An Inexact Penalty Decomposition Method for Sparse Optimization. (15th July 2021)

• Record Type: Journal Article
• Authors: Dong, Zhengshan; Lin, Geng; Chen, Niandong
• Other Names: Leung Henry Man Fai Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-07-15
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/9943519
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The penalty decomposition method is an effective and versatile method for sparse optimization and has been successfully applied to solve compressed sensing, sparse logistic regression, sparse inverse covariance selection, low rank minimization, image restoration, and so on. With increase in the penalty parameters, a sequence of penalty subproblems required being solved by the penalty decomposition method may be time consuming. In this paper, an acceleration of the penalty decomposition method is proposed for the sparse optimization problem. For each penalty parameter, this method just finds some inexact solutions to those subproblems. Computational experiments on a number of test instances demonstrate the effectiveness and efficiency of the proposed method in accurately generating sparse and redundant representations of one-dimensional random signals.