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Annals of Operations Research

, Volume 206, Issue 1, pp 501–525 | Cite as

A hybrid algorithm for linearly constrained minimax problems

  • Fusheng WangEmail author
Article

Abstract

Many real life problems can be stated as a minimax problem, such as economics, finance, management, engineering and other fields, which demonstrate the importance of having reliable methods to tackle minimax problems. In this paper, an algorithm for linearly constrained minimax problems is presented in which we combine the trust-region methods with the line-search methods and curve-search methods. By means of this hybrid technique, it avoids possibly solving the trust-region subproblems many times, and make better use of the advantages of different methods. Under weaker conditions, the global and superlinear convergence are achieved. Numerical experiments show that the new algorithm is robust and efficient.

Keywords

Nonlinear programming Linearly constrained minimax problems Trust-region methods Hybrid technique Superlinear convergence 

Notes

Acknowledgements

The authors are indebted to the editors and anonymous referees for their a number of helpful comments and suggestions that improved the quality of this manuscript.

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of MathematicsTaiyuan Normal UniversityTaiyuanP.R. China

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