Proximal iteratively reweighted algorithm for low-rank matrix recovery

DOI10.1186/s13660-017-1602-xzbMath1391.90477OpenAlexW2782998807WikidataQ47556753 ScholiaQ47556753MaRDI QIDQ1691320

Chao-Qun Ma, Yi-Shuai Ren

Publication date: 15 January 2018

Published in: Journal of Inequalities and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s13660-017-1602-x

zbMATH Keywords

compressed sensingmatrix rank minimizationreweighted nuclear norm minimizationSchatten-\(p\) quasi-norm minimization

Mathematics Subject Classification ID

Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

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• Null space conditions and thresholds for rank minimization
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• Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization
• Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
• Restricted $p$-Isometry Properties of Nonconvex Matrix Recovery

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