Scalable Robust Matrix Recovery: Frank--Wolfe Meets Proximal Methods
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Publication:2830569
DOI10.1137/15M101628XzbMath1348.90465arXiv1403.7588OpenAlexW1517295252MaRDI QIDQ2830569
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Publication date: 28 October 2016
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1403.7588
scalabilityproximal methodsFrank-Wolfecompressive principal component pursuitconditional gradientrobust matrix recovery
Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Methods of reduced gradient type (90C52)
Related Items (14)
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Uses Software
Cites Work
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- Solving semidefinite-quadratic-linear programs using SDPT3
- Latent variable graphical model selection via convex optimization
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
- Conditional gradient algorithms for norm-regularized smooth convex optimization
- Fixed point and Bregman iterative methods for matrix rank minimization
- Conditional gradient algorithms with open loop step size rules
- Partial linearization methods in nonlinear programming
- Efficient algorithms for robust and stable principal component pursuit problems
- 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
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- Sequential greedy approximation for certain convex optimization problems
- Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
- Compressive principal component pursuit
- Robust Matrix Decomposition With Sparse Corruptions
- New analysis and results for the Frank-Wolfe method
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