6 research outputs found
SUNNY-CP and the MiniZinc Challenge
In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
SUNNY with Algorithm Configuration
International audienceThe SUNNY algorithm is a portfolio technique originally tailored for Constraint Satisfaction Problems (CSPs). SUNNY allows to select a set of solvers to be run on a given CSP, and was proven to be effective in the MiniZinc Challenge, i.e., the yearly international competition for CP solvers. In 2015, SUNNY was compared with other solver selectors in the first ICON Challenge on algorithm selection with less satisfactory performance. In this paper we briefly describe the new version of the SUNNY approach for algorithm selection, that was submitted to the first Open Algorithm Selection Challenge
SUNNY-CP: a Portfolio Solver for Constraint Programming
In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the last MiniZinc Challenge —the annual international competition for CP solvers— where it won a gold medal
sunny-as2: Enhancing SUNNY for Algorithm Selection
SUNNY is an Algorithm Selection (AS) technique originally tailored for
Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of
solvers, a subset of solvers to be run on a given CP problem. This approach has
proved to be effective for CP problems, and its parallel version won many gold
medals in the Open category of the MiniZinc Challenge -- the yearly
international competition for CP solvers. In 2015, the ASlib benchmarks were
released for comparing AS systems coming from disparate fields (e.g., ASP, QBF,
and SAT) and SUNNY was extended to deal with generic AS problems. This led to
the development of sunny-as2, an algorithm selector based on SUNNY for ASlib
scenarios. A preliminary version of sunny-as2 was submitted to the Open
Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the
best approach for the runtime minimization of decision problems. In this work,
we present the technical advancements of sunny-as2, including: (i)
wrapper-based feature selection; (ii) a training approach combining feature
selection and neighbourhood size configuration; (iii) the application of nested
cross-validation. We show how sunny-as2 performance varies depending on the
considered AS scenarios, and we discuss its strengths and weaknesses. Finally,
we also show how sunny-as2 improves on its preliminary version submitted to
OASC