1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
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0 KSP Residual norm 0.000510152
1 KSP Residual norm 0.00357106
2 KSP Residual norm 0.00349891
3 KSP Residual norm 0.00342676
4 KSP Residual norm 0.00335461
5 KSP Residual norm 0.00328246
6 KSP Residual norm 0.00321031
7 KSP Residual norm 0.00313816
8 KSP Residual norm 0.00306601
9 KSP Residual norm 0.00299386
10 KSP Residual norm 0.00292171
11 KSP Residual norm 0.00284955
12 KSP Residual norm 0.0027774
13 KSP Residual norm 0.00270525
14 KSP Residual norm 0.0026331
15 KSP Residual norm 0.00256094
16 KSP Residual norm 0.00248879
17 KSP Residual norm 0.00241663
18 KSP Residual norm 0.00234448
19 KSP Residual norm 0.00227233
20 KSP Residual norm 0.00220017
21 KSP Residual norm 0.00212801
22 KSP Residual norm 0.00205586
23 KSP Residual norm 0.0019837
24 KSP Residual norm 0.00191154
25 KSP Residual norm 0.00183938
26 KSP Residual norm 0.00176722
27 KSP Residual norm 0.00169506
28 KSP Residual norm 0.00162289
29 KSP Residual norm 0.00155073
30 KSP Residual norm 0.00147856
31 KSP Residual norm 0.00140639
32 KSP Residual norm 0.00133422
33 KSP Residual norm 0.00126205
34 KSP Residual norm 0.00118987
35 KSP Residual norm 0.00111769
36 KSP Residual norm 0.0010455
37 KSP Residual norm 0.000973308
38 KSP Residual norm 0.000901108
39 KSP Residual norm 0.000828899
40 KSP Residual norm 0.000756678
41 KSP Residual norm 0.000684441
42 KSP Residual norm 0.000612182
43 KSP Residual norm 0.000539894
44 KSP Residual norm 0.000467562
45 KSP Residual norm 0.000395162
46 KSP Residual norm 0.000322648
47 KSP Residual norm 0.000249922
48 KSP Residual norm 0.000176722
49 KSP Residual norm 0.00010203
50 KSP Residual norm < 1.e-11
KSP Object: 1 MPI processes
type: python
Python: example1.py:ConjGrad
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 processes
type: python
Python: example1.py:Jacobi
linear system matrix = precond matrix:
Mat Object: 1 MPI processes
type: python
rows=100, cols=100
Python: example1.py:Laplace1D
error norm = 1.68734e-12
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