Year: 2025
Type: article
Abstract: Constraint satisfaction problems (CSPs) are a class of problems that are ubiquitous in science and engineering. They feature a collection of constraints specified over subsets of variables. A CSP can be solved either directly or by reducing it to other problems. This paper introduces the Julia ecosystem for solving and analyzing CSPs with a focus on the programming practices. We introduce some important CSPs and show how these problems are reduced to each other. We also show how to transform CSPs into tensor networks, how to optimize the tensor network contraction orders, and how to extract the solution space properties by contracting the tensor networks with generic element types. Examples are given, which include computing the entropy constant, analyzing the overlap gap property, and the reduction between CSPs.
Source: Chinese Physics B
Authors: Xuanzhao 煊钊 Gao 高, Xiaofeng 晓锋 Li 李, Jinguo 金国 Liu 刘
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408342039