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PyCSP3: Modeling Combinatorial Constrained Problems in Python

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arxiv 2009.00326 v6 pith:BMRQIGCR submitted 2020-09-01 cs.AI

classification cs.AI
keywords constraintmodelspycspproblemproblemssolvingwritecombinatorial
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abstract

In this document, we introduce PyCSP$3$, a Python library that allows us to write models of combinatorial constrained problems in a declarative manner. Currently, with PyCSP$3$, you can write models of constraint satisfaction and optimization problems. More specifically, you can build CSP (Constraint Satisfaction Problem) and COP (Constraint Optimization Problem) models. Importantly, there is a complete separation between the modeling and solving phases: you write a model, you compile it (while providing some data) in order to generate an XCSP$3$ instance (file), and you solve that problem instance by means of a constraint solver. You can also directly pilot the solving procedure in PyCSP$3$, possibly conducting an incremental solving strategy. In this document, you will find all that you need to know about PyCSP$3$, with more than 50 illustrative models.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 8 citations worldwide. Full citation record

  1. Proceedings of the 2024 XCSP3 Competition

    cs.AI 2024-11 conditional novelty 4.0 of 10

    The 2024 XCSP3 competition proceedings document the benchmark suite, solver descriptions, and final rankings across six constraint-solving tracks.

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