k-Sparse Vector Recovery via $$\ell _1-\alpha \ell _2$$ Local Minimization
From MaRDI portal
Publication:6493963
DOI10.1007/S10957-024-02380-YMaRDI QIDQ6493963
Unnamed Author, Jia Li, Kaihao Liang
Publication date: 29 April 2024
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Cites Work
- Equivalence and strong equivalence between the sparsest and least \(\ell _1\)-norm nonnegative solutions of linear systems and their applications
- Computing sparse representation in a highly coherent dictionary based on difference of \(L_1\) and \(L_2\)
- The null space property for sparse recovery from multiple measurement vectors
- Compressed sensing with coherent and redundant dictionaries
- New bounds on the restricted isometry constant \(\delta _{2k}\)
- The restricted isometry property and its implications for compressed sensing
- A note on guaranteed sparse recovery via \(\ell_1\)-minimization
- Signal recovery under cumulative coherence
- Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing
- Necessary and sufficient conditions of solution uniqueness in 1-norm minimization
- Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization
- Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization
- New Bounds for Restricted Isometry Constants With Coherent Tight Frames
- RSP-Based Analysis for Sparsest and Least $\ell_1$-Norm Solutions to Underdetermined Linear Systems
- Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
- Computational Aspects of Constrained L 1-L 2 Minimization for Compressive Sensing
This page was built for publication: k-Sparse Vector Recovery via $$\ell _1-\alpha \ell _2$$ Local Minimization