HDLASQ swMATH ID: 10261 Software Authors: Li, Shengguo; Gu, Ming; Parlett, Beresford N. Description: An improved DQDS algorithm. In this paper we present an improved differential quotient difference with stifts (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 1·2×-4× 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 3×-10× faster. We develop a hybrid algorithm (HDLASQ) by combining our improvements with the aggressive early deflation strategy (AggDef2 of Y. Nakatsukasa et al. [SIAM J. Matrix Anal. Appl. 33, No. 1, 22–51 (2012; Zbl 1248.65042)]). Numerical results show that HDLASQ is the fastest among these different versions. Homepage: http://arxiv.org/pdf/1209.5462v2.pdf Keywords: singular value; d-deflation; convergence acceleration; differential quotient difference with stifts algorithm; bidiagonal matrix; numerical experiment Related Software: LAPACK; Algorithm 880; MKL; AggDef2; DLASQ Cited in: 3 Publications all top 5 Cited by 8 Authors 2 Gu, Ming 2 Li, Shengguo 1 Andrisani, E. 1 Cheng, Lizhi 1 Chi, Xuebin 1 Di Lena, Giovanni 1 Parlett, Beresford Neill 1 Sun, Meng Cited in 3 Serials 1 Computing 1 SIAM Journal on Matrix Analysis and Applications 1 SIAM Journal on Scientific Computing Cited in 2 Fields 3 Numerical analysis (65-XX) 1 Linear and multilinear algebra; matrix theory (15-XX) Citations by Year