A class of nonmonotone trust region algorithm for solving unconstrained nonlinear optimization problems

被引：0
|
作者：
Ye, Fulan [1 ]
You, Yang [1 ]
Chen, Zhen [1 ]
Chen, Baoguo [1 ]
机构：
[1] Fuzhou Univ Int Studies & Trade, Res Ctr Sci Technol & Soc, Fuzhou 350202, Fujian, Peoples R China
来源：
SCIENCEASIA | 2018年 / 44卷 / 01期
关键词：
global convergence; superlinear convergence; numerical experiment; LINE SEARCH TECHNIQUE;
D O I：
10.2306/scienceasia1513-1874.2018.44.027
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Based on the nonmonotone line search technique proposed by Gu and Mo a nonmonotone trust region algorithm is proposed for solving unconstrained nonlinear optimization problems. The new algorithm is resets the ratio rho(k) for evaluating whether the trial step d(k) is acceptable. The global and superlinear convergence of the algorithm are proved under suitable conditions. Numerical results show that the new algorithm is effective.
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页码：27 / 33
页数：7
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