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Abstract
In this paper, we propose a nonmonotone sequential quadratic programming-filter method for solving nonlinear equality constrained optimization. This new method has more flexibility for the acceptance of the trial step and requires less computational costs compared with the monotone methods. Under reasonable conditions, we give the global convergence properties. Further, the second-order correction step and nonmonotone reduction conditions are used to overcome Maratos effect so that quadratic local convergence is achieved. The numerical experiments are reported to show the effectiveness of the proposed algorithm.
Acknowledgements
The authors would like to thank two anonymous referees for their comments and suggestions that improved the presentation of the paper. The authors gratefully acknowledge the partial supports of the High Level Program of Shanghai Municipal Education Commission (Finance Credit Knowledge Innovation System No. OB09008011), the National Science Foundation Grant (10871130) of China, the Shanghai Leading Academic Discipline Project (T0401), and the Shanghai Finance Budget Project (1138IA0005).