Open AccessThe MiniZinc Challenge 2008–2013
Author(s) -
Stuckey Peter J.,
Feydy Thibaut,
Schutt Andreas,
Tack Guido,
Fischer Julien
Publication year - 2014
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v35i2.2539
Subject(s) - solver , computer science , constraint satisfaction problem , constraint programming , constraint satisfaction , modulo , set (abstract data type) , programming language , integer programming , modeling language , satisfiability modulo theories , theoretical computer science , mathematical optimization , artificial intelligence , mathematics , algorithm , discrete mathematics , stochastic programming , software , probabilistic logic
MiniZinc is a solver‐agnostic modeling language for defining and solving combinatorial satisfaction and optimization problems. MiniZinc provides a solver‐independent modeling language that is now supported by constraint‐programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Every year since 2008 we have run the MiniZinc Challenge, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models. Here we report on what we have learned from running the competition for 6 years.