A modified nonmonotone BFGS algorithm for unconstrained optimization

被引：1
|
作者：
Li, Xiangrong [1 ]
Wang, Bopeng [1 ]
Hu, Wujie [1 ]
机构：
[1] Guangxi Univ, Coll Math & Informat Sci, Guangxi Coll & Univ Key Lab Math & Applicat, Nanning, Guangxi, Peoples R China
来源：
JOURNAL OF INEQUALITIES AND APPLICATIONS | 2017年
关键词：
BFGS update; global convergence; superlinear convergence; nonmonotone; QUASI-NEWTON METHODS; CONJUGATE-GRADIENT ALGORITHM; SYMMETRIC NONLINEAR EQUATIONS; LARGE-SCALE OPTIMIZATION; CONVEX OBJECTIVE FUNCTIONS; TRUST REGION ALGORITHM; LINE SEARCH; SUPERLINEAR CONVERGENCE; GLOBAL CONVERGENCE; MINIMIZATION PROBLEMS;
D O I：
10.1186/s13660-017-1453-5
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, a modified BFGS algorithm is proposed for unconstrained optimization. The proposed algorithm has the following properties: (i) a nonmonotone line search technique is used to obtain the step size ak to improve the effectiveness of the algorithm; (ii) the algorithm possesses not only global convergence but also superlinear convergence for generally convex functions; (iii) the algorithm produces better numerical results than those of the normal BFGS method.
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收藏
页数：18
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