High Order Numerical Methods To Approximate The Singular Value Decomposition

TL;DR

This paper introduces high order numerical methods for approximating the singular value decomposition of complex matrices, extending beyond the previously known order three methods, with proven convergence and practical numerical validation.

Contribution

The paper develops a class of high order methods for SVD approximation, providing convergence analysis, cluster detection, and deflation techniques, which are novel contributions in this area.

Findings

Methods converge with order p+1 for initial approximations satisfying specific conditions.

Numerical experiments confirm the theoretical convergence rates.

The approach effectively detects singular value clusters and facilitates SVD deflation.

Abstract

In this paper, we present a class of high order methods to approximate the singular value decomposition of a given complex matrix (SVD). To the best of our knowledge, only methods up to order three appear in the the literature. A first part is dedicated to defline and analyse this class of method in the regular case, i.e., when the singular values are pairwise distinct. The construction is based on a perturbation analysis of a suitable system of associated to the SVD (SVD system). More precisely, for an integer $p$ be given, we define a sequence which converges with an order $p + 1$ towards the left-right singular vectors and the singular values if the initial approximation of the SVD system satisfies a condition which depends on three quantities : the norm of initial approximation of the SVD system, the greatest singular value and the greatest inverse of the modulus of the difference between the singular values. From a numerical computational point of view, this furnishes a very efficient simple test to prove and certifiy the existence of a SVD in neighborhood of the initial approximation. We generalize these result in the case of clusters of singular values. We show also how to use the result of regular case to detect the clusters of singular values and to define a notion of deflation of the SVD. Moreover numerical experiments confirm the theoretical results.