New Restricted Isometry Property Analysis for $\ell_1-\ell_2$ Minimization Methods

DOI10.1137/20M136517XzbMath1474.94041MaRDI QIDQ5860293

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Publication date: 19 November 2021

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

zbMATH Keywords

sparse recoverysparse representationcompressed sensingrestricted isometry property\( \ell_1-\ell_2\) minimization

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items

Structured model selection via ℓ1−ℓ2 optimization ⋮ \(\boldsymbol{L_1-\beta L_q}\) Minimization for Signal and Image Recovery ⋮ \(k\)-sparse vector recovery via truncated \(\ell_1 -\ell_2\) local minimization ⋮ The Dantzig selector: recovery of signal via ℓ ₁ − αℓ ₂ minimization

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