Convergence in Backward Error of Relaxed GMRES

This work is the follow-up of the experimental study presented in [3]. It is based on and extends some theoretical results in [15, 18]. In a backward error framework we study the convergence of GMRES when the matrixvector products are performed inaccurately. This inaccuracy is modeled by a perturbation of the original matrix. We prove the convergence of GMRES when the perturbation size is proportional to the inverse of the computed residual norm; this implies that the accuracy can be relaxed as the method proceeds which gives rise to the terminology relaxed GMRES. As for the exact GMRES we show under proper assumptions that only happy breakdowns can occur. Furthermore the convergence can be detected using a by-product of the algorithm. We explore the links between relaxed right-preconditioned GMRES and flexible GMRES. In particular this enables us to derive a proof of convergence of FGMRES. Finally we report results on numerical experiments to illustrate the behaviour of the relaxed GMRES monitored by the proposed relaxation strategies.