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Parametric Integer Programming Algorithm for Bilevel Mixed Integer Programs

Author

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  • M. Köppe

    (University of California)

  • M. Queyranne

    (Universidad de Chile
    Sauder School of Business at the University of British Columbia)

  • C. T. Ryan

    (Sauder School of Business at the University of British Columbia)

Abstract

We consider discrete bilevel optimization problems where the follower solves an integer program with a fixed number of variables. Using recent results in parametric integer programming, we present polynomial time algorithms for pure and mixed integer bilevel problems. For the mixed integer case where the leader’s variables are continuous, our algorithm also detects whether the infimum cost fails to be attained, a difficulty that has been identified but not directly addressed in the literature. In this case, it yields a “better than fully polynomial time” approximation scheme with running time polynomial in the logarithm of the absolute precision. For the pure integer case where the leader’s variables are integer, and hence optimal solutions are guaranteed to exist, we present an algorithm which runs in polynomial time when the total number of variables is fixed.

Suggested Citation

  • M. Köppe & M. Queyranne & C. T. Ryan, 2010. "Parametric Integer Programming Algorithm for Bilevel Mixed Integer Programs," Journal of Optimization Theory and Applications, Springer, vol. 146(1), pages 137-150, July.
  • Handle: RePEc:spr:joptap:v:146:y:2010:i:1:d:10.1007_s10957-010-9668-3
    DOI: 10.1007/s10957-010-9668-3
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    References listed on IDEAS

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    Cited by:

    1. Faraz Salehi & S. Mohammad J. Mirzapour Al-E-Hashem & S. Mohammad Moattar Husseini & S. Hassan Ghodsypour, 2023. "A bi-level multi-follower optimization model for R&D project portfolio: an application to a pharmaceutical holding company," Annals of Operations Research, Springer, vol. 323(1), pages 331-360, April.
    2. Leonardo Lozano & J. Cole Smith, 2017. "A Value-Function-Based Exact Approach for the Bilevel Mixed-Integer Programming Problem," Operations Research, INFORMS, vol. 65(3), pages 768-786, June.
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    6. Richard Oberdieck & Nikolaos A. Diangelakis & Styliani Avraamidou & Efstratios N. Pistikopoulos, 2017. "On unbounded and binary parameters in multi-parametric programming: applications to mixed-integer bilevel optimization and duality theory," Journal of Global Optimization, Springer, vol. 69(3), pages 587-606, November.
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    10. Dajun Yue & Jiyao Gao & Bo Zeng & Fengqi You, 2019. "A projection-based reformulation and decomposition algorithm for global optimization of a class of mixed integer bilevel linear programs," Journal of Global Optimization, Springer, vol. 73(1), pages 27-57, January.
    11. Robbins, Matthew J. & Lunday, Brian J., 2016. "A bilevel formulation of the pediatric vaccine pricing problem," European Journal of Operational Research, Elsevier, vol. 248(2), pages 634-645.
    12. George Kozanidis & Eftychia Kostarelou, 2023. "An Exact Solution Algorithm for Integer Bilevel Programming with Application in Energy Market Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 573-607, May.
    13. Bo Zeng, 2020. "A Practical Scheme to Compute the Pessimistic Bilevel Optimization Problem," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1128-1142, October.
    14. Matteo Fischetti & Ivana Ljubić & Michele Monaci & Markus Sinnl, 2017. "A New General-Purpose Algorithm for Mixed-Integer Bilevel Linear Programs," Operations Research, INFORMS, vol. 65(6), pages 1615-1637, December.
    15. Fischetti, Matteo & Monaci, Michele & Sinnl, Markus, 2018. "A dynamic reformulation heuristic for Generalized Interdiction Problems," European Journal of Operational Research, Elsevier, vol. 267(1), pages 40-51.
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