MODIFIED LIMITED MEMORY BFGS METHOD WITH NONMONOTONE LINE SEARCH FOR UNCONSTRAINED OPTIMIZATION

被引：20

作者：

Yuan, Gonglin ^{[1
]}

Wei, Zengxin ^{[1
]}

Wu, Yanlin ^{[1
]}

机构：

[1] Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China

来源：

JOURNAL OF THE KOREAN MATHEMATICAL SOCIETY | 2010年 / 47卷 / 04期

关键词：

limited memory BFGS method; optimization; nonmonotone; global convergence; QUASI-NEWTON METHODS; CONJUGATE-GRADIENT METHODS; GLOBAL CONVERGENCE; SUPERLINEAR CONVERGENCE; PARTITIONED BFGS; ALGORITHM;

D O I：

10.4134/JKMS.2010.47.4.767

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we propose two limited memory BFGS algorithms with a nonmonotone line search technique for unconstrained optimization problems. The global convergence of the given methods will be established under suitable conditions. Numerical results show that the presented algorithms are more competitive than the normal BFGS method.

引用

页码：767 / 788

页数：22

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