A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization. (23rd May 2013)

• Record Type: Journal Article
• Authors: Yu, Zhensheng; Yu, Jinhong
• Other Names: Ammar E. E. Academic Editor.; Silva G. Academic Editor.
• Is Part Of: ISRN operations research. Volume 2013(2013)
• Publication Date: 2013-05-23
• Subjects: Operations research -- Periodicals; Operations research; Electronic journals; Periodicals; 003
• DOI: 10.1155/2013/495378
• Languages: English
• ISSNs: 2314-6397

Abstract

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.