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A feasible filter SQP algorithm with global and local convergence

  • Applied mathematics
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Abstract

A feasible sequential quadratic programming (SQP) filter algorithm is proposed for general nonlinear programming. It is based on the modified quadratic programming (QP) subproblem in which each iteration proceeds in two phases. The first phase solves a general convex QP problem which does not require any feasibility restoration phase whose computation may be expensive. And, under some mild conditions, the global convergence is proved. The second phase can make the presented SQP method derive quadratic convergence by employing exact Hessian information.

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Acknowledgements

This research is supported by National Science Foundation of China (No. 10771162)

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Authors and Affiliations

  1. Department of Mathematics, Tongji University, Shanghai, 200092, PR China

    Meiling Liu & Dingguo Pu

  2. Business School, University of Shanghai for Science and Technology, Shanghai, 200093, PR China

    Xueqian Li

Authors
  1. Meiling Liu
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  2. Xueqian Li
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  3. Dingguo Pu
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Correspondence to Meiling Liu.

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Liu, M., Li, X. & Pu, D. A feasible filter SQP algorithm with global and local convergence. J. Appl. Math. Comput. 40, 261–275 (2012). https://doi.org/10.1007/s12190-012-0549-9

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  • DOI: https://doi.org/10.1007/s12190-012-0549-9

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