PyCSP3: Modeling Combinatorial Constrained Problems in Python

arXiv:2009.00326 · cs.AI · Submitted 2020-09-01 · Read on arXiv

cs.AI

Submitted: 2020-09-01

Updated: 2026-09-01

Comments: 191 pages

Code: https://github.com/xcsp3team/pycsp3

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: In this document, we introduce PyCSP 3, a Python library that allows us to write models of combinatorial constrained problems in a declarative manner.

Terminology

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.

Sources

Related papers