[petsc-users] mg pre-conditioner default setup from PETSc-3.4 to PETSc-3.7

[Mark Adams]

Two iterations for the eigen estimate is too low and gmres converges
slowly. I'm surprised this does not diverge, or just die, for a Laplacian
because you need to get an upper bound. Cheby will scale the estimate up by
some safety factor (is it really large now?). Try: -mg_levels_esteig_ksp_max_it
10 (the old default). I usually use 5.

Also, I would suggest using cg (-mg_levels_esteig_ksp_type cg), it
converges much faster. If your problem is not very asymmetric, it is fine.

On Wed, Sep 13, 2017 at 11:35 AM, Hong <hzhang at mcs.anl.gov> wrote:

> Federico :
>
>>
>>   Coarse grid solver -- level -------------------------------
>>     KSP Object:    (mg_levels_0_)     128 MPI processes
>>       type: chebyshev
>>         Chebyshev: eigenvalue estimates:  min = 0.223549, max = 2.45903
>>         Chebyshev: eigenvalues estimated using gmres with translations
>>  [0. 0.1; 0. 1.1]
>>         KSP Object:        (mg_levels_0_esteig_)         128 MPI processes
>>           type: gmres
>>             GMRES: restart=30, using Classical (unmodified) Gram-Schmidt
>> Orthogonalization with no iterative refinement
>>             GMRES: happy breakdown tolerance 1e-30
>>           maximum iterations=10, initial guess is zero
>>           *tolerances:  relative=1e-12*, absolute=1e-50,
>> divergence=10000.
>>           left preconditioning
>>           *using PRECONDITIONED norm type for convergence test*
>>       maximum iterations=2, initial guess is zero
>>       tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.
>>       left preconditioning
>>       using NONE norm type for convergence test
>>
>
> Chebyshev requires an estimate of operator eigenvalues, for which we use
> few gmres iterations. These default options are used for eigenvalue
> estimates.
>
> Hong
>
>
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