Conflict Optimization for Binary CSP Applied to Minimum Partition into Plane Subgraphs and Graph Coloring

Q2 Mathematics
Loïc Crombez, G. D. D. Fonseca, Florian Fontan, Y. Gérard, A. Gonzalez-Lorenzo, P. Lafourcade, Luc Libralesso, B. Momège, Jack Spalding-Jamieson, Brandon Zhang, D. Zheng
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引用次数: 3

Abstract

CG:SHOP is an annual geometric optimization challenge and the 2022 edition proposed the problem of coloring a certain geometric graph defined by line segments. Surprisingly, the top three teams used the same technique, called conflict optimization. This technique has been introduced in the 2021 edition of the challenge, to solve a coordinated motion planning problem. In this paper, we present the technique in the more general framework of binary constraint satisfaction problems (binary CSP). Then, the top three teams describe their different implementations of the same underlying strategy. We evaluate the performance of those implementations to vertex color not only geometric graphs, but also other types of graphs.
二元CSP冲突优化在平面子图最小分割及图着色中的应用
CG:SHOP是一个年度几何优化挑战,2022年版提出了一个由线段定义的几何图形上色的问题。令人惊讶的是,前三名团队使用了相同的技术,称为冲突优化。该技术已在2021年版的挑战中引入,以解决协调运动规划问题。在本文中,我们在更一般的二元约束满足问题(binary CSP)框架中给出了该技术。然后,排名前三的团队描述他们对相同底层策略的不同实现。我们不仅评估了这些实现对几何图形顶点着色的性能,还评估了其他类型图形顶点着色的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Experimental Algorithmics
Journal of Experimental Algorithmics Mathematics-Theoretical Computer Science
CiteScore
3.10
自引率
0.00%
发文量
29
期刊介绍: The ACM JEA is a high-quality, refereed, archival journal devoted to the study of discrete algorithms and data structures through a combination of experimentation and classical analysis and design techniques. It focuses on the following areas in algorithms and data structures: ■combinatorial optimization ■computational biology ■computational geometry ■graph manipulation ■graphics ■heuristics ■network design ■parallel processing ■routing and scheduling ■searching and sorting ■VLSI design
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