Guido Tack

Guido Tack

Monash University
Design and implementation of MiniZinc, model analysis and transformation, generating efficient FlatZinc for a diverse range of solvers, making MiniZinc easy to use.

Publications on MiniZinc by Guido Tack

  • Maxim Shishmarev, Christopher Mears, Guido Tack, Maria Garcia de la Banda. Learning from Learning Solvers. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 455-472, Springer, 2016.
  • Maxim Shishmarev, Christopher Mears, Guido Tack, and Maria Garcia de la Banda. Visual search tree profiling. In Constraints 21(1), pp. 77–94, 2016.
  • Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016.
  • Andrea Rendl, Tias Guns, Peter J. Stuckey, and Guido Tack. MiniSearch: a solver-independent meta-search language for MiniZinc. In Gilles Pesant, editor, CP, p. 376-392. Springer, 2015.
  • Kevin Leo and Guido Tack. Multi-pass high-level presolving. In Qiang Yang and Michael Wooldridge, editors, IJCAI, pp. 346–352. AAAI Press, 2015.
  • Tias Guns, Anton Dries, Siegfried Nijssen, Guido Tack, and Luc De Raedt. MiningZinc: A declarative framework for constraint-based mining. In Artificial Intelligence, 2015.
  • Andrea Rendl, Guido Tack, and Peter J. Stuckey. Stochastic MiniZinc. In B. O’Sullivan, editor, Proceedings of the 20th International Conference on Principles and Practice of Constraint Programming, volume 8656 of LNCS, pages 636–645. Springer, 2014.
  • Christopher Mears, Andreas Schutt, Peter J. Stuckey, Guido Tack, Kim Marriott, and Mark Wallace. Modelling with option types in MiniZinc. In Proceedings of the 11th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, number 8451 in LNCS, pages 88–103. Springer, 2014.
  • Peter J. Stuckey, Thibaut Feydy, Andreas Schutt, Guido Tack, and Julien Fischer. The MiniZinc challenge 2008-2013. AI Magazine, 35(2):55–60, 2014, 2014. more...
    MiniZinc is a solver agnostic modeling language for defining and solver combinatorial satisfaction and optimization problems. MiniZinc provides a solver independent modeling language which is now supported by constraint programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Since 2008 we have run the MiniZinc challenge every year, 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 learnt from running the competition for 6 years.
  • Peter J. Stuckey and Guido Tack. MiniZinc with functions. In Proceedings of the 10th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, number 7874 in LNCS, pages 268–283. Springer, 2013.
  • T. Schrijvers, G. Tack, P. Wuille, H. Samulowitz, and P.J. Stuckey. Search combinators. Constraints, 18(2):269–305, 2013.
  • Kevin Leo, Christopher Mears, Guido Tack, Maria Garcia de la Banda. Globalizing Constraint Models. Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Uppsala, Sweden, September 16-20, 2013, Proceedings, pp. 432-447, Springer, 2013.
  • Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen and Luc De Raedt. MiningZinc: A Modeling Language for Constraint-based Mining. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1365-1372, 2013.
  • T. Schrijvers, G. Tack, P. Wuille, H. Samulowitz, and P.J. Stuckey. Search combinators. In J.H.M. Lee, editor, Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming, volume 6876 of LNCS, pages 774–788. Springer, 2011.
  • N. Nethercote, P.J. Stuckey, R. Becket, S. Brand, G.J. Duck, and G. Tack. MiniZinc: Towards a standard CP modelling language. In C. Bessiere, editor, Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, volume 4741 of LNCS, pages 529–543. Springer, 2007. more...
    There is no standard modelling language for constraint programming (CP) problems. Most solvers have their own modelling language. This makes it difficult for modellers to experiment with different solvers for a problem. In this paper we present MiniZinc, a simple but expressive CP modelling language which is suitable for modelling problems for a range of solvers and provides a reasonable compromise between many design possibilities. Equally importantly, we also propose a low-level solver-input language called FlatZinc, and a straightforward translation from MiniZinc to FlatZinc that preserves all solver-supported global constraints. This lets a solver writer support MiniZinc with a minimum of effort - they only need to provide a simple FlatZinc front-end to their solver, and then combine it with an existing MiniZinc-to-FlatZinc translator. Such a front-end may then serve as a stepping stone towards a full MiniZinc implementation that is more tailored to the particular solver. A standard language for modelling CP problems will encourage experimentation with and comparisons between different solvers. Although MiniZinc is not perfect - no standard modelling language will be - we believe its simplicity, expressiveness, and ease of implementation make it a practical choice for a standard language.