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PyCSP3: Modeling Combinatorial Constrained Problems in Python

arXiv 2009.00326 ↗First posted 1 Sep 2020cs.AIcited by 9

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Abstract (arXiv)

In this document, we introduce PyCSP3, a Python library that allows us to write models of combinatorial constrained problems in a declarative manner. Currently, with PyCSP3, 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 XCSP3 instance (file), and you solve that problem instance by means of a constraint solver. You can also directly pilot the solving procedure in PyCSP3, possibly conducting an incremental solving strategy. In this document, you will find all that you need to know about PyCSP3, with more than 50 illustrative models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.

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Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 2; 2026: 3.

9 papers.