You are here: MIMS > EPrints
MIMS EPrints

2006.147: QR factorization with complete pivoting and accurate computation of the SVD

2006.147: Nicholas J. Higham (2000) QR factorization with complete pivoting and accurate computation of the SVD. Elsevier, Linear Algebra and its Applications, 309. pp. 153-174. ISSN 0024-3795

Full text available as:

PDF - Archive staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
163 Kb

DOI: 10.1016/S0024-3795(99)00230-x


A new algorithm of Demmel et al. for computing the singular value decomposition (SVD) to high relative accuracy begins by computing a rank-revealing decomposition (RRD). Demmel et al. analyse the use of Gaussian elimination with complete pivoting (GECP) for computing the RRD. We investigate the use of QR factorization with complete pivoting (that is, column pivoting together with row sorting or row pivoting) as an alternative to GECP, since this leads to a faster SVD algorithm. We derive a new componentwise backward error result for Householder QR factorization and combine it with the theory of Demmel et al. to show that high relative accuracy in the computed SVD can be expected for matrices that are diagonal scalings of a well-conditioned matrix. An a posteriori error bound is derived that gives useful estimates of the relative accuracy of the computed singular values. Numerical experiments confirm the theoretical predictions.

Item Type:Article
Uncontrolled Keywords:QR factorization; Householder matrix; Row pivoting; Row sorting; Column pivoting; Complete pivoting; Backward error analysis; Singular value decomposition; Relative accuracy; Graded matrices
Subjects:MSC 2000 > 15 Linear and multilinear algebra; matrix theory
MSC 2000 > 65 Numerical analysis
MIMS number:2006.147
Deposited By:Miss Louise Stait
Deposited On:27 June 2006

Download Statistics: last 4 weeks
Repository Staff Only: edit this item