Hello sir, I remove the duplicated "_type", and get the same error and output.
Best regards, Xiaofeng > On Jun 19, 2025, at 19:45, Matthew Knepley <knep...@gmail.com> wrote: > > This options is wrong > > -fieldsplit_0_mg_coarse_sub_pc_type_type svd > > Notice that "_type" is repeated. > > Thanks, > > Matt > > On Thu, Jun 19, 2025 at 7:10 AM hexioafeng <hexiaof...@buaa.edu.cn > <mailto:hexiaof...@buaa.edu.cn>> wrote: >> Dear authors, >> >> Here are the options passed with fieldsplit preconditioner: >> >> -ksp_type cg -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_0_mg_coarse_sub_pc_type_type svd >> -fieldsplit_1_ksp_type preonly -fieldsplit_1_pc_type bjacobi -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 the output: >> >> 0 KSP unpreconditioned resid norm 2.777777777778e+01 true resid norm >> 2.777777777778e+01 ||r(i)||/||b|| 1.000000000000e+00 >> Linear fieldsplit_0_mg_levels_1_ solve converged due to CONVERGED_ITS >> iterations 2 >> Linear fieldsplit_0_mg_levels_1_ solve converged due to CONVERGED_ITS >> iterations 2 >> Linear fieldsplit_1_ solve did not converge due to DIVERGED_PC_FAILED >> iterations 0 >> PC failed due to SUBPC_ERROR >> Linear fieldsplit_0_mg_levels_1_ solve converged due to CONVERGED_ITS >> iterations 2 >> Linear fieldsplit_0_mg_levels_1_ solve converged due to CONVERGED_ITS >> iterations 2 >> 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: fieldsplit >> FieldSplit with Schur preconditioner, blocksize = 1, factorization FULL >> Preconditioner for the Schur complement formed from Sp, an assembled >> approximation to S, which uses A00's diagonal's inverse >> Split info: >> Split number 0 Defined by IS >> Split number 1 Defined by IS >> KSP solver for A00 block >> KSP Object: (fieldsplit_0_) 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: (fieldsplit_0_) 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 = 1.00222 >> Coarse grid solver -- level ------------------------------- >> KSP Object: (fieldsplit_0_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: (fieldsplit_0_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: (fieldsplit_0_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: (fieldsplit_0_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=8, cols=8 >> package used to perform factorization: petsc >> total: nonzeros=56, allocated nonzeros=56 >> using I-node routines: found 3 nodes, limit used is 5 >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=8, cols=8 >> total: nonzeros=56, allocated nonzeros=56 >> 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=8, cols=8 >> total: nonzeros=56, allocated nonzeros=56 >> 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: (fieldsplit_0_mg_levels_1_) 1 MPI processes >> type: chebyshev >> eigenvalue estimates used: min = 0.0998145, max = 1.09796 >> eigenvalues estimate via gmres min 0.00156735, max 0.998145 >> eigenvalues estimated using gmres with translations [0. 0.1; >> 0. 1.1] >> KSP Object: (fieldsplit_0_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 >> 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: (fieldsplit_0_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: (fieldsplit_0_) 1 MPI processes >> type: mpiaij >> rows=480, cols=480 >> total: nonzeros=25200, allocated nonzeros=25200 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 160 nodes, limit >> used is 5 >> Up solver (post-smoother) same as down solver (pre-smoother) >> linear system matrix = precond matrix: >> Mat Object: (fieldsplit_0_) 1 MPI processes >> type: mpiaij >> rows=480, cols=480 >> total: nonzeros=25200, allocated nonzeros=25200 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 160 nodes, limit >> used is 5 >> KSP solver for S = A11 - A10 inv(A00) A01 >> KSP Object: (fieldsplit_1_) 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: (fieldsplit_1_) 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: (fieldsplit_1_sub_) 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: (fieldsplit_1_sub_) 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: (fieldsplit_1_sub_sub_) >> 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: (fieldsplit_1_sub_sub_) 1 >> MPI processes >> type: ilu >> out-of-place factorization >> 0 levels of fill >> tolerance for zero pivot 2.22045e-14 >> matrix ordering: natural >> factor fill ratio given 1., needed 1. >> Factored matrix follows: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=144, cols=144 >> package used to perform factorization: petsc >> total: nonzeros=240, allocated nonzeros=240 >> not using I-node routines >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=144, cols=144 >> total: nonzeros=240, allocated nonzeros=240 >> total number of mallocs used during MatSetValues >> calls=0 >> not using I-node routines >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=144, cols=144 >> total: nonzeros=240, allocated nonzeros=240 >> total number of mallocs used during MatSetValues calls=0 >> not using I-node (on process 0) routines >> linear system matrix followed by preconditioner matrix: >> Mat Object: (fieldsplit_1_) 1 MPI processes >> type: schurcomplement >> rows=144, cols=144 >> Schur complement A11 - A10 inv(A00) A01 >> A11 >> Mat Object: (fieldsplit_1_) 1 MPI processes >> type: mpiaij >> rows=144, cols=144 >> total: nonzeros=240, allocated nonzeros=240 >> total number of mallocs used during MatSetValues calls=0 >> not using I-node (on process 0) routines >> A10 >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=144, cols=480 >> total: nonzeros=48, allocated nonzeros=48 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 74 