共轭梯度法
非线性共轭梯度法
梯度下降
共轭梯度法的推导
行搜索
共轭残差法
期限(时间)
梯度法
趋同(经济学)
应用数学
数学
结合
直线(几何图形)
财产(哲学)
数学优化
缩小
下降(航空)
计算机科学
双共轭梯度法
数学分析
人工智能
几何学
物理
气象学
哲学
经济
半径
计算机安全
认识论
经济增长
人工神经网络
量子力学
作者
Li Zhang,Weijun Zhou,Donghui Li
标识
DOI:10.1080/10556780701223293
摘要
Abstract In this paper, we propose a three-term conjugate gradient method which can produce sufficient descent condition, that is, . This property is independent of any line search used. When an exact line search is used, this method reduces to the standard Hestenes–Stiefel conjugate gradient method. We also introduce two variants of the proposed method which still preserve the sufficient descent property, and prove that these two methods converge globally with standard Wolfe line search even if the minimization function is nonconvex. We also report some numerical experiment to show the efficiency of the proposed methods. Keywords: Three-term conjugate gradient methodGlobal convergence Acknowledgements The authors would like to thank two referees for giving us many valuable suggestions and comments, which improve this paper greatly. We are very grateful to Professor J. Nocedal and Professor W.W. Hager for providing us with their codes.
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