      0 SNES Function norm 0.995735
      Linear solve converged due to CONVERGED_RTOL iterations 2
      1 SNES Function norm 0.276938
      Linear solve converged due to CONVERGED_RTOL iterations 2
      2 SNES Function norm 0.00668678
      Linear solve converged due to CONVERGED_RTOL iterations 2
      3 SNES Function norm 4.61503e-06
      Linear solve converged due to CONVERGED_RTOL iterations 2
      4 SNES Function norm < 1.e-11
    Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 4
    0 SNES Function norm 0.0763997
    Linear solve converged due to CONVERGED_RTOL iterations 6
    1 SNES Function norm 2.68503e-05
    Linear solve converged due to CONVERGED_RTOL iterations 5
    2 SNES Function norm 6.777e-10
  Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 2
  0 SNES Function norm 0.0408394
  Linear solve converged due to CONVERGED_RTOL iterations 6
  1 SNES Function norm 9.57612e-07
  Linear solve converged due to CONVERGED_RTOL iterations 4
  2 SNES Function norm 2.903e-10
Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 2
SNES Object: 2 MPI processes
  type: newtonls
  maximum iterations=50, maximum function evaluations=10000
  tolerances: relative=1e-08, absolute=-1., solution=1e-08
  total number of linear solver iterations=29
  total number of function evaluations=11
  norm schedule ALWAYS
  total number of grid sequence refinements=2
  Jacobian is built using a DMDA local Jacobian
  SNESLineSearch Object: 2 MPI processes
    type: bt
      interpolation: cubic
      alpha=1.000000e-04
    maxlambda=1.000000e+00, minlambda=1.000000e-12
    tolerances: relative=1.000000e-08, absolute=1.000000e-15, lambda=1.000000e-08
    maximum iterations=40
  KSP Object: 2 MPI processes
    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: 2 MPI processes
    type: mg
      type is MULTIPLICATIVE, levels=3 cycles=v
        Cycles per PCApply=1
        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 1.875
                Factored matrix:
                  Mat Object: (mg_coarse_redundant_) 1 MPI process
                    type: seqaij
                    rows=16, cols=16
                    package used to perform factorization: petsc
                    total: nonzeros=120, allocated nonzeros=120
                      using I-node routines: found 12 nodes, limit used is 5
            linear system matrix, which is also used to construct the preconditioner:
            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
        linear system matrix, which is also used to construct the preconditioner:
        Mat Object: 2 MPI processes
          type: mpiaij
          rows=16, cols=16
          total: nonzeros=64, allocated nonzeros=64
          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 0.12873, max 1.41603
          eigenvalues estimated via gmres: min 0.220123, max 1.2873
          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=49, cols=49
          total: nonzeros=217, allocated nonzeros=217
          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 0.12637, max 1.39007
          eigenvalues estimated via gmres: min 0.0725255, max 1.2637
          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=169, cols=169
          total: nonzeros=793, allocated nonzeros=793
          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=169, cols=169
      total: nonzeros=793, allocated nonzeros=793
      total number of mallocs used during MatSetValues calls=0
