一种修正的LS共轭梯度方法及其收敛性

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  • School of Mathematics and Statistics, Chongqing Three Gorges University
LIU Jin-kui(1982-), male, native of Anyang, Henan, a lecturer of Chongqing Three Gorges University, Master, engages in optimization theory and applications.

收稿日期: 2013-08-07

  网络出版日期: 2023-02-14

基金资助

Supported by The Youth Project Foundation of Chongqing Three Gorges University(13QN17); Supported by the Fund of Scientific Research in Southeast University(the Support Project of Fundamental Research)

A Descent Gradient Method and Its Global Convergence

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  • School of Mathematics and Statistics, Chongqing Three Gorges University
LIU Jin-kui(1982-), male, native of Anyang, Henan, a lecturer of Chongqing Three Gorges University, Master, engages in optimization theory and applications.

Received date: 2013-08-07

  Online published: 2023-02-14

Supported by

Supported by The Youth Project Foundation of Chongqing Three Gorges University(13QN17); Supported by the Fund of Scientific Research in Southeast University(the Support Project of Fundamental Research)

摘要

Y Liu and C Storey(1992) proposed the famous LS conjugate gradient method which has good numerical results. However, the LS method has very weak convergence under the Wolfe-type line search. In this paper, we give a new descent gradient method based on the LS method. It can guarantee the sufficient descent property at each iteration and the global convergence under the strong Wolfe line search. Finally, we also present extensive preliminary numerical experiments to show the efficiency of the proposed method by comparing with the famous PRP+ method. 

本文引用格式

刘金魁 . 一种修正的LS共轭梯度方法及其收敛性[J]. 数学季刊, 2014 , 29(1) : 142 -150 . DOI: 10.13371/j.cnki.chin.q.j.m.2014.01.017

Abstract

Y Liu and C Storey(1992) proposed the famous LS conjugate gradient method which has good numerical results. However, the LS method has very weak convergence under the Wolfe-type line search. In this paper, we give a new descent gradient method based on the LS method. It can guarantee the sufficient descent property at each iteration and the global convergence under the strong Wolfe line search. Finally, we also present extensive preliminary numerical experiments to show the efficiency of the proposed method by comparing with the famous PRP+ method. 
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