Optimization challlenges in the structured low rank approximation problem

Gillard, Jonathan William ORCID: https://orcid.org/0000-0001-9166-298X and Zhigljavsky, Anatoly Alexandrovich ORCID: https://orcid.org/0000-0003-0630-8279 2013. Optimization challlenges in the structured low rank approximation problem. Journal of Global Optimization 57 (3) , pp. 733-751. 10.1007/s10898-012-9962-8

Full text not available from this repository.

Abstract

In this paper we illustrate some optimization challenges in the structured low rank approximation (SLRA) problem. SLRA can be described as the problem of finding a low rank approximation of an observed matrix which has the same structure as this matrix (such as Hankel). We demonstrate that the optimization problem arising is typically very difficult: in particular, the objective function is multiextremal even for simple cases. The main theme of the paper is to suggest that the difficulties described in approximating a solution of the SLRA problem open huge possibilities for the application of stochastic methods of global optimization.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Springer
ISSN:	0925-5001
Last Modified:	20 Oct 2022 09:48
URI:	https://orca.cardiff.ac.uk/id/eprint/33151

Citation Data

Cited 27 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item