Optimization challenges in the structured low rank approximation problem

DOI10.1007/s10898-012-9962-8zbMath1285.90040OpenAlexW1968621307MaRDI QIDQ2434635

Anatoly A. Zhigljavsky, Jonathan Gillard

Publication date: 6 February 2014

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-012-9962-8

zbMATH Keywords

global optimizationHankel matrixstructured low rank approximationCadzow iterations

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26)

Related Items (9)

Deconvolution of a discrete uniform distribution ⋮ Computation of the nearest non-prime polynomial matrix: structured low-rank approximation approach ⋮ Weighted norms in subspace-based methods for time series analysis ⋮ On the Least-Squares Fitting of Data by Sinusoids ⋮ Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms ⋮ Derivative-free local tuning and local improvement techniques embedded in the univariate global optimization ⋮ 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

• CaterpillarSSA

Cites Work

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