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arXiv 2407.04576cs.DMcs.DSmath.PR
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树上边着色随机采样至最大度情形的最优混合

Optimal Mixing for Randomly Sampling Edge Colorings on Trees Down to the Max Degree

Charlie Carlson, Xiaoyu Chen, Weiming Feng, Eric Vigoda

AI总结:

本文证明树上边着色的Glauber动力学在q≥Δ+2时具有O(n)最优松弛时间,并为q=Δ+1情形提出更新相邻边对的替代链,同时给出Δ-正则完全树的混合时间上界。

摘要:

我们研究用于随机生成给定树的边着色的马尔可夫链的收敛速率。我们关注的是Glauber动力学,它在每一步随机选择一条边并更新其颜色。对于具有n个顶点和最大度Δ的树T,当颜色数q满足q≥Δ+2时,我们证明Glauber动力学具有O(n)的最优松弛时间,其中松弛时间是谱间隙的倒数。就Δ而言,这在q的取值范围内是最优的,因为Dyer、Goldberg和Jerrum(2006)证明了当q=Δ+1时松弛时间为Ω(n^3)。对于q=Δ+1的情形,我们证明了一种更新一对相邻边的替代马尔可夫链具有O(n)的松弛时间。此外,对于Δ-正则完全树,我们证明了相应马尔可夫链的O(n log^2 n)混合时间上界。我们的证明通过一种新颖的归纳方法建立了方差的近似张量化,其中基例是一棵高度为ℓ=O(Δ^2 log^2 Δ)的树,我们使用规范路径论证对其进行分析。

英文摘要:

We address the convergence rate of Markov chains for randomly generating an edge coloring of a given tree. Our focus is on the Glauber dynamics which updates the color at a randomly chosen edge in each step. For a tree $T$ with $n$ vertices and maximum degree $Δ$, when the number of colors $q$ satisfies $q\geqΔ+2$ then we prove that the Glauber dynamics has an optimal relaxation time of $O(n)$, where the relaxation time is the inverse of the spectral gap. This is optimal in the range of $q$ in terms of $Δ$ as Dyer, Goldberg, and Jerrum (2006) showed that the relaxation time is $Ω(n^3)$ when $q=Δ+1$. For the case $q=Δ+1$, we show that an alternative Markov chain which updates a pair of neighboring edges has relaxation time $O(n)$. Moreover, for the $Δ$-regular complete tree we prove $O(n\log^2{n})$ mixing time bounds for the respective Markov chain. Our proofs establish approximate tensorization of variance via a novel inductive approach, where the base case is a tree of height $\ell=O(Δ^2\log^2Δ)$, which we analyze using a canonical paths argument.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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