An automated approach to generating efficient constraint solvers

Combinatorial problems appear in numerous settings, from timetabling to industrial design. Constraint solving aims to find solutions to such problems efficiently and automatically. Current constraint solvers are monolithic in design, accepting a broad range of problems. The cost of this convenience is a complex architecture, inhibiting efficiency, extensibility and scalability. Solver components are also tightly coupled with complex restrictions on their configuration, making automated generation of solvers difficult. We describe a novel, automated, model-driven approach to generating efficient solvers tailored to individual problems and present some results from applying the approach. The main contribution of this work is a solver generation framework called Dominion, which analyses a problem and, based on its characteristics, generates a solver using components chosen from a library. The key benefit of this approach is the ability to solve larger and more difficult problems as a result of applying finer-grained optimisations and using specialised techniques as required.