Nonnegative Diagonals and High Performance on Low-Profile Matrices from Householder QR

The Householder reflections used in LAPACK's $QR$ factorization leave positive and negative real entries along $R$'s diagonal. This is sufficient for most applications of $QR$ factorizations, but a few require that $R$ have a nonnegative diagonal. This note describes a new Householder gene...

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Bibliographic Details
Published in:SIAM journal on scientific computing 2009-01, Vol.31 (4), p.2832-2841
Main Authors: Demmel, James W., Hoemmen, Mark, Hida, Yozo, Riedy, E. Jason
Format: Article
Language:English
Subjects:
Computer science
Factorization
Leaves
Mathematical analysis
Matrices
Matrix methods
Multiplication & division
Reflection
Routines
Scanning
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Summary:The Householder reflections used in LAPACK's $QR$ factorization leave positive and negative real entries along $R$'s diagonal. This is sufficient for most applications of $QR$ factorizations, but a few require that $R$ have a nonnegative diagonal. This note describes a new Householder generation routine to produce a nonnegative diagonal. Additionally, we find that scanning for trailing zeros in the generated reflections leads to large performance improvements when applying reflections with many trailing zeros. Factoring low-profile matrices, those with nonzero entries mostly near the diagonal (e.g., band matrices), now require far fewer operations. For example, $QR$ factorization of matrices with profile width $b$ that are stored densely in an $n\times n$ matrix improves from $O(n^3)$ to $O(n^2+nb^2)$. These routines are in LAPACK 3.2.
ISSN:1064-8275
1095-7197
DOI:10.1137/080725763