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.
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Research Article| June 08 2016
Global optimization for structured low rank approximation
AIP Conf. Proc. 1738, 400003 (2016)
Jonathan Gillard, Anatoly Zhigljavsky; Global optimization for structured low rank approximation. AIP Conf. Proc. 8 June 2016; 1738 (1): 400003. https://doi.org/10.1063/1.4952191
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