$\ell_1$ Minimization with Noisy Data

DOI10.1137/110833130zbMath1245.94049OpenAlexW1983209504MaRDI QIDQ2902997

Przemysław Wojtaszczyk

Publication date: 23 August 2012

Published in: SIAM Journal on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/110833130

zbMATH Keywords

compressed sensingrecovery algorithms

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Approximation by arbitrary nonlinear expressions; widths and entropy (41A46) Rate-distortion theory in information and communication theory (94A34)

Related Items (3)

The Recovery Guarantee for Orthogonal Matching Pursuit Method to Reconstruct Sparse Polynomials ⋮ Stability and robustness of \(\ell_1\)-minimizations with Weibull matrices and redundant dictionaries ⋮ Sparse recovery in probability via \(l_q\)-minimization with Weibull random matrices for \(0 < q\leq 1\)

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