4 research outputs found
SUNNY: a Lazy Portfolio Approach for Constraint 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
Portfolio Approaches for Constraint Optimization Problems
International audienceWithin the Constraints Satisfiability Problems (CSP) context, a methodology that has proved to be particularly performant consists in using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations has been done in the world of Constraint Optimization Problems (COP). In this work, we provide a generalization to COP as well as an empirical evaluation of different state of the art existing CSP portfolio approaches properly adapted to deal with COP. Experimental results confirm the effectiveness of portfolios even in the optimization field, and could give rise to some interesting future research