Properties of Nonlinear GMRES Applied to the Preconditioned Richardson Iteration
This work provides incremental theoretical insights into iterative methods for solving linear systems, primarily benefiting researchers in numerical analysis and computational mathematics.
The authors tackled the problem of accelerating fixed-point iterations by proposing new variants of Anderson acceleration and nonlinear GMRES, showing equivalences to preconditioned GMRES and establishing monotonicity conditions, with numerical results validating the theoretical findings.
In this work, we propose new variants of Anderson acceleration and nonlinear GMRES for general fixed-point iterations, based on modified least-squares problems associated with the methods. To solve the underlying linear systems, we apply these new approaches to accelerate the preconditioned Richardson iteration. We establish connections between the proposed variants and both left- and right-preconditioned GMRES. In particular, we show that full NGMRES applied to the preconditioned Richardson iteration is equivalent to right-preconditioned GMRES, while full NGMRES equipped with the new least-squares formulation is equivalent to left-preconditioned GMRES. Furthermore, under certain conditions on the preconditioned coefficient matrix, an equivalence between windowed NGMRES with any depth and preconditioned GMRES. These theoretical results deepen our understanding of NGMRES for solving linear systems and clarify its relationship to classical preconditioned GMRES. Finally, we establish conditions for monotonicity of the various variants. Numerical results are presented to validate our theoretical findings.