Penalty decomposition methods for rank minimization
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Publication:2943834
DOI10.1080/10556788.2014.936438zbMath1323.65070arXiv1008.5373OpenAlexW2615711751MaRDI QIDQ2943834
Xiaorui Li, Yong Zhang, Zhaosong Lu
Publication date: 4 September 2015
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1008.5373
numerical examplesmatrix completionblock coordinate descent methodrank minimizationnearest low-rank correlation matrixpenalty decomposition methods
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Vector spaces, linear dependence, rank, lineability (15A03) Matrix completion problems (15A83)
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Cites Work
- Unnamed Item
- An implementable proximal point algorithmic framework for nuclear norm minimization
- Fixed point and Bregman iterative methods for matrix rank minimization
- Convex optimization methods for dimension reduction and coefficient estimation in multivariate linear regression
- Efficient rank reduction of correlation matrices
- Maximum stable set formulations and heuristics based on continuous optimization
- Optimal low-rank approximation to a correlation matrix
- Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
- Exact matrix completion via convex optimization
- Lectures on Modern Convex Optimization
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- An augmented Lagrangian dual approach for the H-weighted nearest correlation matrix problem
- Review Paper. Interest–rate term–structure pricing models: a review
- Sparse Optimization with Least-Squares Constraints
- Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization
- A Sequential Semismooth Newton Method for the Nearest Low-rank Correlation Matrix Problem
- Estimation of Positive Semidefinite Correlation Matrices by Using Convex Quadratic Semidefinite 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
- Derivatives of Spectral Functions
- ADMiRA: Atomic Decomposition for Minimum Rank Approximation
- Parameterizing correlations: a geometric interpretation
- Convergence of a block coordinate descent method for nondifferentiable minimization
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