\(s\)-goodness for low-rank matrix recovery

DOI10.1155/2013/101974zbMath1317.94027OpenAlexW2094443059WikidataQ58915278 ScholiaQ58915278MaRDI QIDQ369686

Lingchen Kong, Nai-Hua Xiu, Tunçel, Levent

Publication date: 19 September 2013

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/101974

Mathematics Subject Classification ID

Related Items (6)

A singular value \(p\)-shrinkage thresholding algorithm for low rank matrix recovery ⋮ On a unified view of nullspace-type conditions for recoveries associated with general sparsity structures ⋮ Sparse recovery on Euclidean Jordan algebras ⋮ Regularization parameter selection for the low rank matrix recovery ⋮ A \(p\)-spherical section property for matrix Schatten-\(p\) quasi-norm minimization ⋮ The sparsest solution to the system of absolute value equations

Uses Software

• RASL

Cites Work

• Theory of compressive sensing via \(\ell_1\)-minimization: a non-RIP analysis and extensions
• An implementable proximal point algorithmic framework for nuclear norm minimization
• Fixed point and Bregman iterative methods for matrix rank minimization
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• Nuclear norm minimization for the planted clique and biclique problems
• Characterization of the subdifferential of some matrix norms
• Nonsmooth analysis of singular values. II: Applications
• Exact matrix completion via convex optimization
• Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
• A Singular Value Thresholding Algorithm for Matrix Completion
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Decoding by Linear Programming
• Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• On the rank minimization problem over a positive semidefinite linear matrix inequality
• Accuracy Guarantees for <formula formulatype="inline"> <tex Notation="TeX">$\ell_1$</tex></formula>-Recovery
• Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
• Compressed sensing

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