Rowmotion Markov chains

IF 1 3区 数学 Q3 MATHEMATICS, APPLIED
Colin Defant , Rupert Li , Evita Nestoridi
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引用次数: 0

Abstract

Rowmotion is a certain well-studied bijective operator on the distributive lattice J(P) of order ideals of a finite poset P. We introduce the rowmotion Markov chain MJ(P) by assigning a probability px to each xP and using these probabilities to insert randomness into the original definition of rowmotion. More generally, we introduce a very broad family of toggle Markov chains inspired by Striker's notion of generalized toggling. We characterize when toggle Markov chains are irreducible, and we show that each toggle Markov chain has a remarkably simple stationary distribution.

We also provide a second generalization of rowmotion Markov chains to the context of semidistrim lattices. Given a semidistrim lattice L, we assign a probability pj to each join-irreducible element j of L and use these probabilities to construct a rowmotion Markov chain ML. Under the assumption that each probability pj is strictly between 0 and 1, we prove that ML is irreducible. We also compute the stationary distribution of the rowmotion Markov chain of a lattice obtained by adding a minimal element and a maximal element to a disjoint union of two chains.

We bound the mixing time of ML for an arbitrary semidistrim lattice L. In the special case when L is a Boolean lattice, we use spectral methods to obtain much stronger estimates on the mixing time, showing that rowmotion Markov chains of Boolean lattices exhibit the cutoff phenomenon.

行运动马尔科夫链
行运动是有限正集 P 的阶理想的分布晶格 J(P) 上某个研究得很清楚的双射算子。我们引入行运动马尔可夫链 MJ(P),为每个 x∈P 指定一个概率 px,并利用这些概率在行运动的原始定义中插入随机性。更广义地说,我们受 Striker 广义切换概念的启发,引入了一个非常广泛的切换马尔可夫链家族。我们描述了切换马尔可夫链的不可还原性,并证明了每个切换马尔可夫链都有一个非常简单的静态分布。给定一个半迭代网格 L,我们为 L 中的每个不可连接元素 j 指定一个概率 pj,并利用这些概率构建行运动马尔可夫链 ML。在每个概率 pj 严格介于 0 和 1 之间的假设下,我们证明 ML 是不可还原的。在 L 是布尔网格的特殊情况下,我们使用光谱方法获得了更强的混合时间估计值,证明了布尔网格的行运动马尔科夫链表现出截断现象。
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来源期刊
Advances in Applied Mathematics
Advances in Applied Mathematics 数学-应用数学
CiteScore
2.00
自引率
9.10%
发文量
88
审稿时长
85 days
期刊介绍: Interdisciplinary in its coverage, Advances in Applied Mathematics is dedicated to the publication of original and survey articles on rigorous methods and results in applied mathematics. The journal features articles on discrete mathematics, discrete probability theory, theoretical statistics, mathematical biology and bioinformatics, applied commutative algebra and algebraic geometry, convexity theory, experimental mathematics, theoretical computer science, and other areas. Emphasizing papers that represent a substantial mathematical advance in their field, the journal is an excellent source of current information for mathematicians, computer scientists, applied mathematicians, physicists, statisticians, and biologists. Over the past ten years, Advances in Applied Mathematics has published research papers written by many of the foremost mathematicians of our time.
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