Hi,
I finite difference Jacobian approximation for my TS model. The size
of the vector is 2500. I got the following info with(-ts_view):
type: beuler
maximum steps=50
maximum time=50
total number of nonlinear solver iterations=647
total number of linear solver iterations=647
SNES Object:
type: ls
line search variant: SNESLineSearchCubic
alpha=0.0001, maxstep=1e+08, minlambda=1e-12
maximum iterations=50, maximum function evaluations=10000
tolerances: relative=1e-08, absolute=1e-50, solution=1e-08
total number of linear solver iterations=50
total number of function evaluations=51
KSP Object:
type: gmres
GMRES: restart=30, using Classical (unmodified) Gram-Schmidt
Orthogonalization with no iterative refinement
GMRES: 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:
type: ilu
ILU: out-of-place factorization
0 levels of fill
tolerance for zero pivot 1e-12
using diagonal shift to prevent zero pivot
matrix ordering: natural
factor fill ratio given 1, needed 1
Factored matrix follows:
Matrix Object:
type=seqaij, rows=1830, cols=1830
package used to perform factorization: petsc
total: nonzeros=1830, allocated nonzeros=1830
total number of mallocs used during MatSetValues calls =0
not using I-node routines
linear system matrix = precond matrix:
Matrix Object:
type=seqaij, rows=1830, cols=1830
total: nonzeros=1830, allocated nonzeros=29280
total number of mallocs used during MatSetValues calls =1830
not using I-node routines
50 output time step takes me 11.877s. So I guess there is something
not appropriate with my Jacobian Matrix. Could you please tell me how
to speed up my code?
Thanks!
Xuan YU
xxy113 at psu.edu
