Linear Decision Tree Policies for Integer Linear Programs

Abstract

We study optimal decision policies, represented as linear decision trees, for integer linear programs with a fixed feasible set and varying cost vectors. Once synthesized for a given feasible set, they return an optimal solution for any queried cost vector through a sequence of linear tests. We show that there exists a policy performing this operation in a polynomial number of arithmetic operations in the worst case. In contrast, deciding whether there exists an exact policy with a prescribed maximum number of leaves is $\hat{I}{\pounds }_2^p$-complete. Alongside these theoretical results, we develop a practical construction framework to synthesize policies within a specific subclass of linear decision trees. Our computational experiments show that, although policy synthesis can be time-intensive, it allows one to retrieve optimal solutions orders of magnitude faster than classical and specialized solution methods on repeated queries. Overall, this paradigm provides a different perspective on the solution of integer linear programs and offers a principled offline-online approach for repeated optimization.