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A new Liu–Storey type nonlinear conjugate gradient method for unconstrained optimization problems

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成果类型:
期刊论文
作者:
Zhang, Li*
通讯作者:
Zhang, Li
作者机构:
[Zhang, Li; Zhang, L] Changsha Univ Sci & Technol, Coll Math & Computat Sci, Changsha 410076, Hunan, Peoples R China.
通讯机构:
[Zhang, Li] C
Changsha Univ Sci & Technol, Coll Math & Computat Sci, Changsha 410076, Hunan, Peoples R China.
语种:
英文
关键词:
Approximation theory;Flow measurement;Functions;Gradient methods;Newton-Raphson method;Probability density function;General functions;Global convergence;Least-squares method (LSM);Line searches;Newton methods;Non convex minimization;Nonconvex function;Numerica l results;Optimiz ation problems;Conjugate gradient method
期刊:
Journal of Computational and Applied Mathematics
ISSN:
0377-0427
年:
2009
卷:
225
期:
1
页码:
146-157
基金类别:
This work was supported by the NSF foundation (10701018) of China.
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:
Although the Liu-Storey (LS) nonlinear conjugate gradient method has a similar structure as the well-known Polak-Ribiere-Polyak (PRP) and Hestenes-Stiefel (HS) methods, research about this method is very rare. In this paper, based on the memoryless BFGS quasi-Newton method, we propose a new LS type method, which converges globally for general functions with the Grippo-Lucidi line search. Moreover, we modify this new LS method such that the modified scheme is globally convergent for nonconvex minimization if the strong Wolfe line search is used. Numerical ...

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