    Residual norms for  solve.
    0 KSP Residual norm 1.476279106402e+02
    1 KSP Residual norm 3.025158182641e-14
  Linear  solve converged due to CONVERGED_RTOL iterations 1
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=1e-50, divergence=10000.
  left preconditioning
  using PRECONDITIONED norm type for convergence test
PC Object: () 1 MPI process
  type: ilu
    out-of-place factorization
    0 levels of fill
    tolerance for zero pivot 2.22045e-14
    matrix ordering: natural
    factor fill ratio given 1., needed 1.
      Factored matrix follows:
        Mat Object: () 1 MPI process
          type: seqaij
          rows=20, cols=20
          package used to perform factorization: petsc
          total: nonzeros=56, allocated nonzeros=56
            not using I-node routines
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=20, cols=20
    total: nonzeros=56, allocated nonzeros=0
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
KSP Object: (mpi_linear_solver_server_) 1 MPI process
  type: preonly
  maximum iterations=10000, initial guess is zero
  tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
  left preconditioning
  using NONE norm type for convergence test
PC Object: (mpi_linear_solver_server_) 1 MPI process
  type: mpi
  Running MPI linear solver server directly on rank 0 due to its small size
  Desired minimum number of nonzeros per rank for MPI parallel solve 10000
  *** Use -mpi_linear_solver_server_view to statistics on all the solves ***
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=20, cols=20
    total: nonzeros=56, allocated nonzeros=0
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
    Residual norms for  solve.
    0 KSP Residual norm 2.627679687191e+00
    1 KSP Residual norm 1.630826141812e-16
  Linear  solve converged due to CONVERGED_RTOL iterations 1
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=1e-50, divergence=10000.
  left preconditioning
  using PRECONDITIONED norm type for convergence test
PC Object: () 1 MPI process
  type: ilu
    out-of-place factorization
    0 levels of fill
    tolerance for zero pivot 2.22045e-14
    matrix ordering: natural
    factor fill ratio given 1., needed 1.
      Factored matrix follows:
        Mat Object: () 1 MPI process
          type: seqaij
          rows=20, cols=20
          package used to perform factorization: petsc
          total: nonzeros=56, allocated nonzeros=56
            not using I-node routines
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=20, cols=20
    total: nonzeros=56, allocated nonzeros=0
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
KSP Object: (mpi_linear_solver_server_) 1 MPI process
  type: preonly
  maximum iterations=10000, initial guess is zero
  tolerances: relative=1e-05, absolute=1e-50, divergence=10000.
  left preconditioning
  using NONE norm type for convergence test
PC Object: (mpi_linear_solver_server_) 1 MPI process
  type: mpi
  Running MPI linear solver server directly on rank 0 due to its small size
  Desired minimum number of nonzeros per rank for MPI parallel solve 10000
  *** Use -mpi_linear_solver_server_view to statistics on all the solves ***
  linear system matrix = precond matrix:
  Mat Object: 1 MPI process
    type: seqaij
    rows=20, cols=20
    total: nonzeros=56, allocated nonzeros=0
    total number of mallocs used during MatSetValues calls=0
      not using I-node routines
MPI linear solver server statistics:
    Ranks        KSPSolve()s     Mats        KSPs       Avg. Size      Avg. Its
  Sequential         2                         1            20           1
