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# PyCSP3: Modeling Combinatorial Constrained Problems in Python

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.
Document type :
Reports

https://hal-univ-artois.archives-ouvertes.fr/hal-03701203
Contributor : Daniel Le Berre Connect in order to contact the contributor
Submitted on : Tuesday, June 21, 2022 - 5:49:01 PM
Last modification on : Friday, June 24, 2022 - 3:32:41 AM

### Identifiers

• HAL Id : hal-03701203, version 1
• ARXIV : 2009.00326

### Citation

Christophe Lecoutre, Nicolas Szczepanski. PyCSP3: Modeling Combinatorial Constrained Problems in Python. [Technical Report] arXiv. 2021. ⟨hal-03701203⟩

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