Two-stage convex relaxation approach to least squares loss constrained low-rank plus sparsity optimization problems

DOI10.1007/s10589-015-9797-6zbMath1365.90199OpenAlexW1858523460MaRDI QIDQ276859

Shaohua Pan, Le Han, Shujun Bi

Publication date: 4 May 2016

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

Full work available at URL: https://doi.org/10.1007/s10589-015-9797-6

zbMATH Keywords

convex relaxationlocally Lipschitz surrogateslow-rank plus sparsity optimization problems

Mathematics Subject Classification ID

Convex programming (90C25) Combinatorial optimization (90C27)

Related Items (1)

Two-stage convex relaxation approach to low-rank and sparsity regularized least squares loss

Uses Software

• RASL

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
• Some sharp performance bounds for least squares regression with \(L_1\) regularization
• TILT: transform invariant low-rank textures
• Two proposals for robust PCA using semidefinite programming
• Efficient algorithms for robust and stable principal component pursuit problems
• 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
• Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming
• Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
• Robust principal component analysis?
• Rank-Sparsity Incoherence for Matrix Decomposition
• Decoding by Linear Programming
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition
• Compressive principal component pursuit
• Robust PCA via Outlier Pursuit
• Robust Matrix Decomposition With Sparse Corruptions
• Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
• Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Convex Analysis

This page was built for publication: Two-stage convex relaxation approach to least squares loss constrained low-rank plus sparsity optimization problems