Efficient Solution of Generalized Sylvester Equations via Preconditioned Alternating Anderson Acceleration

TL;DR

This paper introduces a preconditioned alternating Anderson acceleration method for efficiently solving generalized Sylvester equations, significantly improving convergence and reducing computational costs in scientific and engineering applications.

Contribution

The paper presents a novel preconditioning strategy combined with Anderson acceleration, enhancing the efficiency of solving generalized Sylvester equations with large spectral radius operators.

Findings

The proposed method accelerates convergence compared to existing techniques.

It reduces computational cost and iteration count in large-scale problems.

Numerical experiments confirm superior performance over state-of-the-art methods.

Abstract

This paper considers the numerical solution of generalized Sylvester matrix equations, which arise in many scientific and engineering applications but remain challenging to solve efficiently, particularly when the coefficient matrices are general and the spectral radius of the associated operator is large but not greater than $1$. We propose a new iterative method, termed preconditioned-alternating Anderson acceleration (P-aAA), which combines a matrix-oriented variant of Anderson acceleration (AA) with a novel preconditioning strategy. The method alternates between preconditioned fixed-point iterations and Anderson acceleration updates, thereby reducing both computational cost and iteration count. A key contribution is the development of an efficient preconditioning operator based on a first-order Neumann series approximation, which avoids expensive operator inversions while enhancing convergence. We theoretically prove that the proposed preconditioning operator accelerates the convergence rate without increasing the overall computational complexity. Extensive numerical experiments further demonstrate that the proposed approach consistently outperforms existing state-of-the-art methods for both medium- and large-scale problems, achieving substantial reductions in computation time and iteration number.