Volume 53, Number 3, July-September 2019
|Page(s)||787 - 805|
|Published online||24 June 2019|
Nonmonotone conic trust region method with line search technique for bound constrained optimization⋆
Department of Social Science Teaching, Nanjing Vocational Institute of Railway Technology, Nanjing 210031, China
* Corresponding author: firstname.lastname@example.org
Accepted: 24 June 2017
In this paper, we propose a nonmonotone trust region method for bound constrained optimization problems, where the bounds are dealt with by affine scaling technique. Differing from the traditional trust region methods, the subproblem in our algorithm is based on a conic model. Moreover, when the trial point isn’t acceptable by the usual trust region criterion, a line search technique is used to find an acceptable point. This procedure avoids resolving the trust region subproblem, which may reduce the total computational cost. The global convergence and Q-superlinear convergence of the algorithm are established under some mild conditions. Numerical results on a series of standard test problems are reported to show the effectiveness of the new method.
Mathematics Subject Classification: 65K05 / 90C30
Key words: Nonmonotone technique / conic model / line search / trust region / bound constrained optimization
© EDP Sciences, ROADEF, SMAI 2019
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