Short proofs for long induced paths

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS Combinatorics, Probability & Computing Pub Date : 2021-06-16 DOI:10.1017/s0963548322000013
N. Dragani'c, Stefan Glock, M. Krivelevich
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引用次数: 2

Abstract

We present a modification of the Depth first search algorithm, suited for finding long induced paths. We use it to give simple proofs of the following results. We show that the induced size-Ramsey number of paths satisfies $\hat{R}_{\mathrm{ind}}(P_n)\leq 5 \cdot 10^7n$ , thus giving an explicit constant in the linear bound, improving the previous bound with a large constant from a regularity lemma argument by Haxell, Kohayakawa and Łuczak. We also provide a bound for the k-colour version, showing that $\hat{R}_{\mathrm{ind}}^k(P_n)=O(k^3\log^4k)n$ . Finally, we present a new short proof of the fact that the binomial random graph in the supercritical regime, $G(n,\frac{1+\varepsilon}{n})$ , contains typically an induced path of length $\Theta(\varepsilon^2) n$ .
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对长诱导路径的简短证明
我们提出了一种改进的深度优先搜索算法,适合于寻找长诱导路径。我们用它来给出以下结果的简单证明。我们从Haxell, Kohayakawa和Łuczak的正则引理论证中证明了诱导的路径size-Ramsey数满足$\hat{R}_{\mathrm{ind}}(P_n)\leq 5 \cdot 10^7n$,从而在线性界中给出了一个显式常数,用一个大常数改进了先前的界。我们还提供了k色版本的界,显示$\hat{R}_{\mathrm{ind}}^k(P_n)=O(k^3\log^4k)n$。最后,我们给出了一个新的简短证明,证明了在超临界状态下,$G(n,\frac{1+\varepsilon}{n})$的二项随机图通常包含一条长度为$\Theta(\varepsilon^2) n$的诱导路径。
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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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