临界KCM的改进通用性:下界

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS Combinatorics, Probability & Computing Pub Date : 2020-11-13 DOI:10.1017/S0963548322000025
Ivailo Hartarsky, Laure Marech'e
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引用次数: 9

摘要

我们研究了一类相互作用的粒子系统,称为动力学约束模型(KCM),它在二维中与称为自举渗透的单调元胞自动机紧密相连。这类模型有三类,研究最多的是关键模型。Martinelli、Morris、Toninelli等人最近的一系列研究表明,具有相同性质的临界自举渗流模型的KCM对应体分为两个具有不同行为的类。与第一作者的论文一起,我们的工作确定了感染时间的对数,直到所有关键KCM的常数因子,这在以前只知道对数修正。这改进了除了Duarte-KCM之外的所有先前的结果,对于Duarte-KCM,我们给出了已知最佳结果的新证明。我们建立了在这个精度水平上临界KCM必须分为7类,而不是自举渗透中的2类。本文统一建立了临界KCM的下界,恢复了Toninelli和作者的普适性结果以及Martinelli、Toninelli和第二作者的Duarte模型结果。
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Refined universality for critical KCM: lower bounds
We study a general class of interacting particle systems called kinetically constrained models (KCM) in two dimensions tightly linked to the monotone cellular automata called bootstrap percolation. There are three classes of such models, the most studied being the critical one. In a recent series of works by Martinelli, Morris, Toninelli and the authors, it was shown that the KCM counterparts of critical bootstrap percolation models with the same properties split into two classes with different behaviour. Together with the companion paper by the first author, our work determines the logarithm of the infection time up to a constant factor for all critical KCM, which were previously known only up to logarithmic corrections. This improves all previous results except for the Duarte-KCM, for which we give a new proof of the best result known. We establish that on this level of precision critical KCM have to be classified into seven categories instead of the two in bootstrap percolation. In the present work, we establish lower bounds for critical KCM in a unified way, also recovering the universality result of Toninelli and the authors and the Duarte model result of Martinelli, Toninelli and the second author.
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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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