A reweighted nuclear norm minimization algorithm for low rank matrix recovery

DOI10.1016/j.cam.2013.12.005zbMath1301.65049OpenAlexW1965356455MaRDI QIDQ2252420

Yanjiao Zhang, Yu-Fan Li, Zheng-Hai Huang

Publication date: 17 July 2014

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2013.12.005

zbMATH Keywords

matrix completion problemreweighted nuclear norm minimizationlow rank matrix minimizationweighted fixed point method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Approximation methods and heuristics in mathematical programming (90C59) Norms of matrices, numerical range, applications of functional analysis to matrix theory (15A60)

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Uses Software

• LMaFit

Cites Work

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• Nonsmooth analysis of singular values. I: Theory
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• An Unconstrained $\ell_q$ Minimization with $0q\leq1$ for Sparse Solution of Underdetermined Linear Systems
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• Smoothing Nonlinear Conjugate Gradient Method for Image Restoration Using Nonsmooth Nonconvex Minimization
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• Restricted isometry properties and nonconvex compressive sensing
• Decoding by Linear Programming
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• Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
• On the Performance of Sparse Recovery Via $\ell_p$-Minimization $(0 \leq p \leq 1)$
• Restricted $p$-Isometry Properties of Nonconvex Matrix Recovery

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