  0 KSP Residual norm 529.43
  1 KSP Residual norm 135.942
  2 KSP Residual norm 269.201
  3 KSP Residual norm 142.532
  4 KSP Residual norm 143.986
  5 KSP Residual norm 61.5152
  6 KSP Residual norm 45.2898
  7 KSP Residual norm 22.6601
  8 KSP Residual norm 29.096
  9 KSP Residual norm 40.9984
 10 KSP Residual norm 31.3586
 11 KSP Residual norm 30.0529
 12 KSP Residual norm 17.2767
 13 KSP Residual norm 8.5664
 14 KSP Residual norm 8.77142
 15 KSP Residual norm 7.24158
 16 KSP Residual norm 3.40719
 17 KSP Residual norm 1.35954
 18 KSP Residual norm 0.784024
 19 KSP Residual norm 0.490711
 20 KSP Residual norm 0.302938
 21 KSP Residual norm 0.213342
 22 KSP Residual norm 0.189106
 23 KSP Residual norm 0.154914
 24 KSP Residual norm 0.187313
 25 KSP Residual norm 0.185263
 26 KSP Residual norm 0.0865907
 27 KSP Residual norm 0.0370298
 28 KSP Residual norm 0.0115801
 29 KSP Residual norm 0.00441017
Linear solve converged due to CONVERGED_RTOL iterations 29
KSP Object: 8 MPI processes
  type: cg
  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: 8 MPI processes
  type: gamg
    type is MULTIPLICATIVE, levels=4 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 =   -0.01   -0.01   -0.01   -0.01  
        Threshold scaling factor for each level not specified = 1.
        Using parallel coarse grid solver (all coarse grid equations not put on one process)
        Using injection restriction/prolongation on first level, dofs:   1   2  
        AGG specific options
          Number of levels of aggressive coarsening 1
          MIS-k aggressive coarsening
          MIS-2 coarsening on aggressive levels
          Number smoothing steps 1
        Complexity:    grid = 1.748    operator = 1.52855
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 8 MPI processes
      type: cg
      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: (mg_coarse_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=16, cols=16, bs=2
        total: nonzeros=256, allocated nonzeros=256
        total number of mallocs used during MatSetValues calls=0
          using I-node (on process 0) routines: found 4 nodes, limit used is 5
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 8 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.240118, max 1.26062
        eigenvalues estimated via cg: min 0.0570861, max 1.20059
        eigenvalues estimated using cg with transform: [0. 0.2; 0. 1.05]
        KSP Object: (mg_levels_1_esteig_) 8 MPI processes
          type: cg
          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_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=228, cols=228, bs=2
        total: nonzeros=16360, allocated nonzeros=16360
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 4 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 2 -------------------------------
    KSP Object: (mg_levels_2_) 8 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.621977, max 3.26538
        eigenvalues estimated via cg: min 0.0471395, max 3.10988
        eigenvalues estimated using cg with transform: [0. 0.2; 0. 1.05]
        KSP Object: (mg_levels_2_esteig_) 8 MPI processes
          type: cg
          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_2_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=2000, cols=2000, bs=2
        total: nonzeros=87808, allocated nonzeros=87808
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 125 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 3 -------------------------------
    KSP Object: (mg_fine_) 8 MPI processes
      type: richardson
        damping factor=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_fine_) 8 MPI processes
      type: jacobi
        type ROWL1 (l1-norm off-diagonal scaling 2.500000e-01)
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=3000, cols=3000, bs=3
        total: nonzeros=197568, allocated nonzeros=243000
        total number of mallocs used during MatSetValues calls=0
          using I-node (on process 0) routines: found 125 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix = precond matrix:
  Mat Object: 8 MPI processes
    type: mpiaij
    rows=3000, cols=3000, bs=3
    total: nonzeros=197568, allocated nonzeros=243000
    total number of mallocs used during MatSetValues calls=0
      using I-node (on process 0) routines: found 125 nodes, limit used is 5
  0 KSP Residual norm 0.0052943
  1 KSP Residual norm 0.00135942
  2 KSP Residual norm 0.00269201
  3 KSP Residual norm 0.00142532
