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带记忆的非单调无约束优化算法的全局收敛性
引用本文:陈茜,贺向阳.带记忆的非单调无约束优化算法的全局收敛性[J].上海第二工业大学学报,2007,24(3):225-228.
作者姓名:陈茜  贺向阳
作者单位:1. 同济大学应用数学系,200092
2. 上海第二工业大学理学院,201209
摘    要:自从非单调线搜索技巧引入非线性优化后,所得的算法得到了成功的应用与扩展。带记忆的梯度方法经常用来求解无约束优化问题,尤其是大规模的问题。将带记忆梯度法与Wolfe非单调线搜索技巧成功融合到一起得到了新算法。证明了该算法全局收敛。

关 键 词:无约束优化  记忆梯度法  非单调线搜索  全局收敛性
文章编号:1001-4543(2007)03-0225-04
收稿时间:2006-07-18
修稿时间:2007-01-20

Global Convergence of a Memory Gradient Method with Nonmonotone Technique for Unconstrained Optimization
CHEN Qian,HE Xiang-yang.Global Convergence of a Memory Gradient Method with Nonmonotone Technique for Unconstrained Optimization[J].Journal of Shanghai Second Polytechnic University,2007,24(3):225-228.
Authors:CHEN Qian  HE Xiang-yang
Affiliation:1. Department of Mathematics, Tongji University, Shanghai200092, P.R. China; 2.School of Science, Shanghai Second Polytechnic University,Shanghai 201029, P.R. China.
Abstract:The technique of nonmonotone line search has received many successful applications and extensions since it was applied in the nonlinear optimization and the memory gradient method is often used for unconstrained optimization,especially large scale problems.This paper combines the memory gradient method and the nonmonotone wolfe line search and the global convergence is obtained.
Keywords:unconstrained optimization  memory gradient method  nonmonotone line search  global convergence
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