Hi, I am solving a saddle point system using a shell preconditioner (which itself uses Krylov solvers, hence the use of FGMRES). I had added the option to re-scale parts of the saddle point system to minimize loss of floating point precision for cases where there are varying orders of magnitude in the system/unknowns.
I wanted to show that re-scaling can alleviate large differences between the preconditioned and unpreconditioned residual norms. However, I notice that FGMRES only supports right preconditioning, meaning the preconditioned residual is never formed/used (I think). Is there any way to form the preconditioned norm for FGMRES, or does it just not make sense in the context of right-preconditioned iterative solvers? Is there any way to show that the re-scaling is improving the solver convergence (i.e. showing that it ensures that the true and relative residual are close to each other)? *Nishant Nangia* Northwestern University Ph.D. Candidate | Engineering Sciences and Applied Mathematics Tech L386
