1 0 KSP Residual norm 7.280770540237e-01 2 1 KSP Residual norm 3.337812423706e-01 3 2 KSP Residual norm 8.313740044832e-02 4 3 KSP Residual norm 5.251832678914e-02 5 4 KSP Residual norm 1.543122743897e-05 6 5 KSP Residual norm 8.220995795227e-08 7 0 KSP Residual norm 9.231762960553e-03 8 1 KSP Residual norm 5.040043033659e-03 9 2 KSP Residual norm 1.892581814900e-03 10 3 KSP Residual norm 1.012579815551e-07 11 4 KSP Residual norm 1.632135870899e-09 12 0 KSP Residual norm 1.440683990950e-05 13 1 KSP Residual norm 1.018413422571e-05 14 2 KSP Residual norm 5.139805580257e-06 15 3 KSP Residual norm 4.683830070462e-07 16 4 KSP Residual norm 1.860650522545e-12 17SNES Object: 1 MPI process 18 type: newtonls 19 maximum iterations=50, maximum function evaluations=10000 20 tolerances: relative=1e-05, absolute=1e-25, solution=1e-05 21 total number of linear solver iterations=13 22 total number of function evaluations=26 23 norm schedule ALWAYS 24 SNESLineSearch Object: 1 MPI process 25 type: bt 26 interpolation: cubic 27 alpha=1.000000e-04 28 maxlambda=1.000000e+00, minlambda=1.000000e-12 29 tolerances: relative=1.000000e-08, absolute=1.000000e-15, lambda=1.000000e-08 30 maximum iterations=40 31 KSP Object: 1 MPI process 32 type: gmres 33 restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement 34 happy breakdown tolerance=1e-30 35 maximum iterations=10000, initial guess is zero 36 tolerances: relative=1e-05, absolute=1e-25, divergence=10000. 37 left preconditioning 38 using PRECONDITIONED norm type for convergence test 39 PC Object: 1 MPI process 40 type: jacobi 41 type DIAGONAL 42 linear system matrix, which is also used to construct the preconditioner: 43 Mat Object: 1 MPI process 44 type: mffd 45 rows=5, cols=5 46 Matrix-free approximation: 47 err=0.000345267 (relative error in function evaluation) 48 Using wp compute h routine 49 Does not compute normU 50number of SNES iterations = 3 51 52