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A new globalization technique for nonlinear conjugate gradient methods for nonconvex minimization

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成果类型:
期刊论文
作者:
Zhang, Li*;Li, Junli
通讯作者:
Zhang, Li
作者机构:
[Zhang, Li; Li, Junli] Changsha Univ Sci & Technol, Dept Math, Changsha 410004, Hunan, Peoples R China.
通讯机构:
[Zhang, Li] C
Changsha Univ Sci & Technol, Dept Math, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Descent direction;Global convergence;Nonlinear conjugate gradient method
期刊:
Applied Mathematics and Computation
ISSN:
0096-3003
年:
2011
卷:
217
期:
24
页码:
10295-10304
基金类别:
This work was partially supported by the NSF foundation ( 10701018 ) of China and a project ( 09C058 ) of Scientific Research Fund of Hunan Provincial Education Department .
机构署名:
本校为第一且通讯机构
院系归属:
数学与统计学院
摘要:
It is well-known that the HS method and the PRP method may not converge for nonconvex optimization even with exact line search. Some globalization techniques have been proposed, for instance, the PRP+ globalization technique and the Grippo–Lucidi globalization technique for the PRP method. In this paper, we propose a new efficient globalization technique for general nonlinear conjugate gradient methods for nonconvex minimization. This new technique utilizes the information of the previous search direction sufficiently. Under suitable condition...

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