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 simple and declarative way. 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 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. In this document, you will find all that you need to know about PyCSP 3 , with more than 40 illustrative models.

Multi-objective Finite-Domain Constraint-Based Forest Management

“…Many successful approaches have been followed to solve CSPs, namely systematic search, in which variables see their domain progressively restricted and each such step triggers the reduction of the domains of related variables, as dictated by the consistency policy-these are in general designated as propagation-based constraint solvers and there are several ones, some being presented as libraries for use within a general-purpose programming language, such as Gecode [ 12] or Choco [ 13]. Others offer a domain-specific language (DSL) which may be used to model a problem and provide it as input to different solvers; such is the case for instance for MiniZinc [ 14] or PyCSP3 [ 15].…”