Table of Contents
ISRN Operations Research
Volume 2013 (2013), Article ID 495378, 7 pages
http://dx.doi.org/10.1155/2013/495378
Research Article

A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization

1College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
2College of Language, University of Shanghai for Science and Technology, Shanghai 200093, China

Received 9 April 2013; Accepted 30 April 2013

Academic Editors: E. E. Ammar and G. Silva

Copyright © 2013 Zhensheng Yu and Jinhong Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a nonmonotone trust region algorithm for nonlinear equality constrained optimization problems. In our algorithm, we use the average of the successive penalty function values to rectify the ratio of predicted reduction and the actual reduction. Compared with the existing nonmonotone trust region methods, our method is independent of the nonmonotone parameter. We establish the global convergence of the proposed algorithm and give the numerical tests to show the efficiency of the algorithm.