On oriented cycles in randomly perturbed digraphs

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Igor Araujo, József Balogh, Robert A. Krueger, Simón Piga, Andrew Treglown
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引用次数: 4

Abstract

Abstract In 2003, Bohman, Frieze, and Martin initiated the study of randomly perturbed graphs and digraphs. For digraphs, they showed that for every $\alpha \gt 0$ , there exists a constant $C$ such that for every $n$ -vertex digraph of minimum semi-degree at least $\alpha n$ , if one adds $Cn$ random edges then asymptotically almost surely the resulting digraph contains a consistently oriented Hamilton cycle. We generalize their result, showing that the hypothesis of this theorem actually asymptotically almost surely ensures the existence of every orientation of a cycle of every possible length, simultaneously. Moreover, we prove that we can relax the minimum semi-degree condition to a minimum total degree condition when considering orientations of a cycle that do not contain a large number of vertices of indegree $1$ . Our proofs make use of a variant of an absorbing method of Montgomery.
随机摄动有向图中的有向环
2003年,Bohman、Frieze和Martin开始了随机摄动图和有向图的研究。对于有向图,他们证明了对于每一个$\ α \gt 0$,存在一个常数$C$,使得对于每一个$n$顶点的最小半度的有向图,如果加上$Cn$随机边,则得到的有向图渐进地几乎肯定包含一个一致取向的汉密尔顿环。我们推广了他们的结果,证明了该定理的假设实际上是渐近地几乎肯定地保证了每个可能长度的环的每个方向同时存在。此外,我们证明了当考虑不包含大量度为$1$的顶点时,我们可以将最小半度条件松弛为最小总度条件。我们的证明采用了蒙哥马利吸收法的一种变体。
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来源期刊
Combinatorics, Probability & Computing
Combinatorics, Probability & Computing 数学-计算机:理论方法
CiteScore
2.40
自引率
11.10%
发文量
33
审稿时长
6-12 weeks
期刊介绍: Published bimonthly, Combinatorics, Probability & Computing is devoted to the three areas of combinatorics, probability theory and theoretical computer science. Topics covered include classical and algebraic graph theory, extremal set theory, matroid theory, probabilistic methods and random combinatorial structures; combinatorial probability and limit theorems for random combinatorial structures; the theory of algorithms (including complexity theory), randomised algorithms, probabilistic analysis of algorithms, computational learning theory and optimisation.
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