efficientprojectedgradientmethodsforcardinalityconstrainedoptimization

	efficientprojectedgradientmethodsforcardinalityconstrainedoptimization
	Xu Fengmin 1; Dai Yuhong2 ; Zhao Zhihu 1; Xu Zongben 1
	2019
Source Publication	sciencechinamathematics
ISSN	1674-7283
Volume	62 Issue:2 Pages:245
Abstract	Sparse optimization has attracted increasing attention in numerous areas such as compressed sens-ing, financial optimization and image processing. In this paper, we first consider a special class of cardinality constrained optimization problems, which involves box constraints and a singly linear constraint. An effcient approach is provided for calculating the projection over the feasibility set after a careful analysis on the projec- tion subproblem. Then we present several types of projected gradient methods for a general class of cardinality constrained optimization problems. Global convergence of the methods is established under suitable assump- tions. Finally, we illustrate some applications of the proposed methods for signal recovery and index tracking. Especially for index tracking, we propose a new model subject to an adaptive upper bound on the sparse portfo-lio weights. The computational results demonstrate that the proposed projected gradient methods are effcient in terms of solution quality.
Language	英语
Funding Project	[National Natural Science Foundation of China] ; [National Science Fund for Distinguished Young Scholars] ; [National 973 Program of China]
Document Type	期刊论文
Identifier	http://ir.amss.ac.cn/handle/2S8OKBNM/41470
Collection	计算数学与科学工程计算研究所
Affiliation	1.西安交通大学 2.中国科学院数学与系统科学研究院
Recommended Citation GB/T 7714	Xu Fengmin,Dai Yuhong,Zhao Zhihu,et al. efficientprojectedgradientmethodsforcardinalityconstrainedoptimization[J]. sciencechinamathematics,2019,62(2):245.
APA	Xu Fengmin,Dai Yuhong,Zhao Zhihu,&Xu Zongben.(2019).efficientprojectedgradientmethodsforcardinalityconstrainedoptimization.sciencechinamathematics,62(2),245.
MLA	Xu Fengmin,et al."efficientprojectedgradientmethodsforcardinalityconstrainedoptimization".sciencechinamathematics 62.2(2019):245.

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