Global Convergence of a Nonmonotone Trust Region Algorithm with Memory for Unconstrained Optimization

Authors:
Zhensheng Yu;Anqi Wang
Affiliations:
College of Science, University of Shanghai for Science and Technology, Shanghai, People's Republic of China 200093;College of Science, University of Shanghai for Science and Technology, Shanghai, People's Republic of China 200093
Venue:
Journal of Mathematical Modelling and Algorithms
Year:
2011

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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.