New line search methods for unconstrained optimization

被引:32
|
作者
Yuan, Gonglin [1 ]
Wei, Zengxin [1 ]
机构
[1] Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Unconstrained optimization; Line search method; Global convergence; R-linear convergence; Probability; QUASI-NEWTON METHODS; TRUST REGION ALGORITHM; CONVERGENCE PROPERTIES; CONSTRAINED MINIMIZATION; SUPERLINEAR CONVERGENCE; REGRESSION; BARZILAI;
D O I
10.1016/j.jkss.2008.05.004
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
It is well known that the search direction plays a main role in the line search method. In this paper, we propose a new search direction together with the Wolfe line search technique and one nonmonotone line search technique for solving unconstrained optimization problems. The given methods possess sufficiently descent property without carrying out any line search rule. The convergent results are established under suitable conditions. For numerical results, analysis of one probability shows that the new methods are more effective, robust, and stable, than other similar methods. Numerical results of two statistical problems also show that the presented methods are more interesting than other normal methods. (C) 2008 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:29 / 39
页数:11
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