Network Relaxations for Discrete Bilevel Optimization under Linear Interactions

TL;DR

This paper introduces a novel network-flow based relaxation technique for discrete bilevel programs with linear interaction constraints, improving solution efficiency and scalability.

Contribution

It develops a new single-level reformulation using decision diagrams and state projections, along with symmetry reduction and parameterized relaxations for large networks.

Findings

Relaxations significantly reduce runtimes in benchmark problems.

The approach improves the ability to solve larger instances.

Relaxation quality correlates with interaction matrix properties.

Abstract

We investigate relaxations for a class of discrete bilevel programs where the interaction constraints linking the leader and the follower are linear. Our approach reformulates the upper-level optimality constraints by projecting the leader's decisions onto vectors that map to distinct follower solution values, each referred to as a state. Based on such a state representation, we develop a network-flow linear program via a decision diagram that captures the convex hull of the follower's value function graph, leading to a new single-level reformulation of the bilevel problem. We also present a reduction procedure that exploits symmetry to identify the reformulation of minimal size. For large networks, we introduce parameterized relaxations that aggregate states by considering tractable hyperrectangles based on lower and upper bounds associated with the interaction constraints, and can be integrated into existing mixed-integer bilevel linear programming (MIBLP) solvers. Numerical experiments suggest that the new relaxations, whether used within a simple cutting-plane procedure or integrated into state-of-the-art MIBLP solvers, significantly reduce runtimes or solve additional benchmark instances. Our findings also highlight the correlation between the quality of relaxations and the properties of the interaction matrix, underscoring the potential of our approach in enhancing solution methods for structured bilevel optimization instances.