@Article{JCM-38-3,
author = {Weichao, Kong and Wang, Jianjun and Wendong, Wang and Zhang, Feng},
title = {Enhanced Block-Sparse Signal Recovery Performance via Truncated $ℓ_2/ℓ_{1−2}$ Minimization},
journal = {Journal of Computational Mathematics},
year = {2020},
volume = {38},
number = {3},
pages = {437--451},
abstract = {<p style="text-align: justify;">In this paper, we investigate truncated $ℓ_2/ℓ_{1−2}$ minimization and its associated alternating direction method of multipliers (ADMM) algorithm for recovering the block sparse
signals. Based on the block restricted isometry property (Block-RIP), a theoretical analysis is presented to guarantee the validity of proposed method. Our theoretical results
not only show a less error upper bound, but also promote the former recovery condition
of truncated ℓ<sub>1−2</sub> method for sparse signal recovery. Besides, the algorithm has been
compared with some state-of-the-art algorithms and numerical experiments have shown
excellent performances on recovering the block sparse signals.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1811-m2017-0275},
url = {https://global-sci.com/article/84323/enhanced-block-sparse-signal-recovery-performance-via-truncated-2-12-minimization}
}