AIJun 26, 2017

SUNNY-CP and the MiniZinc Challenge

arXiv:1706.08627v32 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the problem of improving solver performance in constraint programming for researchers and practitioners, though it appears incremental as it builds on existing portfolio approaches.

The paper presents SUNNY-CP, a portfolio solver for constraint programming that combines multiple solvers to enhance performance, particularly on multicore systems, and reports its success in winning two gold medals at the MiniZinc Challenge in 2015 and 2016.

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)

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