MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers

arXiv (Cornell University)

Abstract

Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-crafted heuristics, largely ignoring common patterns within a given instance distribution of the problem of interest. Here, we propose MIP-GNN, a general framework for enhancing such solvers with data-driven insights. By encoding the variable-constraint interactions of a given mixed-integer linear program (MILP) as a bipartite graph, we leverage state-of-the-art graph neural network architectures to predict variable biases, i.e., component-wise averages of (near) optimal solutions, indicating how likely a variable will be set to 0 or 1 in (near) optimal solutions of binary MILPs. In turn, the predicted biases stemming from a single, once-trained model are used to guide the solver, replacing heuristic components. We integrate MIP-GNN into a state-of-the-art MIP solver, applying it to tasks such as node selection and warm-starting, showing significant improvements compared to the default setting of the solver on two classes of challenging binary MILPs.

Authors 2

1. Elias B. Khalil

   University of Toronto

   Affiliation as printed

   Department of Mechanical & Industrial Engineering , University of Toronto

   Scale AI Research Chair in Data-Driven Algorithms for Modern Supply Chains

2. Andrea Lodi

   Cornell University · Jacobs Institute

   Affiliation as printed

   CERC , Polytechnique Montréal and Jacobs Technion-Cornell Institute , Cornell Tech and Technion -IIT