通过矩阵乘法开辟道路:非最短诱导路径的更快算法

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Yung-Chung Chiu, Hsueh-I Lu
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引用次数: 0

摘要

对于 n 个顶点图 G 的顶点 u 和 v,G 的 uv 轨迹是 G 的诱导 uv 路径,它不是 G 的最短 uv 路径。Berger、Seymour 和 Spirkl [Discrete Mathematics 2021]给出了以前已知的唯一多项式时间算法,运行时间为 O(n18),可以输出 G 的 uv 轨迹或确保 G 不允许 uv 轨迹。我们将复杂度降低到执行 n2×n2 布尔矩阵多对数乘法所需的时间,从而大大改进了 O(n4.75)-time 算法。我们的结果改进了库克、霍斯菲尔德、普雷斯曼、罗宾、西摩、辛蒂亚里、特罗蒂尼翁和武什科维奇[《组合理论学报》,B 辑,2024 年]先前的 O(n21)-time 算法,用于识别所有孔长度相同的图,并将运行时间减少到 O(n7.75)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blazing a trail via matrix multiplications: A faster algorithm for non-shortest induced paths
For vertices u and v of an n-vertex graph G, a uv-trail of G is an induced uv-path of G that is not a shortest uv-path of G. Berger, Seymour, and Spirkl [Discrete Mathematics 2021] gave the previously only known polynomial-time algorithm, running in O(n18) time, to either output a uv-trail of G or ensure that G admits no uv-trail. We reduce the complexity to the time required to perform a poly-logarithmic number of multiplications of n2×n2 Boolean matrices, leading to a largely improved O(n4.75)-time algorithm. Our result improves the previous O(n21)-time algorithm by Cook, Horsfield, Preissmann, Robin, Seymour, Sintiari, Trotignon, and Vušković [Journal of Combinatorial Theory, Series B, 2024] for recognizing graphs with all holes the same length, and reduces the running time to O(n7.75).
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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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