for granted before they begin their > experiments is infinitely more interesting than any results to which their > experiments lead. > -- Norbert Wiener > > https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!fyBp94uNH39ug71rmxdPNy-fg09OxzGTv8H1_ulFiPksc9CZAblBrgYK7D2QWSJMYv-jihmxWTifwLLc1Sn2DSo$ > > <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!dYETsi-moODALE1tmLrk5pxFKF9l552nNiC0cBgsCQ9ebugJWHtsNYa0QBS5Gmws9J_VC_Iec3Nx0c1FgNl1$> >
