A nonmonotone trust region method based on simple conic models for unconstrained optimization

被引：5
|
作者：
Zhou, Qunyan [1 ]
Zhou, Fen [2 ]
Cao, Fengxue [3 ]
机构：
[1] Jiangsu Univ Technol, Sch Math & Phys, Changzhou 213001, Peoples R China
[2] Hohai Univ, Changzhou Branch, Dept Math & Phys, Changzhou 213022, Peoples R China
[3] Jiangsu Univ Technol, Sch Comp Engn, Changzhou 213001, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2013年 / 225卷
关键词：
Nonmonotone trust region method; Simple conic model; Global convergence; Unconstrained optimization; LINE SEARCH TECHNIQUE; MINIMIZATION; ALGORITHM;
D O I：
10.1016/j.amc.2013.09.038
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A new nonmonotone trust region algorithm with simple conic models for unconstrained optimization is proposed. Compared to traditional conic trust region methods, the new method needs less memory capacitance and computational complexity. The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions. Numerical tests indicate that the new algorithm is efficient and robust. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码：295 / 305
页数：11
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