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On the solution of convex bilevel optimization problems

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  • S. Dempe
  • S. Franke

Abstract

An algorithm is presented for solving bilevel optimization problems with fully convex lower level problems. Convergence to a local optimal solution is shown under certain weak assumptions. This algorithm uses the optimal value transformation of the problem. Transformation of the bilevel optimization problem using the Fritz-John necessary optimality conditions applied to the lower level problem is shown to exhibit almost the same difficulties for solving the problem as the use of the Karush–Kuhn–Tucker conditions. Copyright Springer Science+Business Media New York 2016

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  • S. Dempe & S. Franke, 2016. "On the solution of convex bilevel optimization problems," Computational Optimization and Applications, Springer, vol. 63(3), pages 685-703, April.
    • Handle: RePEc:spr:coopap:v:63:y:2016:i:3:p:685-703
    DOI: 10.1007/s10589-015-9795-8
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    Cited by:

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    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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    5. Xiao, Helu & Ren, Tiantian & Zhou, Zhongbao & Liu, Wenbin, 2021. "Parameter uncertainty in estimation of portfolio efficiency: Evidence from an interval diversification-consistent DEA approach," Omega, Elsevier, vol. 103(C).
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    7. Claudio Contardo & Jorge A. Sefair, 2022. "A Progressive Approximation Approach for the Exact Solution of Sparse Large-Scale Binary Interdiction Games," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 890-908, March.
    8. Lorenzo Lampariello & Simone Sagratella, 2020. "Numerically tractable optimistic bilevel problems," Computational Optimization and Applications, Springer, vol. 76(2), pages 277-303, June.
    9. Stephan Dempe & Felix Harder & Patrick Mehlitz & Gerd Wachsmuth, 2019. "Solving inverse optimal control problems via value functions to global optimality," Journal of Global Optimization, Springer, vol. 74(2), pages 297-325, June.

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