Combining nonmonotone conic trust region and line search techniques for unconstrained optimization

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:235

Article

Cui, Zhaocheng¹  Wu, Boying¹  Qu, Shaojian² 

[1] Harbin Inst Technol, Dept Math, Fac Sci, Harbin 150080, Peoples R China

[2] Harbin Inst Technol, Nat Sci Res Ctr, Harbin 150080, Peoples R China

关键词: Unconstrained optimization;    Nonmonotone trust region method;    Line search;    Conic model;    Global convergence;   

DOI  :  10.1016/j.cam.2010.10.044

来源: Elsevier

【 摘 要 】

In this paper, we propose a trust region method for unconstrained optimization that can be regarded as a combination of conic model, nonmonotone and line search techniques. Unlike in traditional trust region methods, the subproblem of our algorithm is the conic minimization subproblem; moreover, our algorithm performs a nonmonotone line search to find the next iteration point when a trial step is not accepted, instead of resolving the subproblem. The global and superlinear convergence results for the algorithm are established under reasonable assumptions. Numerical results show that the new method is efficient for unconstrained optimization problems. (C) 2010 Elsevier B.V. All rights reserved.

【 授权许可】

Free