Enhanced Inference for the Market Split Problem

Inference in constraint programming is usually based on the deductions generated by individual constraints which are then communicated to other constraints through domain filtering. Frequently we find that this is a too coarse-grained form of communication since constraints could exchange more powerful forms of deductions that could help reduce the search effort. In this paper we propose a particular technique for enhancing inference in constraint programming, by generating deductions that involve tighter interleaving of constraints. We apply our method to the market split problem and obtain massive speed-ups which brings a new order of market split problems into the realm of solvability by means of constraint programming.