An Iterative Threshold Algorithm of Log-sum Regularization for Sparse Problem

The log-sum function as a penalty has always been drawing widespread attention in the field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz optimization problem that is difficult to tackle. To overcome the problem, an iterative threshold algorithm for the sparse opt...

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Published inIEEE transactions on circuits and systems for video technology Vol. 33; no. 9; p. 1
Main Authors Zhou, Xin, Liu, Xiaowen, Zhang, Gong, Jia, Luliang, Wang, Xu, Zhao, Zhiyuan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:The log-sum function as a penalty has always been drawing widespread attention in the field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz optimization problem that is difficult to tackle. To overcome the problem, an iterative threshold algorithm for the sparse optimization problems with log-sum function is proposed in this paper. For brevity, the sparse optimization problems with log-sum function are named log-sum regularization. Firstly, by introducing an intermediate function to construct another new function, a property theorem about solution for log-sum regularization is established. Secondly, based on the above theorem, the optimal setting rules of the compromising parameters are elaborated, and an iterative log-sum threshold algorithm is proposed. Thirdly, under the situation that the compromising parameters of log-sum regularization are relatively small, it can be proven that the proposed algorithm converges to a local minimizer of log-sum regularization. Finally, a series of simulations are implemented to examine performance of the proposed algorithm, and the results exhibit that the proposed algorithm outperforms the state-of-the-art algorithms.
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content type line 14
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2023.3247944