An improved dqds algorithm

TL;DR

This paper introduces an improved dqds algorithm with novel deflation and shift strategies, achieving faster convergence and high accuracy in computing singular values of bidiagonal matrices, outperforming existing methods.

Contribution

The paper presents a new dqds algorithm with enhanced deflation and shift techniques, ensuring linear worst-case complexity and significantly faster performance.

Findings

V5 is 1.2x--4x faster than DLASQ without accuracy loss

V5 is 3x--10x faster on slow-converging matrices

HDLASQ outperforms previous versions in speed

Abstract

In this paper we present an improved dqds algorithm for computing all the singular values of a bidiagonal matrix to high relative accuracy. There are two key contributions: a novel deflation strategy that improves the convergence for badly scaled matrices, and some modifications to certain shift strategies that accelerate the convergence for most bidiagonal matrices. These techniques together ensure linear worst case complexity of the improved algorithm (denoted by V5). Our extensive numerical experiments indicate that V5 is typically 1.2x--4x faster than DLASQ (the LAPACK-3.4.0 implementation of dqds) without any degradation in accuracy. On matrices for which DLASQ shows very slow convergence, V5 can be 3x--10x faster. At the end of this paper, a hybrid algorithm (HDLASQ) is developed by combining our improvements with the aggressive early deflation strategy (AggDef2 in [SIAM J. Matrix Anal. Appl., 33(2012), 22-51]). Numerical results show that HDLASQ is the fastest among these different versions.