A Modified Nonlinear Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization

doi:10.1007/s10957-020-01636-7

	A Modified Nonlinear Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization
	Woldu, Tsegay Giday 2; Zhang, Haibin 2; Zhang, Xin 1; Fissuh, Yemane Hailu 2
刊名	JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
	2020-02-12
页码	16
关键词	Conjugate gradient method Moreau-Yosida regularization Nonsmooth large-scale problems Global convergence
ISSN号	0022-3239
DOI	10.1007/s10957-020-01636-7
通讯作者	Woldu, Tsegay Giday(tsegikuhil@gmail.com)
英文摘要	Nonlinear conjugate gradient methods are among the most preferable and effortless methods to solve smooth optimization problems. Due to their clarity and low memory requirements, they are more desirable for solving large-scale smooth problems. Conjugate gradient methods make use of gradient and the previous direction information to determine the next search direction, and they require no numerical linear algebra. However, the utility of nonlinear conjugate gradient methods has not been widely employed in solving nonsmooth optimization problems. In this paper, a modified nonlinear conjugate gradient method, which achieves the global convergence property and numerical efficiency, is proposed to solve large-scale nonsmooth convex problems. The new method owns the search direction, which generates sufficient descent property and belongs to a trust region. Under some suitable conditions, the global convergence of the proposed algorithm is analyzed for nonsmooth convex problems. The numerical efficiency of the proposed algorithm is tested and compared with some existing methods on some large-scale nonsmooth academic test problems. The numerical results show that the new algorithm has a very good performance in solving large-scale nonsmooth problems.
资助项目	National Natural Science Foundation of China[11771003]
WOS关键词	NONMONOTONE LINE SEARCH ; CONVERGENCE ANALYSIS ; BUNDLE METHODS ; SEGMENTATION
WOS研究方向	Operations Research & Management Science ; Mathematics
语种	英语
出版者	SPRINGER/PLENUM PUBLISHERS
WOS记录号	WOS:000513025700002
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28564]
专题	自动化研究所_脑网络组研究中心
通讯作者	Woldu, Tsegay Giday
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Beijing Univ Technol, Coll Appl Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Woldu, Tsegay Giday,Zhang, Haibin,Zhang, Xin,et al. A Modified Nonlinear Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization[J]. JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS,2020:16.
APA	Woldu, Tsegay Giday,Zhang, Haibin,Zhang, Xin,&Fissuh, Yemane Hailu.(2020).A Modified Nonlinear Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization.JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS,16.
MLA	Woldu, Tsegay Giday,et al."A Modified Nonlinear Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization".JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS (2020):16.