  0 KSP Residual norm 0.0443241
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 2.33385
      1 KSP Residual norm 1.18269
      2 KSP Residual norm 0.367737
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.372534
      1 KSP Residual norm 0.190318
      2 KSP Residual norm 0.0612006
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 1.65824
    1 KSP Residual norm 0.864691
    2 KSP Residual norm 0.214791
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0276895
      1 KSP Residual norm 0.0154796
      2 KSP Residual norm 0.00702236
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.00783567
      1 KSP Residual norm 0.00476578
      2 KSP Residual norm 0.00137409
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.217797
    1 KSP Residual norm 0.116558
    2 KSP Residual norm 0.0312166
  1 KSP Residual norm 0.00138323
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.26684
      1 KSP Residual norm 0.173881
      2 KSP Residual norm 0.061286
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0629698
      1 KSP Residual norm 0.0351539
      2 KSP Residual norm 0.0119135
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.982198
    1 KSP Residual norm 0.561428
    2 KSP Residual norm 0.152152
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0507925
      1 KSP Residual norm 0.0331169
      2 KSP Residual norm 0.00961403
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0100591
      1 KSP Residual norm 0.00669029
      2 KSP Residual norm 0.00249984
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.16365
    1 KSP Residual norm 0.100099
    2 KSP Residual norm 0.0346352
  2 KSP Residual norm 3.36276e-05
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.574261
      1 KSP Residual norm 0.370644
      2 KSP Residual norm 0.103663
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.095791
      1 KSP Residual norm 0.0584995
      2 KSP Residual norm 0.0234572
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 1.13144
    1 KSP Residual norm 0.657896
    2 KSP Residual norm 0.198591
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.132276
      1 KSP Residual norm 0.0940755
      2 KSP Residual norm 0.0309249
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0234275
      1 KSP Residual norm 0.0102593
      2 KSP Residual norm 0.00680986
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.238703
    1 KSP Residual norm 0.158193
    2 KSP Residual norm 0.0604068
  3 KSP Residual norm 1.97101e-06
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.495273
      1 KSP Residual norm 0.234702
      2 KSP Residual norm 0.146941
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.19482
      1 KSP Residual norm 0.115058
      2 KSP Residual norm 0.0323438
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.852783
    1 KSP Residual norm 0.42757
    2 KSP Residual norm 0.242289
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.100108
      1 KSP Residual norm 0.0444515
      2 KSP Residual norm 0.037141
      Residual norms for mg_levels_1_ solve.
      0 KSP Residual norm 0.0466498
      1 KSP Residual norm 0.0264831
      2 KSP Residual norm 0.00780339
    Residual norms for mg_levels_2_ solve.
    0 KSP Residual norm 0.28356
    1 KSP Residual norm 0.155117
    2 KSP Residual norm 0.0560442
  4 KSP Residual norm 1.01338e-07
KSP Object: 1 MPI process
  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: 1 MPI process
  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_) 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_) 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_) 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=384
        total number of mallocs used during MatSetValues calls=0
          not using I-node routines
  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 0.099124, max 1.09036
        eigenvalues estimated via gmres: min 0.0171844, max 0.99124
        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
      not checking for convergence
    PC Object: (mg_levels_1_) 1 MPI process
      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: 1 MPI process
        type: seqaij
        rows=255, cols=255
        total: nonzeros=763, allocated nonzeros=765
        total number of mallocs used during MatSetValues calls=0
          not using I-node routines
  Up solver (post-smoother) same as down solver (pre-smoother)
  Down solver (pre-smoother) on level 2 -------------------------------
    KSP Object: (mg_levels_2_) 1 MPI process
      type: chebyshev
        Chebyshev polynomial of first kind
        eigenvalue targets used: min 0.0977779, max 1.07556
        eigenvalues estimated via gmres: min 0.00339269, max 0.977779
        eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]
        KSP Object: (mg_levels_2_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
      not checking for convergence
    PC Object: (mg_levels_2_) 1 MPI process
      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: 1 MPI process
        type: seqaij
        rows=509, cols=509
        total: nonzeros=1525, allocated nonzeros=1527
        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, which is also used to construct the preconditioner:
  Mat Object: 1 MPI process
    type: seqaij
    rows=509, cols=509
    total: nonzeros=1525, allocated nonzeros=1527
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
Residual norm 5.83374e-08
