• Corpus ID: 222310722

Learning Active Constraints to Efficiently Solve Bilevel Problems.

@article{Prat2020LearningAC,
  title={Learning Active Constraints to Efficiently Solve Bilevel Problems.},
  author={El'ea Prat and Spyros Chatzivasileiadis},
  journal={arXiv: Optimization and Control},
  year={2020}
}
Bilevel programming can be used to formulate many engineering and economics problems. However, solving such problems is hard, which impedes their implementation in real-life. In this paper, we propose to address this tractability challenge using machine learning classification techniques to learn the active constraints of the lower-level problem, in order to reduce it to those constraints only. Unlike in the commonly used reformulation of bilevel programs with the Karush-Kuhn-Tucker conditions… 

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