EXACT LOW-RANK MATRIX RECOVERY VIA NONCONVEX SCHATTEN p-MINIMIZATION
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Publication:2846490
DOI10.1142/S0217595913400101zbMath1273.90257MaRDI QIDQ2846490
Publication date: 5 September 2013
Published in: Asia-Pacific Journal of Operational Research (Search for Journal in Brave)
Numerical mathematical programming methods (65K05) Applications of mathematical programming (90C90) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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Cites Work
- A note on the complexity of \(L _{p }\) minimization
- Sparse recovery by non-convex optimization - instance optimality
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- Exact matrix completion via convex optimization
- Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization
- Robust principal component analysis?
- Compressed sensing and best 𝑘-term approximation
- Restricted isometry properties and nonconvex compressive sensing
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Uncertainty principles and ideal atomic decomposition
- Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
- Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$
- On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- New Bounds for Restricted Isometry Constants
- Compressed sensing
