Block RLS using row Householder reflections
type: Item
instance of: scholarly article
title: Block RLS using row Householder reflections
zbMATH Open document ID: 0781.65032
DOI: 10.1016/0024-3795(93)90464-Y
author: Adam W. Bojanczyk
author: James G. Nagy
author: Robert J. Plemmons
published in: Linear Algebra and its Applications
publication date: 1993-08-25T00:00:00Z
review text: The authors introduce new row Householder and row hyperbolic Householder reflections to zero a contiguous sequence of entries in a row of a matrix when applied from the left. These reflections are used to develop efficient algorithms for recursive least squares (RLS) problems of the sliding window type. These algorithms are based upon rank-\(k\) modification to the inverse Cholesky factor \(R^{-1}\) of the covariance matrix. Numerical experiments show that these algorithms are rich in matrix- matrix BLAS-3 computations, making them even more economical on high performance architectures than \(k\) applications of rank-1 modification schemes.
Mathematics Subject Classification ID: 65F30
Mathematics Subject Classification ID: 65Y20
zbMATH DE Number: 398917
zbMATH Keywords: Cholesky factorization
zbMATH Keywords: recursive least squares problems
zbMATH Keywords: row hyperbolic Householder reflections
zbMATH Keywords: algorithms
zbMATH Keywords: Numerical experiments
zbMATH Keywords: matrix-matrix BLAS-3 computations
zbMATH Keywords: performance
reviewed by: Prem Narain
describes a project that uses: LAPACK
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 398917