PyCSP3: Modeling Combinatorial Constrained Problems in Python

• URL: http://arxiv.org/abs/2009.00326v6
• Date: Thu, 29 Aug 2024 10:12:28 GMT
• Title: PyCSP3: Modeling Combinatorial Constrained Problems in Python
• Authors: Christophe Lecoutre, Nicolas Szczepanski,
• Abstract summary: PyCSP$3$ is a Python library that allows us to write models of constrained problems in a declarative manner. In this document, you will find all that you need to know about PyCSP$3$, with more than 50 illustrative models.
• Score: 1.9336815376402718
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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