Solving Hankel matrix approximation problem using semidefinite programming
type: Item
instance of: scholarly article
title: Solving Hankel matrix approximation problem using semidefinite programming
zbMATH Open document ID: 1117.65083
published in: Journal of Computational and Applied Mathematics
publication date: 2007-04-13T00:00:00Z
review text: The author studies the problem of finding the nearest matrix to a positive semidefinite Hankel matrix in such a way that all the special properties are preserved. In the usual evaluation techniques rounding and truncation limitations result in some of the properties to be lost. The author describes a semidefinite programming optimization problem, the solution of which can produce a suitable matrix. The proposed algorithm is presented in detail (section 4) and the results of the computational experimentation are reported at the end of the article.
reviewed by: Efstratios Rappos
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C22
Mathematics Subject Classification ID: 90C51
Mathematics Subject Classification ID: 65F30
zbMATH DE Number: 5142299
zbMATH Keywords: Hankel matrix
zbMATH Keywords: semidefinite programming
zbMATH Keywords: primal-dual interior-point algorithm
zbMATH Keywords: numerical examples
zbMATH Keywords: nearest matrix
zbMATH Keywords: algorithm
author: Suliman Al-Homidan
describes a project that uses: CRAIG
describes a project that uses: SDPT3
describes a project that uses: LSQR
describes a project that uses: KELLEY
MaRDI profile type: Publication
full work available at URL: j.cam.2006.02.033
OpenAlex ID: W1977701268
cites work: Q3421416
cites work: Q5420586
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DOI: 10.1016/J.CAM.2006.02.033
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description: scientific article; zbMATH DE number 5142299