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Graph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Bilevel mixed-integer linear optimization problems model hierarchical decision processes in which a leader anticipates the optimal response of a follower. Although expressive, these problems are computationally challenging because lower-level optimality is embedded in the leader's feasible region. Value-function reformulations replace the nested follower optimization with a constraint involving the follower's optimal value, but evaluating this value function exactly can itself be expensive. This paper introduces Graph4BiLO, a graph neural network (GNN) approach for learning bilevel value funct

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