A fast proximal point algorithm for \(\ell_{1}\)-minimization problem in compressed sensing

DOI10.1016/j.amc.2015.08.082zbMath1410.90160OpenAlexW2198277968MaRDI QIDQ670842

Gaohang Yu, Yun Zhu, Jian Wu

Publication date: 20 March 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2015.08.082

zbMATH Keywords

proximal point algorithmcompressed sensing\(\ell_{1}\)-regularized least square

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Convex programming (90C25) Sensitivity, stability, parametric optimization (90C31) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Convergence rate analysis for fixed-point iterations of generalized averaged nonexpansive operators ⋮ Smoothed \(\ell_1\)-regularization-based line search for sparse signal recovery

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