A singular value \(p\)-shrinkage thresholding algorithm for low rank matrix recovery

DOI10.1007/s10589-019-00084-yzbMath1414.90287OpenAlexW2922326455WikidataQ128298200 ScholiaQ128298200MaRDI QIDQ2419553

Kun Shang, Yu-Fan Li, Zheng-Hai Huang

Publication date: 13 June 2019

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-019-00084-y

zbMATH Keywords

matrix completionimage inpaintinglow rank matrix recoverysingular value \(p\)-shrinkage thresholding

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (3)

A singular value shrinkage thresholding algorithm for folded concave penalized low-rank matrix optimization problems ⋮ Enhancing low-rank tensor completion via first-order and second-order total variation regularizations ⋮ T-product factorization based method for matrix and tensor completion problems

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Nearly unbiased variable selection under minimax concave penalty
• An alternating direction method with continuation for nonconvex low rank minimization
• \(s\)-goodness for low-rank matrix recovery
• Fixed point and Bregman iterative methods for matrix rank minimization
• Null space conditions and thresholds for rank minimization
• A perturbation inequality for concave functions of singular values and its applications in low-rank matrix recovery
• Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
• Weighted nuclear norm minimization and its applications to low level vision
• A reweighted nuclear norm minimization algorithm for low rank matrix recovery
• Complexity analysis of interior point algorithms for non-Lipschitz and nonconvex minimization
• Exact matrix completion via convex optimization
• Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
• EXACT LOW-RANK MATRIX RECOVERY VIA NONCONVEX SCHATTEN p-MINIMIZATION
• Worst-Case Complexity of Smoothing Quadratic Regularization Methods for Non-Lipschitzian Optimization
• A Singular Value Thresholding Algorithm for Matrix Completion
• Compressed sensing recovery via nonconvex shrinkage penalties
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• On $l_q$ Optimization and Matrix Completion
• Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Restricted $p$-Isometry Properties of Nonconvex Matrix Recovery

This page was built for publication: A singular value \(p\)-shrinkage thresholding algorithm for low rank matrix recovery