Titlebook: Bilevel Optimization; Advances and Next Ch Stephan Dempe,Alain Zemkoho Book 2020 Springer Nature Switzerland AG 2020 Algorithms for linear

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Regularization and Approximation Methods in Stackelberg Games and Bilevel Optimization different types of mathematical problems. We present formulations and solution concepts for such problems, together with their possible roles in bilevel optimization, and we illustrate the crucial issues concerning these solution concepts. Then, we discuss which of these issues can be positively or

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Applications of Bilevel Optimization in Energy and Electricity Markets centralized planners and has become the responsibility of many different entities such as market operators, private generation companies, transmission system operators and many more. The interaction and sequence in which these entities make decisions in liberalized market frameworks have led to a r

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Bilevel Optimization of Regularization Hyperparameters in Machine Learning Needless to say, prediction performance of ML models significantly relies on the choice of hyperparameters. Hence, establishing methodology for properly tuning hyperparameters has been recognized as one of the most crucial matters in ML. In this chapter, we introduce the role of bilevel optimizatio

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Bilevel Optimization and Variational Analysis bilevel optimization with Lipschitzian data. We mainly concentrate on optimistic models, although the developed machinery also applies to pessimistic versions. Some open problems are posed and discussed.

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Constraint Qualifications and Optimality Conditions in Bilevel Optimizationqualifications in terms of problem data and applicable optimality conditions. For the bilevel program with convex lower level program we discuss drawbacks of reformulating a bilevel programming problem by the mathematical program with complementarity constraints and present a new sharp necessary opt

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MPEC Methods for Bilevel Optimization Problemssfies a constraint qualification for all possible upper-level decisions. Replacing the lower-level optimization problem by its first-order conditions results in a mathematical program with equilibrium constraints (MPEC) that needs to be solved. We review the relationship between the MPEC and bilevel

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