KSP Object: 1 MPI process
  type: gmres
    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
    happy breakdown tolerance 1e-30
  maximum iterations=10000, initial guess is zero
  tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
  left preconditioning
  using DEFAULT norm type for convergence test
PC Object: 1 MPI process
  type: gamg
  PC has not been set up so information may be incomplete
    type is MULTIPLICATIVE, levels=0 cycles=unknown
      Cycles per PCApply=0
      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
          Number of levels of aggressive coarsening 1
          Square graph aggressive coarsening
          Coarsening algorithm not yet selected
          Number smoothing steps to construct prolongation 1
        Complexity:    grid = 0.    operator = 0.
        Per-level complexity: op = operator, int = interpolation
            #equations  | #active PEs | avg nnz/row op | avg nnz/row int
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=16, cols=16
    total: nonzeros=64, allocated nonzeros=64
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
KSP Object: 1 MPI process
  type: gmres
    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
    happy breakdown tolerance 1e-30
  maximum iterations=10000, initial guess is zero
  tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
  left preconditioning
  using PRECONDITIONED norm type for convergence test
PC Object: 1 MPI process
  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 =   -1.   -1.  
        Threshold scaling factor for each level not specified = 1.
        AGG specific options
          Number of levels of aggressive coarsening 1
          Square graph aggressive coarsening
          MatCoarsen Object: (pc_gamg_) 1 MPI process
            type: mis
          Number smoothing steps to construct prolongation 1
        Complexity:    grid = 1.1875    operator = 1.14062
        Per-level complexity: op = operator, int = interpolation
            #equations  | #active PEs | avg nnz/row op | avg nnz/row int
                  3            1              3                0
                 16            1              4                2
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 1 MPI process
      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 process
      type: bjacobi
        number of blocks = 1
        Local solver information for first block is in the following KSP and PC objects on rank 0:
        Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks
        KSP Object: (mg_coarse_sub_) 1 MPI process
          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 process
          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: (mg_coarse_sub_) 1 MPI process
                  type: seqaij
                  rows=3, cols=3
                  package used to perform factorization: petsc
                  total: nonzeros=9, allocated nonzeros=9
                    using I-node routines: found 1 nodes, limit used is 5
          linear system matrix = precond matrix:
          Mat Object: (mg_coarse_sub_) 1 MPI process
            type: seqaij
            rows=3, cols=3
            total: nonzeros=9, allocated nonzeros=9
            total number of mallocs used during MatSetValues calls=0
              using I-node routines: found 1 nodes, limit used is 5
      linear system matrix = precond matrix:
      Mat Object: (mg_coarse_sub_) 1 MPI process
        type: seqaij
        rows=3, cols=3
        total: nonzeros=9, allocated nonzeros=9
        total number of mallocs used during MatSetValues calls=0
          using I-node routines: found 1 nodes, limit used is 5
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 1 MPI process
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.514268, max 5.65695
        eigenvalues provided (min 0.299461, max 5.14268) with transform: [0. 0.1; 0. 1.1]
      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 process
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 1 MPI process
        type: seqaij
        rows=16, cols=16
        total: nonzeros=64, allocated nonzeros=64
        total number of mallocs used during MatSetValues calls=0
          not using I-node routines
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=16, cols=16
    total: nonzeros=64, allocated nonzeros=64
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
KSP Object: 1 MPI process
  type: gmres
    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
    happy breakdown tolerance 1e-30
  maximum iterations=10000, initial guess is zero
  tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
  left preconditioning
  using PRECONDITIONED norm type for convergence test
PC Object: 1 MPI process
  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 =   -1.   -1.  
        Threshold scaling factor for each level not specified = 1.
