A new sufficient condition for sparse vector recovery via ℓ1 − ℓ2 local minimization

DOI10.1142/S0219530521500068zbMath1492.94029OpenAlexW3153729528MaRDI QIDQ5016825

Jun Tan, Wai-Shing Tang, Ning Bi

Publication date: 13 December 2021

Published in: Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219530521500068

zbMATH Keywords

compressive sensingsparse vectorvector recovery\(\ell_1\)-\( \ell_2\) minimization

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) General harmonic expansions, frames (42C15) Approximation with constraints (41A29) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10) Sampling theory in information and communication theory (94A20)

Related Items (2)

A necessary and sufficient condition for sparse vector recovery via \(\ell_1-\ell_2\) minimization ⋮ \(k\)-sparse vector recovery via truncated \(\ell_1 -\ell_2\) local minimization

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