Hello -

I have a simple question I was hoping to get advice on related to setting 
the linear solver tolerance for an iterative solver. In the tutorial 
examples (and in most of the codes I have seen and written) the solver 
tolerance is some fraction of the initial right hand side 2-norm (e.g. tol 
= 1.0e-8 * rhs.l2_norm() ). However, I'm questioning this strategy for a 
nonlinear problem using Newton's method to solve the nonlinear system. As 
the iterations progress, the initial norm of the right hand side (i.e. the 
norm of the residual) is decreasing substantially. For a number of problems 
that I am trying to solve, this causes my tolerance to drop below machine 
precision at some point (if this happens I take 1e-15 to be the tolerance).

I would like to have a tolerance setting strategy that allows me to have a 
reasonable and consistent number of iterations for each linear solve 
required in a Newton increment. Due to this issue the number of linear 
solver iterations in a single Newton increment can vary drastically (~10 to 
~600) even though the properties of the matrix (like the condition number 
which I have checked) are not changing very much (I am using the AMG 
preconditioner in Trilinos and also use the one in PETSc).

Does anyone have a better strategy for setting the linear solver tolerance 
other than trial and error (or simply a different strategy that I could 
try)? From what I remember, the iterative solvers in deal.II are checking 
the 2-norm of the residual against the tolerance, but correct me if I am 
wrong please.

Thank you in advance for any comments or advice,
Jonathan

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