Low-rank matrix recovery problem minimizing a new ratio of two norms approximating the rank function then using an ADMM-type solver with applications
From MaRDI portal
Publication:6056241
DOI10.1016/j.cam.2023.115564MaRDI QIDQ6056241
Zheng-Hai Huang, Lulu Guo, Kaixin Gao
Publication date: 30 October 2023
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Numerical mathematical programming methods (65K05) Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Random matrices (algebraic aspects) (15B52)
Cites Work
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Fixed point and Bregman iterative methods for matrix rank minimization
- On gradients of functions definable in o-minimal structures
- Analysis of the ratio of \(\ell_1\) and \(\ell_2\) norms in compressed sensing
- Low-rank traffic matrix completion with marginal information
- Weighted nuclear norm minimization and its applications to low level vision
- A reweighted nuclear norm minimization algorithm for low rank matrix recovery
- Ratio and difference of \(l_1\) and \(l_2\) norms and sparse representation with coherent dictionaries
- Majorized proximal alternating imputation for regularized rank constrained matrix completion
- Low rank matrix minimization with a truncated difference of nuclear norm and Frobenius norm regularization
- Augmented $\ell_1$ and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm
- A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
- The Stability of Low-Rank Matrix Reconstruction: A Constrained Singular Value View
- A Singular Value Thresholding Algorithm for Matrix Completion
- Global Convergence of Splitting Methods for Nonconvex Composite Optimization
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Performance Analysis of Sparse Recovery Based on Constrained Minimal Singular Values
- A Fixed Point Iterative Method for Low $n$-Rank Tensor Pursuit
- Iterative Concave Rank Approximation for Recovering Low-Rank Matrices
- First-Order Methods in Optimization
- Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
- Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction
- High-Dimensional Probability
- Comparing Measures of Sparsity
- Convergence of alternating direction method for minimizing sum of two nonconvex functions with linear constraints
- Minimization of $L_1$ Over $L_2$ for Sparse Signal Recovery with Convergence Guarantee
- Minimizing L 1 over L 2 norms on the gradient
- Accelerated Schemes for the $L_1/L_2$ Minimization
- A Scale-Invariant Approach for Sparse Signal Recovery
- A long and winding road to definable sets
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Restricted $p$-Isometry Properties of Nonconvex Matrix Recovery
- A Multiplicative Iterative Algorithm for Box-Constrained Penalized Likelihood Image Restoration
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
- Limited-Angle CT Reconstruction via the $L_1/L_2$ Minimization
This page was built for publication: Low-rank matrix recovery problem minimizing a new ratio of two norms approximating the rank function then using an ADMM-type solver with applications