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。[著者文摘]

文章出处:
《上海第二工业大学学报》-2007年24卷3期 -225-228页
Journal of Shanghai Second Polytechnic University
分 类 号:
文献标识码:
A
文章编号:
1001-4543(2007)03-0225-04
[参考文献]
Global Convergence of a Memory Gradient Method with Nonmonotone Technique for Unconstrained Optimization
CHEN Qian, HE Xiang-yang (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.[著者文摘]
Key words:
unconstrained optimization; memory gradient method; nonmonotone line search; global convergence
收稿日期: 2006-07-18
修订日期: 2007-01-20
基金资助:
上海市教委科研基金项目(No.05RZ12)

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