Optimization challenges in the structured low rank approximation problem

Jonathan Gillard () and Anatoly Zhigljavsky ()

Journal of Global Optimization, 2013, vol. 57, issue 3, 733-751

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. Copyright Springer Science+Business Media, LLC. 2013

Keywords: Structured low rank approximation; Hankel matrix; Global optimization; Cadzow iterations (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10898-012-9962-8

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