Global optimization for structured low rank approximation

Gillard, Jonathan William ORCID: https://orcid.org/0000-0001-9166-298X and Zhigljavsky, Anatoly ORCID: https://orcid.org/0000-0003-0630-8279 2016. Global optimization for structured low rank approximation. Presented at: International Conference of Numerical Analysis and Applied Mathematics 2015, Rhodes, Greece, 22-28 September 2015. AIP Conference Proceedings. , vol.1738 American Institute of Physics, p. 400003. 10.1063/1.4952191

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Abstract

In this paper, we investigate the complexity of the numerical construction of the Hankel structured low-rank approximation (HSLRA) problem, and develop a family of algorithms to solve this problem. Briefly, HSLRA is the problem of finding the closest (in some pre-defined norm) rank r approximation of a given Hankel matrix, which is also of Hankel structure. Unlike many other methods described in the literature the family of algorithms we propose has the property of guaranteed convergence.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	American Institute of Physics
ISSN:	0094-243X
Date of First Compliant Deposit:	13 October 2016
Date of Acceptance:	13 October 2016
Last Modified:	01 Nov 2022 11:32
URI:	https://orca.cardiff.ac.uk/id/eprint/95326

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