  4 KSP Residual norm 0.00143986
  5 KSP Residual norm 0.000615152
  6 KSP Residual norm 0.000452898
  7 KSP Residual norm 0.000226601
  8 KSP Residual norm 0.00029096
  9 KSP Residual norm 0.000409984
 10 KSP Residual norm 0.000313586
 11 KSP Residual norm 0.000300529
 12 KSP Residual norm 0.000172767
 13 KSP Residual norm 8.5664e-05
 14 KSP Residual norm 8.77142e-05
 15 KSP Residual norm 7.24158e-05
 16 KSP Residual norm 3.40719e-05
 17 KSP Residual norm 1.35954e-05
 18 KSP Residual norm 7.84024e-06
 19 KSP Residual norm 4.90711e-06
 20 KSP Residual norm 3.02938e-06
 21 KSP Residual norm 2.13342e-06
 22 KSP Residual norm 1.89106e-06
 23 KSP Residual norm 1.54914e-06
 24 KSP Residual norm 1.87313e-06
 25 KSP Residual norm 1.85263e-06
 26 KSP Residual norm 8.65907e-07
 27 KSP Residual norm 3.70298e-07
 28 KSP Residual norm 1.15801e-07
 29 KSP Residual norm 4.41017e-08
Linear solve converged due to CONVERGED_RTOL iterations 29
KSP Object: 8 MPI processes
  type: cg
  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: 8 MPI processes
  type: gamg
    type is MULTIPLICATIVE, levels=4 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 =   -0.01   -0.01   -0.01   -0.01  
        Threshold scaling factor for each level not specified = 1.
        Using parallel coarse grid solver (all coarse grid equations not put on one process)
        Using injection restriction/prolongation on first level, dofs:   1   2  
        AGG specific options
          Number of levels of aggressive coarsening 1
          MIS-k aggressive coarsening
          MIS-2 coarsening on aggressive levels
          Number smoothing steps 1
        Complexity:    grid = 1.748    operator = 1.52855
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 8 MPI processes
      type: cg
      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: (mg_coarse_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=16, cols=16, bs=2
        total: nonzeros=256, allocated nonzeros=256
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 4 nodes, limit used is 5
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 8 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.240118, max 1.26062
        eigenvalues estimated via cg: min 0.0570861, max 1.20059
        eigenvalues estimated using cg with transform: [0. 0.2; 0. 1.05]
        KSP Object: (mg_levels_1_esteig_) 8 MPI processes
          type: cg
          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_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=228, cols=228, bs=2
        total: nonzeros=16360, allocated nonzeros=16360
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 4 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 2 -------------------------------
    KSP Object: (mg_levels_2_) 8 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.621977, max 3.26538
        eigenvalues estimated via cg: min 0.0471395, max 3.10988
        eigenvalues estimated using cg with transform: [0. 0.2; 0. 1.05]
        KSP Object: (mg_levels_2_esteig_) 8 MPI processes
          type: cg
          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_2_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=2000, cols=2000, bs=2
        total: nonzeros=87808, allocated nonzeros=87808
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 125 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 3 -------------------------------
    KSP Object: (mg_fine_) 8 MPI processes
      type: richardson
        damping factor=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_fine_) 8 MPI processes
      type: jacobi
        type ROWL1 (l1-norm off-diagonal scaling 2.500000e-01)
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=3000, cols=3000, bs=3
        total: nonzeros=197568, allocated nonzeros=243000
        total number of mallocs used during MatSetValues calls=0
          using I-node (on process 0) routines: found 125 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix = precond matrix:
  Mat Object: 8 MPI processes
    type: mpiaij
    rows=3000, cols=3000, bs=3
    total: nonzeros=197568, allocated nonzeros=243000
    total number of mallocs used during MatSetValues calls=0
      using I-node (on process 0) routines: found 125 nodes, limit used is 5
  0 KSP Residual norm 0.0052943
  1 KSP Residual norm 0.00135942
  2 KSP Residual norm 0.00269201
  3 KSP Residual norm 0.00142532
  4 KSP Residual norm 0.00143986
  5 KSP Residual norm 0.000615152
  6 KSP Residual norm 0.000452898
