A modified Anderson acceleration with sharp linear convergence rate predictions and application to incompressible flows
For researchers solving incompressible flow problems, this method offers a theoretically grounded acceleration technique with predictable convergence, though it is an incremental extension of existing work.
The paper extends a modified Anderson acceleration (AAg) to Picard iteration for Navier-Stokes equations, providing sharp linear convergence rate predictions and an adaptive depth selection strategy. Numerical experiments show AAg outperforms standard Anderson acceleration and nonlinear GMRES.
In this work, we extend a modified Anderson acceleration proposed in [Y. He, arXiv:2603.25983, 2026] to accelerate the Picard iteration for the Navier-Stokes equations. In this variant of Anderson acceleration, named AAg, the nonlinear residual--rather than the standard fixed-point iteration residual--is used to define the associated least-squares problem. We establish a convergence analysis for this method with any depth that shows how AAg accelerates convergence through the gain of the optimization problem, and obtain a sharp prediction of its linear convergence rate (a feature that is not part of the known theory for classical Anderson acceleration). Additionally, motivated by this sharp convergence prediction, we introduce an adaptive strategy that automatically selects the depth parameter. Results of several numerical experiments are given that illustrate the new theory and also demonstrate the effectiveness of the proposed adaptive approach. Comparisons of AAg to usual AA and nonlinear GMRES are also provided.