A local core number based algorithm for the maximum clique problem

IF 0.6 Q3 MATHEMATICS Transactions on Combinatorics Pub Date : 2021-01-21 DOI:10.22108/TOC.2021.120153.1686
Neda Mohammadi, M. Kadivar
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引用次数: 1

Abstract

The maximum clique problem (MCP) is to determine a complete subgraph of maximum cardinality in a graph. MCP is a fundamental problem in combinatorial optimization and is noticeable for its wide range of applications. In this paper, we present two branch-and-bound exact algorithms for finding a maximum clique in an undirected graph. Many efficient exact branch and bound maximum clique algorithms use approximate coloring to compute an upper bound on the clique number but, as a new pruning strategy, we show that local core numbers are more efficient. Moreover, instead of neighbors set of a vertex, our search area is restricted to a subset of the set in each subproblem which speeds up clique finding process. This subset is based on the core of the vertices of a given graph. We improved the MCQ and MaxCliqueDyn algorithms with respect to the new pruning strategy and search area restriction. Experimental results demonstrate that the improved algorithms outperform the previous well-known algorithms for many instances when applied to DIMACS benchmark and random graphs.
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最大集团问题的一种基于局部核数的算法
最大团问题(MCP)是确定图中最大基数的完整子图。MCP是组合优化中的一个基本问题,因其广泛的应用而引人注目。本文给出了两个在无向图中求最大团的分枝定界精确算法。许多有效的精确分支定界最大团算法使用近似着色来计算团数的上界,但作为一种新的修剪策略,我们证明了局部核心数更有效。此外,我们的搜索区域被限制在每个子问题中的集合的子集,而不是顶点的邻居集,这加快了集团查找过程。该子集基于给定图的顶点的核心。在新的修剪策略和搜索区域限制方面,我们改进了MCQ和MaxCliqueDyn算法。实验结果表明,将改进算法应用于DIMACS基准图和随机图时,在许多情况下都优于以前的算法。
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
CiteScore
0.80
自引率
0.00%
发文量
2
审稿时长
30 weeks
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