Large Independent Sets on Random d-Regular Graphs with Fixed Degree d

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-10-17 DOI:10.3390/computation11100206
Raffaele Marino, Scott Kirkpatrick
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引用次数: 4

Abstract

The maximum independent set problem is a classic and fundamental combinatorial challenge, where the objective is to find the largest subset of vertices in a graph such that no two vertices are adjacent. In this paper, we introduce a novel linear prioritized local algorithm tailored to address this problem on random d-regular graphs with a small and fixed degree d. Through exhaustive numerical simulations, we empirically investigated the independence ratio, i.e., the ratio between the cardinality of the independent set found and the order of the graph, which was achieved by our algorithm across random d-regular graphs with degree d ranging from 5 to 100. Remarkably, for every d within this range, our results surpassed the existing lower bounds determined by theoretical methods. Consequently, our findings suggest new conjectured lower bounds for the MIS problem on such graph structures. This finding has been obtained using a prioritized local algorithm. This algorithm is termed ‘prioritized’ because it strategically assigns priority in vertex selection, thereby iteratively adding them to the independent set.
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d阶固定随机正则图上的大独立集
最大独立集问题是一个经典和基本的组合挑战,其目标是在图中找到最大的顶点子集,使得没有两个顶点相邻。在本文中,我们引入了一种新的线性优先局部算法,专门针对具有小而固定的d度的随机d正则图来解决这一问题。通过详尽的数值模拟,我们经验地研究了独立比,即所发现的独立集的基数与图的阶数之间的比率,我们的算法在d度范围为5到100的随机d正则图上实现了这一比例。值得注意的是,在这个范围内的每一个d,我们的结果都超过了由理论方法确定的现有下界。因此,我们的发现提出了这种图结构上MIS问题的新推测下界。这一发现是使用优先级局部算法获得的。这种算法被称为“优先”,因为它在顶点选择中策略性地分配优先级,从而迭代地将它们添加到独立的集合中。
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
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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