Approximation of the Scattering Amplitude using Nonsymmetric Saddle Point Matrices

TL;DR

This paper introduces a conjugate gradient-like iterative method for approximating scattering amplitudes via nonsymmetric saddle point matrices, showing improved convergence over existing methods through numerical experiments.

Contribution

It proposes a novel iterative approach based on nonsymmetric saddle point matrices with positive spectra, enhancing convergence in scattering amplitude computations.

Findings

Method outperforms GLSQR and QMR in numerical experiments.

Combining with preconditioning accelerates convergence.

Demonstrates robustness and efficiency in solving forward and adjoint systems.

Abstract

In this paper we examine iterative methods for solving the forward ($A{\bf x}={\bf b}$) and adjoint ($A^{T}{\bf y}={\bf g}$) systems of linear equations used to approximate the scattering amplitude, defined by ${\bf g}^{T}{\bf x}={\bf y}^{T}{\bf b}$. Based on an idea first proposed by Gene Golub, we use a conjugate gradient-like iteration for a nonsymmetric saddle point matrix that is constructed so as to have a real positive spectrum. Numerical experiments show that this method is more consistent than known methods for computing the scattering amplitude such as GLSQR or QMR. We then demonstrate that when combined with known preconditioning techniques, the proposed method exhibits more rapid convergence than state-of-the-art iterative methods for nonsymmetric systems.