Global convergence of a new Wei-Yao-Liu type conjugate gradient method

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摘要:

共轭梯度法是求解大规模无约束优化问题的一类重要的优化方法,该方法具有全局收敛性和存储量小的优点.提出了一类修正的Wei-Yao-Liu型三项共轭梯度法,该方法扩大了其中参数的选择范围,在强Wolfe搜索下满足充分下降条件和全局收敛性.初步的数值试验说明了算法的有效性.

Due to the features of strong global convergence properties and low memory requirement,conjugate gradient methods constitute an active choice for efficiently solving the large-scale unconstrained optimization problems.In this paper,an improved Wei-Yao-Liu type three-term conjugate gradient method is proposed,in which the scope of the involved parameter is enlarged.With the proper conditions,the sufficient descent condition and global convergence of the presented method are satisfied with the strong Wolfe conditions.preliminary computational results show that the improved method is effcient and can be used to deal with some test problems.

作者:

董晓亮李卫军

Dong Xiaoliang;Li Weijun(School of Mathematics and Information;Network Information Technology Center,Beifang Minzu University,Yinchuan 750021,China)

机构地区:

北方民族大学数学与信息科学学院北方民族大学网络信息中心

出处:

《betway官方app 学报：自然科学版》 CAS 北大核心 2018年第4期107-112,共6页

基金:

国家自然科学基金青年基金(11601012) 宁夏自然科学基金(NZ17103) 宁夏高校科研基金(NGY2016134 NGY2016143) 北方民族大学科研项目(2016SXKY05 2016SXKY06) 北方民族大学重大专项项目(ZDZX201804)

关键词:

共轭梯度法全局收敛性充分下降条件

conjugate gradient method global convergence sufficient descent condition