For building MILP solvers and learned optimization policies, GraphBU generates synthetic instances that preserve the graph structure and feasibility of real problems better than existing methods, improving downstream model training by ~8%.
GraphBU is a method for generating realistic MILP instances by treating local subproblems and their connections as fundamental units. Unlike existing generators that use templates or statistics, it explicitly preserves how different parts of an optimization problem couple together, maintaining structural properties that solvers and learned policies depend on.