Stochastic algorithms for solving structured low-rank matrix approximation problems

DOI10.1016/j.cnsns.2014.08.023zbMath1336.65070OpenAlexW2014411195MaRDI QIDQ907198

Jonathan Gillard, Anatoly A. Zhigljavsky

Publication date: 25 January 2016

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: http://orca.cf.ac.uk/71431/1/PaperHankelLA1.pdf

zbMATH Keywords

global optimizationHankel matrixstructured low rank approximation

Mathematics Subject Classification ID

Related Items (8)

Weighted norms in subspace-based methods for time series analysis ⋮ Metaheuristic vs. deterministic global optimization algorithms: the univariate case ⋮ On the Least-Squares Fitting of Data by Sinusoids ⋮ Preface to the special issue NUMTA 2013 ⋮ On the search of the shape parameter in radial basis functions using univariate global optimization methods ⋮ Blind deconvolution of covariance matrix inverses for autoregressive processes ⋮ On Deterministic Diagonal Methods for Solving Global Optimization Problems with Lipschitz Gradients ⋮ Structured low-rank approximation: optimization on matrix manifold approach

Uses Software

• TDSL
• SLRA
• CaterpillarSSA
• TSDL

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