SVD-MPE: An SVD-Based Vector Extrapolation Method of Polynomial Type

TL;DR

This paper introduces SVD-MPE, a new polynomial-type vector extrapolation method based on SVD, designed to accelerate the convergence of slowly converging vector sequences in scientific computing.

Contribution

The paper presents a novel SVD-based polynomial extrapolation method, SVD-MPE, with a stable algorithm and minimal computational cost, improving convergence acceleration techniques.

Findings

SVD-MPE effectively accelerates convergence of vector sequences.

The method is numerically stable and computationally efficient.

Numerical examples demonstrate its practical advantages.

Abstract

An important problem that arises in different areas of science and engineering is that of computing the limits of sequences of vectors $\{\xx_m\}$, where $\xx_m\in \C^N$, $N$ being very large. Such sequences arise, for example, in the solution of systems of linear or nonlinear equations by fixed-point iterative methods, and $\lim_{m\to\infty}\xx_m$ are simply the required solutions. In most cases of interest, however, these sequences converge to their limits extremely slowly. One practical way to make the sequences $\{\xx_m\}$ converge more quickly is to apply to them vector extrapolation methods. Two types of methods exist in the literature: polynomial type methods and epsilon algorithms. In most applications, the polynomial type methods have proved to be superior convergence accelerators. Three polynomial type methods are known, and these are the {minimal polynomial extrapolation} (MPE), the {reduced rank extrapolation} (RRE), and the {modified minimal polynomial extrapolation} (MMPE). In this work, we develop yet another polynomial type method, which is based on the singular value decomposition, as well as the ideas that lead to MPE. We denote this new method by SVD-MPE. We also design a numerically stable algorithm for its implementation, whose computational cost and storage requirements are minimal. Finally, we illustrate the use of {SVD-MPE} with numerical examples.