  0 KSP Residual norm 0.0443241
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 2.33385
      1 KSP Residual norm 0.149578
      2 KSP Residual norm 1.22679
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 1.23503
      1 KSP Residual norm 0.0794808
      2 KSP Residual norm 0.648684
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 1.78001
    1 KSP Residual norm 0.105963
    2 KSP Residual norm 0.937442
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.340912
      1 KSP Residual norm 0.0213783
      2 KSP Residual norm 0.17921
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.179876
      1 KSP Residual norm 0.0100323
      2 KSP Residual norm 0.0948167
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.874599
    1 KSP Residual norm 0.0478285
    2 KSP Residual norm 0.461302
  1 KSP Residual norm 0.0185809
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.114582
      1 KSP Residual norm 0.012349
      2 KSP Residual norm 0.0594869
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0591948
      1 KSP Residual norm 0.00584115
      2 KSP Residual norm 0.0309345
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.993391
    1 KSP Residual norm 0.0537321
    2 KSP Residual norm 0.523741
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0192774
      1 KSP Residual norm 0.00266358
      2 KSP Residual norm 0.00984219
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.00893108
      1 KSP Residual norm 0.00111185
      2 KSP Residual norm 0.0046294
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.523143
    1 KSP Residual norm 0.0273265
    2 KSP Residual norm 0.276048
  2 KSP Residual norm 0.000795661
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.998379
      1 KSP Residual norm 0.0737639
      2 KSP Residual norm 0.523585
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.518305
      1 KSP Residual norm 0.0343328
      2 KSP Residual norm 0.273078
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.401463
    1 KSP Residual norm 0.0509673
    2 KSP Residual norm 0.203322
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.149823
      1 KSP Residual norm 0.0151393
      2 KSP Residual norm 0.0784378
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0721186
      1 KSP Residual norm 0.00488792
      2 KSP Residual norm 0.0379712
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.12669
    1 KSP Residual norm 0.0190126
    2 KSP Residual norm 0.0636628
  3 KSP Residual norm 3.98257e-05
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.47043
      1 KSP Residual norm 0.092957
      2 KSP Residual norm 0.234014
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.239915
      1 KSP Residual norm 0.0468276
      2 KSP Residual norm 0.11874
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 1.16151
    1 KSP Residual norm 0.191695
    2 KSP Residual norm 0.560581
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.133266
      1 KSP Residual norm 0.0357375
      2 KSP Residual norm 0.0628862
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0524995
      1 KSP Residual norm 0.00991596
      2 KSP Residual norm 0.0264384
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.485175
    1 KSP Residual norm 0.0798161
    2 KSP Residual norm 0.23725
  4 KSP Residual norm 2.40449e-06
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.635269
      1 KSP Residual norm 0.140149
      2 KSP Residual norm 0.312191
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.358238
      1 KSP Residual norm 0.0742635
      2 KSP Residual norm 0.174384
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.838294
    1 KSP Residual norm 0.107736
    2 KSP Residual norm 0.433668
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.201841
      1 KSP Residual norm 0.042807
      2 KSP Residual norm 0.0951668
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0819979
      1 KSP Residual norm 0.0177626
      2 KSP Residual norm 0.0403337
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.412919
    1 KSP Residual norm 0.054804
    2 KSP Residual norm 0.212956
  5 KSP Residual norm 1.35943e-07
KSP Object: 2 MPI processes
  type: fgmres
    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.
  right preconditioning
  using UNPRECONDITIONED norm type for convergence test
PC Object: 2 MPI processes
  type: mg
    type is FULL, levels=3 cycles=v
      Not using Galerkin computed coarse grid matrices
  Coarse grid solver -- level 0 -------------------------------
    KSP Object: (mg_coarse_) 2 MPI processes
      type: preonly
      maximum iterations=10000, initial guess is zero
      tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
      left preconditioning
      not checking for convergence
    PC Object: (mg_coarse_) 2 MPI processes
      type: redundant
        First (color=0) of 2 PCs follows
        KSP Object: (mg_coarse_redundant_) 1 MPI process
          type: preonly
          maximum iterations=10000, initial guess is zero
          tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
          left preconditioning
          not checking for convergence
        PC Object: (mg_coarse_redundant_) 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 1.60209
              Factored matrix:
                Mat Object: (mg_coarse_redundant_) 1 MPI process
                  type: seqaij
                  rows=128, cols=128
                  package used to perform factorization: petsc
                  total: nonzeros=612, allocated nonzeros=612
                    not using I-node routines
          linear system matrix, which is also used to construct the preconditioner:
          Mat Object: 1 MPI process
            type: seqaij
            rows=128, cols=128
            total: nonzeros=382, allocated nonzeros=382
            total number of mallocs used during MatSetValues calls=0
              not using I-node routines
      linear system matrix, which is also used to construct the preconditioner:
      Mat Object: 2 MPI processes
        type: mpiaij
        rows=128, cols=128
        total: nonzeros=382, allocated nonzeros=768
        total number of mallocs used during MatSetValues calls=0
  Down solver (pre-smoother) on level 1 -------------------------------
    KSP Object: (mg_levels_1_) 2 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.141423, max 1.55565
        eigenvalues estimated via gmres: min 0.0186576, max 1.41423
        eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]
        KSP Object: (mg_levels_1_esteig_) 2 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 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
      not checking for convergence
    PC Object: (mg_levels_1_) 2 MPI processes
      type: sor
        type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.
      linear system matrix, which is also used to construct the preconditioner:
      Mat Object: 2 MPI processes
        type: mpiaij
        rows=255, cols=255
        total: nonzeros=763, allocated nonzeros=1530
        total number of mallocs used during MatSetValues calls=0
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 2 -------------------------------
    KSP Object: (mg_levels_2_) 2 MPI processes
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.141422, max 1.55565
        eigenvalues estimated via gmres: min 0.00388229, max 1.41422
        eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]
        KSP Object: (mg_levels_2_esteig_) 2 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 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
      not checking for convergence
    PC Object: (mg_levels_2_) 2 MPI processes
      type: sor
        type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.
      linear system matrix, which is also used to construct the preconditioner:
      Mat Object: 2 MPI processes
        type: mpiaij
        rows=509, cols=509
        total: nonzeros=1525, allocated nonzeros=3054
        total number of mallocs used during MatSetValues calls=0
  Up solver (post-smoother) same as down solver (pre-smoother)
  linear system matrix, which is also used to construct the preconditioner:
  Mat Object: 2 MPI processes
    type: mpiaij
    rows=509, cols=509
    total: nonzeros=1525, allocated nonzeros=3054
    total number of mallocs used during MatSetValues calls=0
Residual norm 6.6474e-08
