Efficient algorithms for robust and stable principal component pursuit problems
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Publication:2450901
DOI10.1007/s10589-013-9613-0zbMath1320.90057arXiv1309.6976OpenAlexW2025736252MaRDI QIDQ2450901
Donald Goldfarb, Necdet Serhat Aybat, Shi-Qian Ma
Publication date: 23 May 2014
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1309.6976
convex optimizationsmoothingmatrix completionprincipal component analysiscompressed sensingiteration complexityalternating linearization methodaccelerated proximal gradient methodalternating direction augmented Lagrangian method
Related Items (5)
Two-stage convex relaxation approach to least squares loss constrained low-rank plus sparsity optimization problems ⋮ An alternating direction method with increasing penalty for stable principal component pursuit ⋮ Two-stage convex relaxation approach to low-rank and sparsity regularized least squares loss ⋮ Scalable Robust Matrix Recovery: Frank--Wolfe Meets Proximal Methods ⋮ A customized proximal point algorithm for stable principal component pursuit with nonnegative constraint
Uses Software
Cites Work
- Unnamed Item
- Smooth minimization of non-smooth functions
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Fast alternating linearization methods for minimizing the sum of two convex functions
- An alternating direction method with increasing penalty for stable principal component pursuit
- Fixed point and Bregman iterative methods for matrix rank minimization
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Introductory lectures on convex optimization. A basic course.
- A unified approach for minimizing composite norms
- Exact matrix completion via convex optimization
- A First-Order Augmented Lagrangian Method for Compressed Sensing
- Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
- Robust principal component analysis?
- A Singular Value Thresholding Algorithm for Matrix Completion
- Rank-Sparsity Incoherence for Matrix Decomposition
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Proximal Decomposition Via Alternating Linearization
- The Power of Convex Relaxation: Near-Optimal Matrix Completion
- Compressed sensing
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