Gecode is a toolkit for developing constraint-based systems and applications. Gecode provides a constraint solver with state-of-the-art performance while being modular and extensible. Gecode is: open. Gecode is radically open for programming: it can be easily interfaced to other systems. It supports the programming of new constraints, branching strategies, and search engines. New variable domains can be programmed at the same level of efficiency as the variables that come predefined with Gecode. comprehensive. Gecode has a comprehensive set of features: constraints over integers, Booleans, sets, and floats (it implements more than 70 constraints from the Global Constraint Catalog and many more on top); C++ modeling layer; advanced branching heuristics (accumulated failure count, activity); many search engines (parallel, interactive graphical, restarts); automatic symmetry breaking (LDSB); MiniZinc support; and many more. efficient Gecode offers excellent performance with respect to both runtime and memory usage. It won all gold medals in all categories at the MiniZinc Challenges from 2008 to 2012: 2012, 2011, 2010, 2009, and 2008. documented Gecode comes with both complete tutorial (more than 500 pages) and complete reference documentation that allows users to focus on different modeling and programming tasks with Gecode. free Gecode is distributed under the MIT license and is listed as free software by the FSF. All of its parts including reference documentation, implementations of global constraints, and examples are available as source code for download. portable Gecode is implemented in C++ that carefully follows the C++ standard. It can be compiled with modern C++ compilers and runs on a wide range of machines (including 64bit machines). parallel Gecode complies with reality in that it exploits the multiple cores of today’s commodity hardware for parallel search, giving an already efficient base system an additional edge. tested Gecode uses a test suite with almost 50000 different test cases reaching a test coverage close to 100%.

References in zbMATH (referenced in 83 articles )

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  1. Müller, David; Müller, Marcus G.; Kress, Dominik; Pesch, Erwin: An algorithm selection approach for the flexible job shop scheduling problem: choosing constraint programming solvers through machine learning (2022)
  2. Åstrand, Max; Johansson, Mikael; Feyzmahdavian, Hamid Reza: Short-term scheduling of production fleets in underground mines using CP-based LNS (2021)
  3. Chalumeau, Félix; Coulon, Ilan; Cappart, Quentin; Rousseau, Louis-Martin: SeaPearl: a constraint programming solver guided by reinforcement learning (2021)
  4. Geibinger, Tobias; Kletzander, Lucas; Krainz, Matthias; Mischek, Florian; Musliu, Nysret; Winter, Felix: Physician scheduling during a pandemic (2021)
  5. Amadini, Roberto; Gange, Graeme; Stuckey, Peter J.: Dashed strings for string constraint solving (2020)
  6. Åstrand, Max; Johansson, Mikael; Zanarini, Alessandro: Underground mine scheduling of mobile machines using constraint programming and large neighborhood search (2020)
  7. Bădică, Amelia; Bădică, Costin; Ivanović, Mirjana: Block structured scheduling using constraint logic programming (2020)
  8. Bădică, Amelia; Bădică, Costin; Logofătu, Doina; Buligiu, Ion; Ciora, Liviu: Modeling block structured project scheduling with resource constraints (2020)
  9. Benadè, Gerdus; Hooker, John N.: Optimization bounds from the branching dual (2020)
  10. Gerault, David; Lafourcade, Pascal; Minier, Marine; Solnon, Christine: Computing AES related-key differential characteristics with constraint programming (2020)
  11. Maximiliano Cristiá, Andrea Fois, Gianfranco Rossi: Declarative Programming with Intensional Sets in Java Using JSetL (2020) arXiv
  12. Omrani, Mohamed Amine; Naanaa, Wady: Constraints for generating graphs with imposed and forbidden patterns: an application to molecular graphs (2020)
  13. Talbot, Pierre; Monfroy, Éric; Truchet, Charlotte: Modular constraint solver cooperation via abstract interpretation (2020)
  14. Amadini, Roberto; Andrlon, Mak; Gange, Graeme; Schachte, Peter; Søndergaard, Harald; Stuckey, Peter J.: Constraint programming for dynamic symbolic execution of JavaScript (2019)
  15. Björdal, Gustav; Flener, Pierre; Pearson, Justin: Generating compound moves in local search by hybridisation with complete search (2019)
  16. Geibinger, Tobias; Mischek, Florian; Musliu, Nysret: Investigating constraint programming for real world industrial test laboratory scheduling (2019)
  17. Kadıoğlu, Serdar: Core group placement: allocation and provisioning of heterogeneous resources (2019)
  18. Amadini, Roberto; Gange, Graeme; Stuckey, Peter J.: Propagating \textsclex, \textscfindand \textscreplacewith dashed strings (2018)
  19. Enright, Jessica; Meeks, Kitty: Deleting edges to restrict the size of an epidemic: a new application for treewidth (2018)
  20. Fischetti, Matteo; Monaci, Michele; Salvagnin, Domenico: SelfSplit parallelization for mixed-integer linear programming (2018)

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