论文标题
PYCSP3：Python中建模的组合约束问题
PyCSP3: Modeling Combinatorial Constrained Problems in Python
论文作者
论文摘要
在本文档中，我们介绍了PYCSP $ 3 $，这是一个python库，它允许我们以声明性的方式编写组合受限问题的模型。当前，使用PYCSP $ 3 $，您可以编写约束满意度和优化问题的模型。更具体地说，您可以构建CSP（约束满意度问题）和COP（约束优化问题）模型。重要的是，建模和求解阶段之间存在完全的分离：您编写模型，将其编译（同时提供一些数据）以生成XCSP $ 3 $实例（文件），然后通过约束求解器解决该问题实例。您还可以直接以Pycsp $ 3 $进行求解程序，可能会执行增量解决策略。在本文档中，您将发现有关PYCSP $ 3 $的所有知识，其中有50多个说明性模型。
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.
