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An Interior Point Algorithm for Computing Saddle Points of Constrained Continuous Minimax

Author

Listed:
  • Stanislav Žaković
  • Costas Pantelides
  • Berc Rustem

Abstract

The aim of this paper is to present an algorithm for finding a saddle point to the constrained minimax problem. The initial problem is transformed into an equivalent equality constrained problem, and then the interior point approach is used. To satisfy the original inequality constraints a logarithmic barrier function is used and special care is given to step size parameter to keep the variables within permitted boundaries. Numerical results illustrating the method are given. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Stanislav Žaković & Costas Pantelides & Berc Rustem, 2000. "An Interior Point Algorithm for Computing Saddle Points of Constrained Continuous Minimax," Annals of Operations Research, Springer, vol. 99(1), pages 59-77, December.
    • Handle: RePEc:spr:annopr:v:99:y:2000:i:1:p:59-77:10.1023/a:1019284715657
    DOI: 10.1023/A:1019284715657
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    Citations

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    Cited by:

    1. P. Parpas & B. Rustem, 2009. "An Algorithm for the Global Optimization of a Class of Continuous Minimax Problems," Journal of Optimization Theory and Applications, Springer, vol. 141(2), pages 461-473, May.
    2. Tadeusz Antczak & Najeeb Abdulaleem, 2021. "E-differentiable minimax programming under E-convexity," Annals of Operations Research, Springer, vol. 300(1), pages 1-22, May.
    3. Zheng, Liang & Bao, Ji & Xu, Chengcheng & Tan, Zhen, 2022. "Biobjective robust simulation-based optimization for unconstrained problems," European Journal of Operational Research, Elsevier, vol. 299(1), pages 249-262.
    4. Hakan Kaya, 2017. "Managing ambiguity in asset allocation," Journal of Asset Management, Palgrave Macmillan, vol. 18(3), pages 163-187, May.
    5. Dimitris Bertsimas & Omid Nohadani & Kwong Meng Teo, 2010. "Robust Optimization for Unconstrained Simulation-Based Problems," Operations Research, INFORMS, vol. 58(1), pages 161-178, February.

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