        AGG specific options
          Number of levels of aggressive coarsening 1
          Square graph aggressive coarsening
          MatCoarsen Object: (pc_gamg_) 1 MPI process
            type: mis
          Number smoothing steps to construct prolongation 1
        Complexity:    grid = 1.1875    operator = 1.14062
        Per-level complexity: op = operator, int = interpolation
            #equations  | #active PEs | avg nnz/row op | avg nnz/row int
                  3            1              3                0
                 16            1              4                2
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 1 MPI process
      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 process
      type: bjacobi
        number of blocks = 1
        Local solver information for first block is in the following KSP and PC objects on rank 0:
        Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks
        KSP Object: (mg_coarse_sub_) 1 MPI process
          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 process
          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: (mg_coarse_sub_) 1 MPI process
                  type: seqaij
                  rows=3, cols=3
                  package used to perform factorization: petsc
                  total: nonzeros=9, allocated nonzeros=9
                    using I-node routines: found 1 nodes, limit used is 5
          linear system matrix = precond matrix:
          Mat Object: (mg_coarse_sub_) 1 MPI process
            type: seqaij
            rows=3, cols=3
            total: nonzeros=9, allocated nonzeros=9
            total number of mallocs used during MatSetValues calls=0
              using I-node routines: found 1 nodes, limit used is 5
      linear system matrix = precond matrix:
      Mat Object: (mg_coarse_sub_) 1 MPI process
        type: seqaij
        rows=3, cols=3
        total: nonzeros=9, allocated nonzeros=9
        total number of mallocs used during MatSetValues calls=0
          using I-node routines: found 1 nodes, limit used is 5
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 1 MPI process
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.159372, max 1.75309
        eigenvalues estimated via gmres: min 0.406283, max 1.59372
        eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]
        KSP Object: (mg_levels_1_esteig_) 1 MPI process
          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 a noisy random number generated 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 process
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 1 MPI process
        type: seqaij
        rows=16, cols=16
        total: nonzeros=64, allocated nonzeros=64
        total number of mallocs used during MatSetValues calls=0
          not using I-node routines
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=16, cols=16
    total: nonzeros=64, allocated nonzeros=64
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
KSP Object: 1 MPI process
  type: gmres
    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement
    happy breakdown tolerance 1e-30
  maximum iterations=10000, initial guess is zero
  tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
  left preconditioning
  using PRECONDITIONED norm type for convergence test
PC Object: 1 MPI process
  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 =   -1.   -1.  
        Threshold scaling factor for each level not specified = 1.
        AGG specific options
          Number of levels of aggressive coarsening 1
          Square graph aggressive coarsening
          MatCoarsen Object: (pc_gamg_) 1 MPI process
            type: mis
          Number smoothing steps to construct prolongation 1
        Complexity:    grid = 1.1875    operator = 1.14062
        Per-level complexity: op = operator, int = interpolation
            #equations  | #active PEs | avg nnz/row op | avg nnz/row int
                  3            1              3                0
                 16            1              4                2
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 1 MPI process
      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 process
      type: bjacobi
        number of blocks = 1
        Local solver information for first block is in the following KSP and PC objects on rank 0:
        Use -mg_coarse_ksp_view ::ascii_info_detail to display information for all blocks
        KSP Object: (mg_coarse_sub_) 1 MPI process
          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 process
          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: (mg_coarse_sub_) 1 MPI process
                  type: seqaij
                  rows=3, cols=3
                  package used to perform factorization: petsc
                  total: nonzeros=9, allocated nonzeros=9
                    using I-node routines: found 1 nodes, limit used is 5
          linear system matrix = precond matrix:
          Mat Object: (mg_coarse_sub_) 1 MPI process
            type: seqaij
            rows=3, cols=3
            total: nonzeros=9, allocated nonzeros=9
            total number of mallocs used during MatSetValues calls=0
              using I-node routines: found 1 nodes, limit used is 5
      linear system matrix = precond matrix:
      Mat Object: (mg_coarse_sub_) 1 MPI process
        type: seqaij
        rows=3, cols=3
        total: nonzeros=9, allocated nonzeros=9
        total number of mallocs used during MatSetValues calls=0
          using I-node routines: found 1 nodes, limit used is 5
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 1 MPI process
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.160581, max 1.76639
        eigenvalues estimated via gmres: min 0.394193, max 1.60581
        eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]
        KSP Object: (mg_levels_1_esteig_) 1 MPI process
          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 a noisy random number generated 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 process
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 1 MPI process
        type: seqaij
        rows=16, cols=16
        total: nonzeros=64, allocated nonzeros=64
        total number of mallocs used during MatSetValues calls=0
          not using I-node routines
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=16, cols=16
    total: nonzeros=64, allocated nonzeros=64
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