  7 KSP Residual norm 0.000226601
  8 KSP Residual norm 0.00029096
  9 KSP Residual norm 0.000409984
 10 KSP Residual norm 0.000313586
 11 KSP Residual norm 0.000300529
 12 KSP Residual norm 0.000172767
 13 KSP Residual norm 8.5664e-05
 14 KSP Residual norm 8.77142e-05
 15 KSP Residual norm 7.24158e-05
 16 KSP Residual norm 3.40719e-05
 17 KSP Residual norm 1.35954e-05
 18 KSP Residual norm 7.84024e-06
 19 KSP Residual norm 4.90711e-06
 20 KSP Residual norm 3.02938e-06
 21 KSP Residual norm 2.13342e-06
 22 KSP Residual norm 1.89106e-06
 23 KSP Residual norm 1.54914e-06
 24 KSP Residual norm 1.87313e-06
 25 KSP Residual norm 1.85263e-06
 26 KSP Residual norm 8.65907e-07
 27 KSP Residual norm 3.70298e-07
 28 KSP Residual norm 1.15801e-07
 29 KSP Residual norm 4.41017e-08
Linear solve converged due to CONVERGED_RTOL iterations 29
KSP Object: 8 MPI processes
  type: cg
  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: 8 MPI processes
  type: gamg
    type is MULTIPLICATIVE, levels=4 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 =   -0.01   -0.01   -0.01   -0.01  
        Threshold scaling factor for each level not specified = 1.
        Using parallel coarse grid solver (all coarse grid equations not put on one process)
        Using injection restriction/prolongation on first level, dofs:   1   2  
        AGG specific options
          Number of levels of aggressive coarsening 1
          MIS-k aggressive coarsening
          MIS-2 coarsening on aggressive levels
          Number smoothing steps 1
        Complexity:    grid = 1.748    operator = 1.52855
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 8 MPI processes
      type: cg
      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: (mg_coarse_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=16, cols=16, bs=2
        total: nonzeros=256, allocated nonzeros=256
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 4 nodes, limit used is 5
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 8 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.240118, max 1.26062
        eigenvalues estimated via cg: min 0.0570861, max 1.20059
        eigenvalues estimated using cg with transform: [0. 0.2; 0. 1.05]
        KSP Object: (mg_levels_1_esteig_) 8 MPI processes
          type: cg
          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_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=228, cols=228, bs=2
        total: nonzeros=16360, allocated nonzeros=16360
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 4 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 2 -------------------------------
    KSP Object: (mg_levels_2_) 8 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.621977, max 3.26538
        eigenvalues estimated via cg: min 0.0471395, max 3.10988
        eigenvalues estimated using cg with transform: [0. 0.2; 0. 1.05]
        KSP Object: (mg_levels_2_esteig_) 8 MPI processes
          type: cg
          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_2_) 8 MPI processes
      type: jacobi
        type DIAGONAL
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=2000, cols=2000, bs=2
        total: nonzeros=87808, allocated nonzeros=87808
        total number of mallocs used during MatSetValues calls=0
          using nonscalable MatPtAP() implementation
          using I-node (on process 0) routines: found 125 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 3 -------------------------------
    KSP Object: (mg_fine_) 8 MPI processes
      type: richardson
        damping factor=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_fine_) 8 MPI processes
      type: jacobi
        type ROWL1 (l1-norm off-diagonal scaling 2.500000e-01)
      linear system matrix = precond matrix:
      Mat Object: 8 MPI processes
        type: mpiaij
        rows=3000, cols=3000, bs=3
        total: nonzeros=197568, allocated nonzeros=243000
        total number of mallocs used during MatSetValues calls=0
          using I-node (on process 0) routines: found 125 nodes, limit used is 5
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix = precond matrix:
  Mat Object: 8 MPI processes
    type: mpiaij
    rows=3000, cols=3000, bs=3
    total: nonzeros=197568, allocated nonzeros=243000
    total number of mallocs used during MatSetValues calls=0
      using I-node (on process 0) routines: found 125 nodes, limit used is 5
[0]main |b-Ax|/|b|=1.165490e-04, |b|=5.391826e+00, emax=9.990809e-01
