PyCSP3: Modeling Combinatorial Constrained Problems in Python
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2020
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916374087991296 |
|---|---|
| author | Lecoutre, Christophe Szczepanski, Nicolas |
| author_facet | Lecoutre, Christophe Szczepanski, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_00326 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | PyCSP3: Modeling Combinatorial Constrained Problems in Python Lecoutre, Christophe Szczepanski, Nicolas Artificial Intelligence 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. |
| title | PyCSP3: Modeling Combinatorial Constrained Problems in Python |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2009.00326 |