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Global Convergence of a Robust Smoothing SQP Method for Semi-Infinite Programming

Author

Listed:
  • C. Ling

    (Zhejiang University of Finance and Economics)

  • L. Q. Qi

    (City University of Hong Kong, Kowloon Tong)

  • G. L. Zhou

    (Curtin University of Technology)

  • S. Y. Wu

    (National Cheng-Kung University)

Abstract

The semi-infinite programming (SIP) problem is a program with infinitely many constraints. It can be reformulated as a nonsmooth nonlinear programming problem with finite constraints by using an integral function. Due to the nondifferentiability of the integral function, gradient-based algorithms cannot be used to solve this nonsmooth nonlinear programming problem. To overcome this difficulty, we present a robust smoothing sequential quadratic programming (SQP) algorithm for solving the nonsmooth nonlinear programming problem. At each iteration of the algorthm, we need to solve only a quadratic program that is always feasible and solvable. The global convergence of the algorithm is established under mild conditions. Numerical results are given.

Suggested Citation

  • C. Ling & L. Q. Qi & G. L. Zhou & S. Y. Wu, 2006. "Global Convergence of a Robust Smoothing SQP Method for Semi-Infinite Programming," Journal of Optimization Theory and Applications, Springer, vol. 129(1), pages 147-164, April.
    • Handle: RePEc:spr:joptap:v:129:y:2006:i:1:d:10.1007_s10957-006-9049-0
    DOI: 10.1007/s10957-006-9049-0
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    References listed on IDEAS

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    1. Dong-Hui Li & Liqun Qi & Judy Tam & Soon-Yi Wu, 2004. "A Smoothing Newton Method for Semi-Infinite Programming," Journal of Global Optimization, Springer, vol. 30(2), pages 169-194, November.
    2. K.L. Teo & X.Q. Yang & L.S. Jennings, 2000. "Computational Discretization Algorithms for Functional Inequality Constrained Optimization," Annals of Operations Research, Springer, vol. 98(1), pages 215-234, December.
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    Cited by:

    1. Xiaojiao Tong & Liqun Qi & Soon-Yi Wu & Felix Wu, 2012. "A smoothing SQP method for nonlinear programs with stability constraints arising from power systems," Computational Optimization and Applications, Springer, vol. 51(1), pages 175-197, January.
    2. Ping Jin & Chen Ling & Huifei Shen, 2015. "A smoothing Levenberg–Marquardt algorithm for semi-infinite programming," Computational Optimization and Applications, Springer, vol. 60(3), pages 675-695, April.
    3. Thinh, Vo Duc & Chuong, Thai Doan & Le Hoang Anh, Nguyen, 2023. "Formulas of first-ordered and second-ordered generalization differentials for convex robust systems with applications," Applied Mathematics and Computation, Elsevier, vol. 455(C).
    4. Jiachen Ju & Qian Liu, 2020. "Convergence properties of a class of exact penalty methods for semi-infinite optimization problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(3), pages 383-403, June.
    5. Qian Liu & Changyu Wang & Xinmin Yang, 2013. "On the convergence of a smoothed penalty algorithm for semi-infinite programming," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 78(2), pages 203-220, October.

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