@inproceedings{
dumouchelle2024neurbilo,
title={Neur2Bi{LO}: Neural Bilevel Optimization},
author={Justin Dumouchelle and Esther Julien and Jannis Kurtz and Elias Boutros Khalil},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=esVleaqkRc}
}
Bilevel optimization deals with nested problems in which *leader* takes the first decision to minimize their objective function while accounting for a *follower*'s best-response reaction. Constrained bilevel problems with integer variables are particularly notorious for their hardness. While exact solvers have been proposed for mixed-integer *linear* bilevel optimization, they tend to scale poorly with problem size and are hard to generalize to the non-linear case. On the other hand, problem-specific algorithms (exact and heuristic) are limited in scope. Under a data-driven setting in which similar instances of a bilevel problem are solved routinely, our proposed framework, Neur2BiLO, embeds a neural network approximation of the leader's or follower's value function, trained via supervised regression, into an easy-to-solve mixed-integer program.  Neur2BiLO serves as a heuristic that produces high-quality solutions extremely fast for four applications with linear and non-linear objectives and pure and mixed-integer variables.
Neur2BiLO: Neural Bilevel Optimization
Justin Dumouchelle Esther Julien Jannis Kurtz Elias Boutros Khalil