摘要
本文研究了一种新的求解无约束优化问题的非线性共轭梯度方法,其能够在广义Wolfe线搜索下保证充分下降条件:gT k dk≤(1-σ)||gk||2,并且具有全局收敛性,改进了传统CD方法(Fletcher,1987,[1])的缺陷.最后,通过与著名的CD方法(Fletcher,1987,[1])和PRP方法(Polak,Ribire,[2],Polak[3],1969)比较,结果显示新方法具有一定的研究意义.
In this paper, a new nonlinear conjugate gradient method is studied to solve the unconstrained optimization problems, which can guarantee the sufficient descent property: T gk^T dk 〈 -(1 -σ)││gk││2 and global convergence property under the general Wolfe line search con- ditions, and improve the CD method (Fletcher R, 1987, [1] ) defects. In the last part, numerical results are reported which show that the proposed method has a certain research significance by comparing with the famous CD method (Fletcher R, 1987, [1]) and the famous PRP method (Polak n,Ribire G [2], Polak B.T [3], 1969 ).
出处
《数学杂志》
CSCD
北大核心
2013年第6期1036-1042,共7页
Journal of Mathematics
基金
Supported by Nature Science Foundation of Chongqing Education Committee(KJ121112)
National Natural Science Foundation for Young Scholars(11201510)
关键词
无约束最优化
共轭梯度法
广义Wolfe线搜索
全局收敛性
unconstrained optimization
conjugate gradient method
general Wolfe line search
global convergence