MiniZinc

MiniZinc is a medium-level constraint modelling language. It is high-level enough to express most constraint problems easily, but low-level enough that it can be mapped onto existing solvers easily and consistently. It is a subset of the higher-level language Zinc. We hope it will be adopted as a standard by the Constraint Programming community.


References in zbMATH (referenced in 56 articles , 2 standard articles )

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  1. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: SUNNY-CP and the MiniZinc challenge (2018)
  2. Enright, Jessica; Meeks, Kitty: Deleting edges to restrict the size of an epidemic: a new application for treewidth (2018)
  3. Kreter, Stefan; Schutt, Andreas; Stuckey, Peter J.; Zimmermann, Jürgen: Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems (2018)
  4. Amadini, Roberto; Flener, Pierre; Pearson, Justin; Scott, Joseph D.; Stuckey, Peter J.; Tack, Guido: Minizinc with strings (2017)
  5. Battistutta, Michele; Schaerf, Andrea; Urli, Tommaso: Feature-based tuning of single-stage simulated annealing for examination timetabling (2017)
  6. Carlsson, Mats; Johansson, Mikael; Larson, Jeffrey: Scheduling double round-robin tournaments with divisional play using constraint programming (2017)
  7. Dekker, Jip J.; Björdal, Gustav; Carlsson, Mats; Flener, Pierre; Monette, Jean-Noël: Auto-tabling for subproblem presolving in minizinc (2017)
  8. Guns, Tias; Dries, Anton; Nijssen, Siegfried; Tack, Guido; De Raedt, Luc: MiningZinc: A declarative framework for constraint-based mining (2017)
  9. Hebrard, Emmanuel; Siala, Mohamed: Explanation-based weighted degree (2017)
  10. Hemmi, David; Tack, Guido; Wallace, Mark: Scenario-based learning for stochastic combinatorial optimisation (2017)
  11. Kreter, Stefan; Schutt, Andreas; Stuckey, Peter J.: Using constraint programming for solving RCPSP/MAX-cal (2017)
  12. Leo, Kevin; Tack, Guido: Debugging unsatisfiable constraint models (2017)
  13. Michel, L.; Van Hentenryck, P.: A microkernel architecture for constraint programming (2017)
  14. Passerini, Andrea (ed.); Tack, Guido (ed.); Guns, Tias (ed.): Introduction to the special issue on combining constraint solving with mining and learning (2017)
  15. Rusu, Irena: Graph matching problems and the NP-hardness of sortedness constraints (2017)
  16. Scott, Joseph D.; Flener, Pierre; Pearson, Justin; Schulte, Christian: Design and implementation of bounded-length sequence variables (2017)
  17. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  18. Hurley, Barry; O’Sullivan, Barry; Allouche, David; Katsirelos, George; Schiex, Thomas; Zytnicki, Matthias; de Givry, Simon: Multi-language evaluation of exact solvers in graphical model discrete optimization (2016)
  19. Salvagnin, Domenico: Detecting semantic groups in MIP models (2016)
  20. van Iersel, Leo; Kelk, Steven; Lekić, Nela; Linz, Simone: Satisfying ternary permutation constraints by multiple linear orders or phylogenetic trees (2016)

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