Global Convergence of a Nonmonotone Trust Region Algorithm with Memory for Unconstrained Optimization |
| |
Authors: | Zhensheng Yu Anqi Wang |
| |
Institution: | 1. College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, People??s Republic of China
|
| |
Abstract: | In this paper, we consider a trust region algorithm for unconstrained optimization problems. Unlike the traditional memoryless
trust region methods, our trust region model includes memory of the past iteration, which makes the algorithm less myopic
in the sense that its behavior is not completely dominated by the local nature of the objective function, but rather by a
more global view. The global convergence is established by using a nonmonotone technique. The numerical tests are also given
to show the efficiency of our proposed method. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|