        0 SNES Function norm 0.995735
        Linear solve converged due to CONVERGED_RTOL iterations 3
        1 SNES Function norm 0.276938
        Linear solve converged due to CONVERGED_RTOL iterations 3
        2 SNES Function norm 0.00668684
        Linear solve converged due to CONVERGED_RTOL iterations 3
        3 SNES Function norm 4.6129e-06
      0 SNES Function norm 0.0763977
      Linear solve converged due to CONVERGED_RTOL iterations 2
      1 SNES Function norm 2.68308e-05
      Linear solve converged due to CONVERGED_RTOL iterations 2
      2 SNES Function norm 1.90247e-08
    0 SNES Function norm 0.0408394
    Linear solve converged due to CONVERGED_RTOL iterations 2
    1 SNES Function norm 9.09197e-07
    Linear solve converged due to CONVERGED_RTOL iterations 2
    2 SNES Function norm 3.74739e-08
  0 SNES Function norm 0.021012
  Linear solve converged due to CONVERGED_RTOL iterations 2
  1 SNES Function norm 8.22296e-08
SNES Object: 1 MPI process
  type: newtonls
  maximum iterations=50, maximum function evaluations=10000
  tolerances: relative=1e-05, absolute=1e-25, solution=1e-05
  total number of linear solver iterations=19
  total number of function evaluations=12
  norm schedule ALWAYS
  total number of grid sequence refinements=3
  Jacobian is built using a DMDA local Jacobian
  SNESLineSearch Object: 1 MPI process
    type: bt
      interpolation: cubic
      alpha=1.000000e-04
    maxstep=1.000000e+08, minlambda=1.000000e-12
    tolerances: relative=1.000000e-08, absolute=1.000000e-15, lambda=1.000000e-08
    maximum iterations=40
  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=-1., divergence=10000.
    left preconditioning
    using PRECONDITIONED norm type for convergence test
  PC Object: 1 MPI process
    type: mg
      type is FULL, levels=4 cycles=v
        Using Galerkin computed coarse grid matrices for pmat
    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-25, divergence=10000.
        left preconditioning
        using NONE norm type for convergence test
      PC Object: (mg_coarse_) 1 MPI process
        type: lu
          out-of-place factorization
          tolerance for zero pivot 1.19209e-05
          using diagonal shift on blocks to prevent zero pivot [INBLOCKS]
          matrix ordering: nd
          factor fill ratio given 5., needed 1.42
            Factored matrix follows:
              Mat Object: (mg_coarse_) 1 MPI process
                type: seqaij
                rows=16, cols=16
                package used to perform factorization: petsc
                total: nonzeros=142, allocated nonzeros=142
                  using I-node routines: found 11 nodes, limit used is 5
        linear system matrix = precond matrix:
        Mat Object: 1 MPI process
          type: seqaij
          rows=16, cols=16
          total: nonzeros=100, allocated nonzeros=100
          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 0.569941, max 1.25387
          eigenvalues estimated via gmres: min 0.381692, max 1.13988
          eigenvalues estimated using gmres with transform: [0. 0.5; 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-25, 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-25, divergence=10000.
        left preconditioning
        using UNPRECONDITIONED norm type for convergence test
      PC Object: (mg_levels_1_) 1 MPI process
        type: sor
          type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.
        linear system matrix = precond matrix:
        Mat Object: 1 MPI process
          type: seqaij
          rows=49, cols=49
          total: nonzeros=361, allocated nonzeros=361
          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 0.498207, max 1.09606
          eigenvalues estimated via gmres: min 0.113268, max 0.996415
          eigenvalues estimated using gmres with transform: [0. 0.5; 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-25, 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-25, divergence=10000.
        left preconditioning
        using UNPRECONDITIONED norm type for convergence test
      PC Object: (mg_levels_2_) 1 MPI process
        type: sor
          type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.
        linear system matrix = precond matrix:
        Mat Object: 1 MPI process
          type: seqaij
          rows=169, cols=169
          total: nonzeros=1369, allocated nonzeros=1369
          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 3 -------------------------------
      KSP Object: (mg_levels_3_) 1 MPI process
        type: chebyshev
          Chebyshev polynomial of first kind
          eigenvalue targets used: min 0.498868, max 1.09751
          eigenvalues estimated via gmres: min 0.0267908, max 0.997736
          eigenvalues estimated using gmres with transform: [0. 0.5; 0. 1.1]
          KSP Object: (mg_levels_3_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-25, 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-25, divergence=10000.
        left preconditioning
        using UNPRECONDITIONED norm type for convergence test
      PC Object: (mg_levels_3_) 1 MPI process
        type: sor
          type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.
        linear system matrix = precond matrix:
        Mat Object: 1 MPI process
          type: seqaij
          rows=625, cols=625
          total: nonzeros=3025, allocated nonzeros=3025
          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=625, cols=625
      total: nonzeros=3025, allocated nonzeros=3025
      total number of mallocs used during MatSetValues calls=0
        not using I-node routines
