xref: /petsc/src/snes/tutorials/output/ex18_3.out (revision 70646cd191a02c3aba559ba717dac5da7a8a1e20)

SNES Object: 1 MPI process
  type: newtontr
    Trust region parameters:
      eta1=0.001, eta2=0.25, eta3=0.75
      t1=0.25, t2=2.
      delta_min=1e-12, delta_0=0.2, delta_max=1e+10
      kmdc=0.
      fallback=DOGLEG
  maximum iterations=50, maximum function evaluations=10000
  tolerances: relative=1e-08, absolute=1e-50, solution=0.
  total number of linear solver iterations=24
  total number of function evaluations=10
  norm schedule ALWAYS
  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 MULTIPLICATIVE, levels=3 cycles=v
        Cycles per PCApply=1
        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-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 1.59172
            Factored matrix:
              Mat Object: (mg_coarse_) 1 MPI process
                type: seqaij
                rows=25, cols=25
                package used to perform factorization: petsc
                total: nonzeros=269, allocated nonzeros=269
                  using I-node routines: found 17 nodes, limit used is 5
        linear system matrix, which is also used to construct the preconditioner:
        Mat Object: 1 MPI process
          type: seqaij
          rows=25, cols=25
          total: nonzeros=169, allocated nonzeros=169
          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 0.0996438, max 1.09608
          eigenvalues estimated via gmres: min 0.139653, max 0.996438
          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=81, cols=81
          total: nonzeros=625, allocated nonzeros=625
          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 0.0990486, max 1.08953
          eigenvalues estimated via gmres: min 0.0626846, max 0.990486
          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=289, cols=289
          total: nonzeros=1377, allocated nonzeros=1377
          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=289, cols=289
      total: nonzeros=1377, allocated nonzeros=1377
      total number of mallocs used during MatSetValues calls=0
        not using I-node routines
Number of SNES iterations = 9
Number of Linear iterations = 24
Average Linear its / SNES = 2.666667e+00