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Journal of Applied Sciences

Year: 2004 | Volume: 4 | Issue: 3 | Page No.: 498-507
DOI: 10.3923/jas.2004.498.507

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Authors


Zhibin Zhu


Keywords


  • ams subject classificatrions
  • nonlinear minimax problem
  • global convergence
  • sqp algorithm
  • 90c30
  • 65k10
  • superlinear convergence
Research Article

An SQP Method for Solving the Nonlinear Minimax Problem

Zhibin Zhu
In this study, a new algorithm is presented to solve the following nonlinear minimax problem


This algorithm belongs to the sequential quadratic programming (SQP) type methods. At each iteration, the search direction d is obtained by solving one quadratic programming according to the K-T condition of the minimax problem. When d is equal to zero, then the corresponding iteration point x is a K-T point, otherwise, d is a descent direction. Unlike the SQP type algorithms for nonlinear programming, the direction d doesn`t induce any Maratos like effect. A particular linear search with above-mentioned direction assure global convergence as well as superlinear convergence. Numerical results to date suggest the resulting algorithm is effective.
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How to cite this article

Zhibin Zhu, 2004. An SQP Method for Solving the Nonlinear Minimax Problem. Journal of Applied Sciences, 4: 498-507.

DOI: 10.3923/jas.2004.498.507

URL: https://scialert.net/abstract/?doi=jas.2004.498.507

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