Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery

DOI10.1109/TSP.2020.3037996OpenAlexW3002118509MaRDI QIDQ5103223

Nan Meng, Yun-bin Zhao

Publication date: 23 September 2022

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2001.07181

Mathematics Subject Classification ID

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

Related Items (7)

Improved RIP-based bounds for guaranteed performance of two compressed sensing algorithms ⋮ Newton-type optimal thresholding algorithms for sparse optimization problems ⋮ Accurate and efficient image segmentation and bias correction model based on entropy function and level sets ⋮ Heavy-ball-based optimal thresholding algorithms for sparse linear inverse problems ⋮ Scaled proximal gradient methods for sparse optimization problems ⋮ Dual-density-based reweighted \(\ell_1\)-algorithms for a class of \(\ell_0\)-minimization problems ⋮ Partial gradient optimal thresholding algorithms for a class of sparse optimization problems

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