Dear Bruno

>
> > 2) The largest eigenvalue is computed instead of the largest one, and, I 
> guess, the smallest eigenvalue is computed instead of the largest one. 
> If you use ARPACK through deal without patching it, then the only 
> method available is shift-inverse. The thing is that when you know the 
> largest eigenvalue of the inverse you can get the smallest eigenvalue 
> of the operator. So yes, the names are reversed. 
>
Yup,  using the patch solved this issue.  At least the smallest eigenvalue 
is smaller than the largest.

>
> > 3) I get complex eigenvalues in some cases and I am quite sure, that all 
> eigenvalues of the system are real. 
> I've not had this problem. Maybe it's because of numerical error? 

Eigenvalue:  (1.03342,0.0069945) with arpack tolerance 1e-8. Is it possible?

>
> > I noticed that in some cases the "smallest" eigenvalue does not match 
> maximum eigenvalue obtained from power method. 
> Are you sure that the power method has converged? Also, try using 
> ARPACK's regular mode instead of shift-inverse. I think it might be 
> better to find the largest eigenvalue. 
>
 The deal.II power method converged with residual: 9.5052e-05 I switched to 
regular mode and obtained 1.04192 from arpack and 1.03981 from power 
method. The tolerance of arpack was set to 1e-8. The difference is growing 
with mesh size,  so I assume the tolerance is set for eigenvector, 
resulting in much smaller tolerance for the eigenvalue.

Also, I got the smallest eigenvalues equal to  0 in some cases, while I'm 
sure that the matrix is not singular. I can solve a linear system with the 
same matrix using  Krylov solvers without a problem.

Michał

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