Graph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization
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 functions from variable--constraint graph representations. In contrast to fixed-length multilayer perc...
| Authors | Jessica D. Elrefaei, Kaixun Hua, Seungbae Kim et al. |
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| Primary category | cs.LG |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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