PyCSP3: Modeling Combinatorial Constrained Problems in Python
September 01, 2020 Β· Declared Dead Β· π arXiv.org
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Authors
Christophe Lecoutre, Nicolas Szczepanski
arXiv ID
2009.00326
Category
cs.AI: Artificial Intelligence
Citations
22
Venue
arXiv.org
Last Checked
4 months ago
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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