Core-Guided Model Reformulation

Kevin Leo, Graeme Gange, Maria Garcia de la Banda, Mark Wallace

Abstract: Constraint propagation and SAT solvers often underperform when dealing with optimisation problems that have an additive (or separable) objective function. The core-guided search introduced by MaxSAT solvers can overcome this weakness by detecting and exploiting cores: subsets of the objective components that cannot collectively take their lower bounds. This paper shows how to use the information collected during core-guided search, to reformulate the objective function for an entire class of problems (those captured by the problem model). The resulting (currently manual) method is examined on several case studies, with very promising results.