nodes, >> limit used is 5 >> KSP of A00 >> KSP Object: (fieldsplit_0_) 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: (fieldsplit_0_) 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 = 1.00222 >> Coarse grid solver -- level ------------------------------- >> KSP Object: (fieldsplit_0_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: (fieldsplit_0_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: (fieldsplit_0_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: (fieldsplit_0_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=8, cols=8 >> package used to perform factorization: petsc >> total: nonzeros=56, allocated nonzeros=56 >> using I-node routines: found 3 nodes, limit >> used is 5 >> linear system matrix = precond matrix: >> Mat Object: 1 MPI processes >> type: seqaij >> rows=8, cols=8 >> total: nonzeros=56, allocated nonzeros=56 >> 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=8, cols=8 >> total: nonzeros=56, allocated nonzeros=56 >> 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: (fieldsplit_0_mg_levels_1_) 1 MPI processes >> type: chebyshev >> eigenvalue estimates used: min = 0.0998145, max = >> 1.09796 >> eigenvalues estimate via gmres min 0.00156735, max >> 0.998145 >> eigenvalues estimated using gmres with translations >> [0. 0.1; 0. 1.1] >> KSP Object: (fieldsplit_0_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 >> 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: (fieldsplit_0_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: (fieldsplit_0_) 1 MPI processes >> type: mpiaij >> rows=480, cols=480 >> total: nonzeros=25200, allocated nonzeros=25200 >> total number of mallocs used during MatSetValues >> calls=0 >> using I-node (on process 0) routines: found 160 >> nodes, limit used is 5 >> Up solver (post-smoother) same as down solver (pre-smoother) >> linear system matrix = precond matrix: >> Mat Object: (fieldsplit_0_) 1 MPI processes >> type: mpiaij >> rows=480, cols=480 >> total: nonzeros=25200, allocated nonzeros=25200 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 160 nodes, >> limit used is 5 >> A01 >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=480, cols=144 >> total: nonzeros=48, allocated nonzeros=48 >> total number of mallocs used during MatSetValues calls=0 >> using I-node (on process 0) routines: found 135 nodes, >> limit used is 5 >> Mat Object: 1 MPI processes >> type: mpiaij >> rows=144, cols=144 >> total: nonzeros=240, allocated nonzeros=240 >> total number of mallocs used during MatSetValues calls=0 >> not using I-node (on process 0) routines >> 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 >> >> >> Thanks, >> Xiaofeng >> >> >> >>> On Jun 17, 2025, at 19:05, Mark Adams <mfad...@lbl.gov >>> <mailto:mfad...@lbl.gov>> wrote: >>> >>> 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 >>> <mailto:knep...@gmail.com>> wrote: >>>> On Sun, Jun 15, 2025 at 9:46 PM hexioafeng <hexiaof...@buaa.edu.cn >>>> <mailto: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 >>>>>> <mailto: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 >>>>>>> <mailto:knep...@gmail.com>> wrote: >>>>>>> >>>>>>> On Thu, Jun 12, 2025 at 10:55 PM hexioafeng <hexiaof...@buaa.edu.cn >>>>>>> <mailto: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 >>>>>>>>> <mailto:mfad...@lbl.gov>> wrote: >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> On Thu, Jun 12, 2025 at 8:44 AM Matthew Knepley <knep...@gmail.com >>>>>>>>> <mailto:knep...@gmail.com>> wrote: >>>>>>>>>> On Thu, Jun 12, 2025 at 4:58 AM Mark Adams <mfad...@lbl.gov >>>>>>>>>> <mailto: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 >>>>>>>>>>> <mailto: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!ZB1cNFj48U8eb5ckEu57heuMiZw2CzbgK4EOR8laI0ulI607DgVUe-wDatJn7gTSAcRZv8Bcw3BmbXZ0gJOgxQicaGoXrw$ >>>>>>>>>> >>>>>>>>>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!f-YJSzthRa7atIa1xs1GPHW53hGIqSenvp1eO2kDsSyf4jv1_Vp0kL9Lg8pyyPeG8al4Im8XlLqGRHw1FxYh$> >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> What most experimenters take for granted before they begin their >>>>>>> experiments is infinitely more interesting than any results to which >>>>>>> their experiments lead. >>>>>>> -- Norbert Wiener >>>>>>> >>>>>>> https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!ZB1cNFj48U8eb5ckEu57heuMiZw2CzbgK4EOR8laI0ulI607DgVUe-wDatJn7gTSAcRZv8Bcw3BmbXZ0gJOgxQicaGoXrw$ >>>>>>> >>>>>>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!f-YJSzthRa7atIa1xs1GPHW53hGIqSenvp1eO2kDsSyf4jv1_Vp0kL9Lg8pyyPeG8al4Im8XlLqGRHw1FxYh$> >>>>>> >>>>> >>>> >>>> >>>> >>>> -- >>>> What most experimenters take for granted before they begin their >>>> experiments is infinitely more interesting than any results to which their >>>> experiments lead. >>>> -- Norbert Wiener >>>> >>>> https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!ZB1cNFj48U8eb5ckEu57heuMiZw2CzbgK4EOR8laI0ulI607DgVUe-wDatJn7gTSAcRZv8Bcw3BmbXZ0gJOgxQicaGoXrw$ >>>> >>>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!dYETsi-moODALE1tmLrk5pxFKF9l552nNiC0cBgsCQ9ebugJWHtsNYa0QBS5Gmws9J_VC_Iec3Nx0c1FgNl1$> >> > > > > -- > What most experimenters take for granted before they begin their experiments > is infinitely more interesting than any results to which their experiments > lead. > -- Norbert Wiener > > https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!ZB1cNFj48U8eb5ckEu57heuMiZw2CzbgK4EOR8laI0ulI607DgVUe-wDatJn7gTSAcRZv8Bcw3BmbXZ0gJOgxQicaGoXrw$ > > <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!ZB1cNFj48U8eb5ckEu57heuMiZw2CzbgK4EOR8laI0ulI607DgVUe-wDatJn7gTSAcRZv8Bcw3BmbXZ0gJOgxQjTnTB2XQ$ > >
