Titlebook: Bilevel Programming Problems; Theory, Algorithms a Stephan Dempe,Vyacheslav Kalashnikov,Nataliya Kala Book 2015 Springer-Verlag Berlin Heid

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MARK A. MASLIN,GEORGE E.A. SWANNl optimization problem in the second section. Here, the lower level problem is a multicommodity network flow problem with parametric objective function. An algorithm implementing the filling functions technique is presented. At the end results of a numerical realization of this algorithm are reported.

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https://doi.org/10.1007/978-3-030-33652-3ution algorithms for the linear bilevel optimization problem are formulated either using regions of stability for solutions of the lower level problem or the optimal value reformulation of the bilevel problem.

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Linear Bilevel Optimization Problem,ution algorithms for the linear bilevel optimization problem are formulated either using regions of stability for solutions of the lower level problem or the optimal value reformulation of the bilevel problem.

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Book 2015t describes recent applications in energy problems, such as the stochastic bilevel optimization approaches used in the natural gas industry. New algorithms for solving linear and mixed-integer bilevel programming problems are presented and explained..

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Cyril Chelle-Michou,Urs Schalteggern problem with one Boolean variable in the lower level problem. We report related models, arising in applications, if the Boolean variable is shifted to the upper level problem or when stochastic data are implemented. Solution algorithms and numerical results are presented.

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MARK A. MASLIN,GEORGE E.A. SWANN a parametric Nash equilibrium between two followers is considered in the lower level. The cases of linear and nonlinear bilevel optimization are described in detail. An example is used to illustrate this idea.

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