Exact recovery of sparse multiple measurement vectors by \(l_{2,p}\)-minimization
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Publication:1691327
DOI10.1186/s13660-017-1601-yzbMath1386.15012OpenAlexW2784035757WikidataQ47555828 ScholiaQ47555828MaRDI QIDQ1691327
Publication date: 15 January 2018
Published in: Journal of Inequalities and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1186/s13660-017-1601-y
Determinants, permanents, traces, other special matrix functions (15A15) Positive matrices and their generalizations; cones of matrices (15B48) Linear equations (linear algebraic aspects) (15A06)
Cites Work
- A unified algorithm for mixed \(l_{2,p}\)-minimizations and its application in feature selection
- The null space property for sparse recovery from multiple measurement vectors
- Analysis of convergence for the alternating direction method applied to joint sparse recovery
- Sparse regression using mixed norms
- Real versus complex null space properties for sparse vector recovery
- Exact matrix completion via convex optimization
- Sparse Recovery Algorithms: Sufficient Conditions in Terms of Restricted Isometry Constants
- $NP/CMP$ Equivalence: A Phenomenon Hidden Among Sparsity Models $l_{0}$ Minimization and $l_{p}$ Minimization for Information Processing
- Decoding by Linear Programming
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Direction-of-Arrival Estimation Using a Mixed $\ell _{2,0}$ Norm Approximation
- Sampling-50 years after Shannon
- Sparse Approximate Solutions to Linear Systems
- A Semismooth Newton Method with Multidimensional Filter Globalization for $l_1$-Optimization
- Theoretical and Empirical Results for Recovery From Multiple Measurements
- Sparse solutions to linear inverse problems with multiple measurement vectors
- A sparse signal reconstruction perspective for source localization with sensor arrays
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