Block sparse recovery via mixed \(l_2/l_1\) minimization

DOI10.1007/s10114-013-1564-yzbMath1322.94034OpenAlexW1977569001MaRDI QIDQ353620

Jun Hong Lin, Song Li

Publication date: 16 July 2013

Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10114-013-1564-y

zbMATH Keywords

compressed sensingblock RIPblock sparsitymixed \(l_2/l_1\) minimization

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)

Related Items (9)

A new bound on the block restricted isometry constant in compressed sensing ⋮ Recovery of block sparse signals under the conditions on block RIC and ROC by BOMP and BOMMP ⋮ Block-sparse recovery and rank minimization using a weighted \(l_p-l_q\) model ⋮ Irreducible infeasible subsystems of semidefinite systems ⋮ Block-sparse recovery of semidefinite systems and generalized null space conditions ⋮ Group sparse recovery in impulsive noise via alternating direction method of multipliers ⋮ On the null space property of \(l_q\)-minimization for \(0 < q \leq 1\) in compressed sensing ⋮ Convergence and stability analysis of iteratively reweighted least squares for noisy block sparse recovery ⋮ Weighted lp − l1 minimization methods for block sparse recovery and rank minimization

Cites Work

• New bounds on the restricted isometry constant \(\delta _{2k}\)
• The restricted isometry property and its implications for compressed sensing
• Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Decoding by Linear Programming
• Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
• Shifting Inequality and Recovery of Sparse Signals
• Robust Recovery of Signals From a Structured Union of Subspaces
• Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$
• New Bounds for Restricted Isometry Constants
• Model-Based Compressive Sensing
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing

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