Sparsifying preconditioner for pseudospectral approximations of indefinite systems on periodic structures

TL;DR

This paper presents a novel sparsifying preconditioner for pseudospectral methods, transforming dense indefinite systems on periodic structures into sparse systems to enable efficient iterative solutions, demonstrated on Helmholtz and Schrödinger equations.

Contribution

The paper introduces a new sparsifying preconditioner that converts dense pseudospectral discretizations into sparse systems, improving computational efficiency for indefinite problems.

Findings

Reduces iteration counts in iterative solvers

Effective for Helmholtz and Schrödinger equations in 2D and 3D

Provides accurate preconditioning for highly indefinite systems

Abstract

This paper introduces the sparsifying preconditioner for the pseudospectral approximation of highly indefinite systems on periodic structures, which include the frequency-domain response problems of the Helmholtz equation and the Schr\"odinger equation as examples. This approach transforms the dense system of the pseudospectral discretization approximately into an sparse system via an equivalent integral reformulation and a specially-designed sparsifying operator. The resulting sparse system is then solved efficiently with sparse linear algebra algorithms and serves as a reasonably accurate preconditioner. When combined with standard iterative methods, this new preconditioner results in small iteration counts. Numerical results are provided for the Helmholtz equation and the Schr\"odinger in both 2D and 3D to demonstrate the effectiveness of this new preconditioner.