SUNNY: a Lazy Portfolio Approach for Constraint Solving
This work addresses the need for better constraint solving tools for researchers and practitioners, but it is incremental as it builds on existing portfolio approaches.
The paper tackles the problem of improving constraint solving by proposing SUNNY, a lazy portfolio algorithm that schedules solvers without learning an explicit model, and empirical tests on MiniZinc benchmarks show its performance aligns with predictions, achieving effective results in CSP solving.
*** To appear in Theory and Practice of Logic Programming (TPLP) *** Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute --- without learning an explicit model --- a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of SUNNY conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.