0 SNES Function norm 3393.27 0 SNES Function norm 3393.27 1 SNES Function norm 937.658 0 SNES Function norm 858.91 1 SNES Function norm 343.648 0 SNES Function norm 280.975 1 SNES Function norm 193.8 2 SNES Function norm 61483.4 3 SNES Function norm 18172.7 4 SNES Function norm 5338.44 5 SNES Function norm 1535.53 6 SNES Function norm 411.217 7 SNES Function norm 86.8928 8 SNES Function norm 8.80363 9 SNES Function norm 0.129676 10 SNES Function norm 2.95747e-05 11 SNES Function norm < 1.e-11 0 SNES Function norm 270.325 1 SNES Function norm 160.311 0 SNES Function norm 761.459 1 SNES Function norm 226.117 1 SNES Function norm 226.117 0 SNES Function norm 226.117 1 SNES Function norm 67.3939 0 SNES Function norm 46.5799 1 SNES Function norm 19.1174 0 SNES Function norm 14.928 1 SNES Function norm 32.9355 2 SNES Function norm 7.81806 3 SNES Function norm 1.11612 4 SNES Function norm 0.0418029 5 SNES Function norm 0.00019802 6 SNES Function norm 2.54966e-08 0 SNES Function norm 29.0551 1 SNES Function norm 17.1993 0 SNES Function norm 32.2278 1 SNES Function norm 8.04014 2 SNES Function norm 8.04014 0 SNES Function norm 8.04014 1 SNES Function norm 2.06394 0 SNES Function norm 1.45779 1 SNES Function norm 0.168478 0 SNES Function norm 0.165074 1 SNES Function norm 0.00222455 2 SNES Function norm 4.88426e-07 3 SNES Function norm < 1.e-11 0 SNES Function norm 0.0994334 1 SNES Function norm 0.00170881 0 SNES Function norm 1.30474 1 SNES Function norm 0.113808 3 SNES Function norm 0.113808 0 SNES Function norm 0.113808 1 SNES Function norm 0.00241366 0 SNES Function norm 0.00139771 1 SNES Function norm 2.4063e-07 0 SNES Function norm 1.98295e-07 1 SNES Function norm < 1.e-11 0 SNES Function norm 1.73039e-07 1 SNES Function norm < 1.e-11 0 SNES Function norm 0.00208614 1 SNES Function norm 3.3809e-06 4 SNES Function norm 3.3809e-06 L_2 Error: 0.00363695 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 4 SNES Object: 1 MPI process type: fas type is MULTIPLICATIVE, levels=3, cycles=1 Not using Galerkin computed coarse grid function evaluation Coarse grid solver -- level 0 ------------------------------- SNES Object: (fas_coarse_) 1 MPI process type: newtonls maximum iterations=50, maximum function evaluations=10000 tolerances: relative=1e-08, absolute=1e-50, solution=1e-08 total number of linear solver iterations=1 total number of function evaluations=1 norm schedule ALWAYS SNESLineSearch Object: (fas_coarse_) 1 MPI process type: basic 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: (fas_coarse_) 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-10, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test PC Object: (fas_coarse_) 1 MPI process type: svd All singular values smaller than 1e-12 treated as zero Provided essential rank of the matrix 0 (all other eigenvalues are zeroed) linear system matrix, which is also used to construct the preconditioner: Mat Object: 1 MPI process type: seqaij rows=5, cols=5 total: nonzeros=13, allocated nonzeros=13 total number of mallocs used during MatSetValues calls=0 not using I-node routines Down solver (pre-smoother) on level 1 ------------------------------- SNES Object: (fas_levels_1_) 1 MPI process type: newtonls maximum iterations=1, maximum function evaluations=10000 tolerances: relative=0., absolute=0., solution=0. total number of linear solver iterations=1 total number of function evaluations=2 norm schedule FINALONLY SNESLineSearch Object: (fas_levels_1_) 1 MPI process 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: (fas_levels_1_) 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-10, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test PC Object: (fas_levels_1_) 1 MPI process type: svd All singular values smaller than 1e-12 treated as zero Provided essential rank of the matrix 0 (all other eigenvalues are zeroed) linear system matrix, which is also used to construct the preconditioner: Mat Object: 1 MPI process type: seqaij rows=25, cols=25 total: nonzeros=137, allocated nonzeros=137 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 ------------------------------- SNES Object: (fas_levels_2_) 1 MPI process type: newtonls maximum iterations=1, maximum function evaluations=10000 tolerances: relative=0., absolute=1e-11, solution=0. total number of linear solver iterations=1 total number of function evaluations=2 norm schedule FINALONLY SNESLineSearch Object: (fas_levels_2_) 1 MPI process 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: (fas_levels_2_) 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-10, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test PC Object: (fas_levels_2_) 1 MPI process type: svd All singular values smaller than 1e-12 treated as zero Provided essential rank of the matrix 0 (all other eigenvalues are zeroed) linear system matrix, which is also used to construct the preconditioner: Mat Object: 1 MPI process type: seqaij rows=113, cols=113 total: nonzeros=721, allocated nonzeros=721 total number of mallocs used during MatSetValues calls=0 not using I-node routines Up solver (post-smoother) same as down solver (pre-smoother) maximum iterations=10000, maximum function evaluations=30000 tolerances: relative=1e-08, absolute=1e-50, solution=1e-08 total number of function evaluations=1 norm schedule ALWAYS