A null-space-based weightedl1minimization approach to compressed sensing

DOI10.1093/imaiai/iaw002zbMath1386.94050OpenAlexW2315788538MaRDI QIDQ4603732

Sheng-Long Zhou, Lingchen Kong, Yingnan Wang, Hou-Duo Qi, Nai-Hua Xiu

Publication date: 19 February 2018

Published in: Information and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/imaiai/iaw002

zbMATH Keywords

compressed sensingrestricted isometry constantnull space propertyweighted \(\ell_1\) minimization

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items

Sufficient conditions on stable reconstruction of weighted problem ⋮ Gradient projection Newton pursuit for sparsity constrained optimization ⋮ Newton method for \(\ell_0\)-regularized optimization ⋮ The null space property of the weighted ℓr − ℓ1 minimization ⋮ Unnamed Item ⋮ A Lagrange-Newton algorithm for sparse nonlinear programming

Uses Software

• GQTPAR
• CoSaMP

Cites Work

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