Low rank approximation of a Hankel matrix by structured total least norm
type: Item
instance of: scholarly article
title: Low rank approximation of a Hankel matrix by structured total least norm
zbMATH Open document ID: 0944.65043
DOI: 10.1023/A:1022347425533
author: Haesun Park
author: Lei Zhang
author: J. B. Rosen
published in: BIT
publication date: 2000-02-03T00:00:00Z
review text: This paper deals with the structure preserving low rank approximation of a matrix. By formulating the structure preserving low rank approximation problem as a constrained optimization problem, based on the structured total least norm, a new method is presented. An algorithm is described for Hankel structure preserving low rank approximation using a structured total least norm with the \(L_p\) norm. Numerical examples are presented to demonstrate the performances of the new method. The results show that the new method takes advantages over other methods such as singular value decomposition based method and \textit{J. A. Cadzow's} method [IEEE Trans. Acoustics, Speech, and Signal Processing, No. 1, ASSP-36, 49-62 (1988; Zbl 0649.93059)].
Mathematics Subject Classification ID: 65F20
zbMATH DE Number: 1398431
zbMATH Keywords: overdetermined linear system
zbMATH Keywords: rank reduction
zbMATH Keywords: numerical examples
zbMATH Keywords: structure preserving low rank approximation
zbMATH Keywords: constrained optimization
zbMATH Keywords: algorithm
zbMATH Keywords: Hankel structure
zbMATH Keywords: structured total least norm
zbMATH Keywords: performances
zbMATH Keywords: singular value decomposition
reviewed by: Liu Xinguo
describes a project that uses: DGGLSE
describes a project that uses: MINOS
MaRDI profile type: Publication
full work available at URL: a:1022347425533
OpenAlex ID: W191724716
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description: scientific article; zbMATH DE number 1398431