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> 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> 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> 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!egZBIZkxo3gzmVhbpj-LqC0RWijjneLGmQ3sGX354yBmpAP5IhzpECOVON-QT9cwOy5aX1SSdofeEKFrRNlQ$
>>>>>
>>>>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!f-YJSzthRa7atIa1xs1GPHW53hGIqSenvp1eO2kDsSyf4jv1_Vp0kL9Lg8pyyPeG8al4Im8XlLqGRHw1FxYh$>
>>>>>
>>>>
>>>>
>>>
>>> --
>>> What most experimenters take for granted before they begin their
>>> experiments is infinitely more interesting than any results to which their
>>> experiments lead.
>>> -- Norbert Wiener
>>>
>>> https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!egZBIZkxo3gzmVhbpj-LqC0RWijjneLGmQ3sGX354yBmpAP5IhzpECOVON-QT9cwOy5aX1SSdofeEKFrRNlQ$
>>>
>>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!f-YJSzthRa7atIa1xs1GPHW53hGIqSenvp1eO2kDsSyf4jv1_Vp0kL9Lg8pyyPeG8al4Im8XlLqGRHw1FxYh$>
>>>
>>>
>>>
>>>
>>
>> --
>> What most experimenters take for granted before they begin their
>> experiments is infinitely more interesting than any results to which their
>> experiments lead.
>> -- Norbert Wiener
>>
>> https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!egZBIZkxo3gzmVhbpj-LqC0RWijjneLGmQ3sGX354yBmpAP5IhzpECOVON-QT9cwOy5aX1SSdofeEKFrRNlQ$
>>
>> <https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!dYETsi-moODALE1tmLrk5pxFKF9l552nNiC0cBgsCQ9ebugJWHtsNYa0QBS5Gmws9J_VC_Iec3Nx0c1FgNl1$>
>>
>
>
--
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
https://urldefense.us/v3/__https://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!egZBIZkxo3gzmVhbpj-LqC0RWijjneLGmQ3sGX354yBmpAP5IhzpECOVON-QT9cwOy5aX1SSdofeEKFrRNlQ$
<https://urldefense.us/v3/__http://www.cse.buffalo.edu/*knepley/__;fg!!G_uCfscf7eWS!egZBIZkxo3gzmVhbpj-LqC0RWijjneLGmQ3sGX354yBmpAP5IhzpECOVON-QT9cwOy5aX1SSdofeEOsuEPPR$
>
