论文标题

$ {\ mathbb z}^d $ on Swendsen-wang Dynamics中的熵衰减

Entropy decay in the Swendsen-Wang dynamics on ${\mathbb Z}^d$

论文作者

Blanca, Antonio, Caputo, Pietro, Parisi, Daniel, Sinclair, Alistair, Vigoda, Eric

论文摘要

我们研究了整数晶格$ {\ Mathbb Z}^d $上的铁磁ising和Potts模型的Swendsen-Wang动力学的混合时间。该动力学是一种广泛使用的马尔可夫链,在很大程度上抵抗了尖锐的分析,因为它是非本地的,即,它在一个步骤中更改了整个配置。我们证明,每当强烈的空间混合(SSM)保持时,以$ {\ Mathbb z}^d $为$ O(\ log n)$上的任何$ n $ vertex Cube上的混合时间,我们证明这是通过在混合时间上建立匹配的下限来建立匹配的下限。以前最著名的界限是$ O(n)$。 SSM是一种标准条件,对应于与晶格上旋转之间距离之间的距离的指数衰减,并且已知在整个高温(单相)区域中以$ d = 2 $尺寸保持。我们的结果来自修改的对数 - 贝贝尔夫的不等式,这表达了一个事实,即动力学在每个步骤下以恒定速率收缩相对熵。这一事实的证明利用了旋转和边缘的熵空间中熵的新分解,这是Swendsen-Wang动力学的基础,该动力学扩展到有界程度的一般两部分图。这种分解导致了几个其他结果,包括在关节空间上的许多天然局部和非本地马尔可夫链以及标准的随机群集动力学的混合时间边界。

We study the mixing time of the Swendsen-Wang dynamics for the ferromagnetic Ising and Potts models on the integer lattice ${\mathbb Z}^d$. This dynamics is a widely used Markov chain that has largely resisted sharp analysis because it is non-local, i.e., it changes the entire configuration in one step. We prove that, whenever Strong Spatial Mixing (SSM) holds, the mixing time on any $n$-vertex cube in ${\mathbb Z}^d$ is $O(\log n)$, and we prove this is tight by establishing a matching lower bound on the mixing time. The previous best known bound was $O(n)$. SSM is a standard condition corresponding to exponential decay of correlations with distance between spins on the lattice and is known to hold in $d=2$ dimensions throughout the high-temperature (single phase) region. Our result follows from a Modified Log-Sobolev Inequality, which expresses the fact that the dynamics contracts relative entropy at a constant rate at each step. The proof of this fact utilizes a new factorization of the entropy in the joint probability space over spins and edges that underlies the Swendsen-Wang dynamics, which extends to general bipartite graphs of bounded degree. This factorization leads to several additional results, including mixing time bounds for a number of natural local and non-local Markov chains on the joint space, as well as for the standard random-cluster dynamics.

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