Twin-width is a recently formulated graph and matrix invariant that intuitively quantifies how far a graph is from having the structural simplicity of a co-graph. Since its introduction in 2020, twin-width has received increasing attention and has driven research leading to notable advances in algorithmic fields, including graph theory and combinatorics. The 2023 edition of the Parameterized Algorithms and Computational Experiments (PACE) Challenge aimed to fulfill the need for a diverse and consistent public benchmark encompassing various graph structures, while also collecting state-of-the-art heuristic and exact approaches to the problem. In this paper, we propose two algorithms for efficiently computing the twin-width of graphs with arbitrary structures, comprising one exact and one heuristic approach. The proposed solutions performed strongly in the competition, with the exact algorithm achieving the best student result and ranking fourth overall. We release our source code publicly to enable practical applications of our work and support further research.
翻译:双宽度是最近提出的图和矩阵不变量,直观上量化了图与具有共图结构简单性的距离。自2020年引入以来,双宽度受到越来越多的关注,并推动了图论和组合数学等算法领域的显著进展。2023年参数化算法与计算实验(PACE)挑战赛旨在满足对涵盖多种图结构的多样化且一致的公共基准的需求,同时收集该问题的最先进启发式和精确方法。本文中,我们提出了两种算法,用于高效计算任意结构图的双宽度,包括一种精确方法和一种启发式方法。所提出的解决方案在竞赛中表现优异,其中精确算法取得了最佳学生成绩,并在总排名中位列第四。我们公开发布源代码,以实现我们工作的实际应用并支持进一步研究。