Timo:
I don't have "general rules", but I do have questions:
I have been using the dealii Trilinos API of ML to generate an AMG
preconditioner for MPI distributed sparse system matrix quite a while. It
roubustely works for most computing of a damped Newton method on clusters.
However, casually the linear solver of Newton update vector gives extremely
incorrect update vector, which leads to divergence of iteration. Here is an
example shown as log file record for a line search:
```shell
Refinement cycle : 0, iteration_loop: 1
Volume is 1.568e+06
assembly is done !
start to build AMG preconditioner.
AMG preconditioner is built up.
system_rhs.l2_norm() is 307.629
Starting linear solving.
Solved in 8 iterations.
AMG preconditioned solving is done ! With solver_tol 0.7
step length alpha is: 1, residual is: 1.08098e+57, previous_residual is:
307.629
Yes, this clearly isn't good. What does solver_to=0.7 correspond to? A
reduction of the residual by a factor of 0.7? That's of course far from the
1e-6 or so that we always use in the tutorials.
Regardless, it is clear that you made the residual *much* worse, and that...
...
step length alpha is: 9.74386e-09, residual is: 1.00003e+33,
previous_residual is: 307.629
```
Reducing the step length does not help either.
As the last two lines show, the iteration Vn+1 = Vn + alpha Xn went into total
failure because Xn, which is solved by FGMRES + Trilinos AMG, is no where
What is the underlying problem you are trying to solve? AMG is best (and
perhaps only really usable) if applied to elliptic systems. But then you'd be
using CG, not FGMRES. Can you explain what you use the AMG for?
Best
W.
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