CSpace  > 计算数学与科学工程计算研究所
Xu Fengmin1; Dai Yuhong2; Zhao Zhihu1; Xu Zongben1
Source Publicationsciencechinamathematics
AbstractSparse 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.
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
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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