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Slow Mixing of Markov Chains Using Fault Lines and Fat Contours

机译:使用故障线和胖轮廓缓慢混合马尔可夫链

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摘要

We show that local dynamics require exponential time for two sampling problems motivated by statistical physics: independent sets on the triangular lattice (the hard-core lattice gas model) and weighted even orientations of the two-dimensional Cartesian lattice (the 8-vertex model). For each problem, there is a parameter λ known as the fugacity, such that local Markov chains are expected to be fast when λ is small and slow when λ is large. Unfortunately, establishing slow mixing for these models has been a challenge, as standard contour arguments typically used to show that a chain has small conductance do not seem to apply. We modify this approach by introducing the notion of fat contours that can have nontrivial area, and use these to establish slow mixing of local chains defined for these models.
机译:我们表明,局部动力学需要两个时间来解决由统计物理学引起的采样问题:三角晶格上的独立集合(硬核晶格气体模型)和二维笛卡尔晶格的加权偶数方向(8顶点模型) 。对于每个问题,都有一个参数λ称为逸度,这样,当λ较小时,预期局部马尔可夫链快,而在λ大时则预期慢。不幸的是,为这些模型建立缓慢混合一直是一个挑战,因为通常用于表明链电导率较小的标准轮廓参数似乎并不适用。我们通过引入可以具有非平凡面积的脂肪轮廓的概念来修改此方法,并使用这些轮廓建立为这些模型定义的局部链的缓慢混合。

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