We study the single-site Glauber dynamics for the fugacity $\lambda$, Hard-core model on the random graph $G(n, d/n)$. We show that for the typical instances of the random graph $G(n,d/n)$ and for fugacity $\lambda < \frac{d^d}{(d-1)^{d+1}}$, the mixing time of Glauber dynamics is $n^{1 + O(1/\log \log n)}$. Our result improves on the recent elegant algorithm in [Bezakova, Galanis, Goldberg Stefankovic; ICALP'22]. The algorithm there is a MCMC based sampling algorithm, but it is not the Glauber dynamics. Our algorithm here is simpler, as we use the classic Glauber dynamics. Furthermore, the bounds on mixing time we prove are smaller than those in Bezakova et al. paper, hence our algorithm is also faster. The main challenge in our proof is handling vertices with unbounded degrees. We provide stronger results with regard the spectral independence via branching values and show that the our Gibbs distributions satisfy the approximate tensorisation of the entropy. We conjecture that the bounds we have here are optimal for $G(n,d/n)$. As corollary of our analysis for the Hard-core model, we also get bounds on the mixing time of the Glauber dynamics for the Monomer-dimer model on $G(n,d/n)$. The bounds we get for this model are slightly better than those we have for the Hard-core model
翻译:我们用随机图形$G(n,d/n) 和fugaity $(d-1)++1+$(美元)来研究单点Glauber的动态。我们用随机图形$G(n,d/n) 和fugacle $(lambda) 的典型例子来研究Glauber的动态。我们用随机图形$G(n),d/n) 和forgacle $(d-1) d+1+$(美元),Glauber 的混合时间是$1+O(1/log\log n)$(美元) 。我们的主要挑战就是用无线度处理最近的精度算法。这里的算法是基于 MC 的抽样算法,但不是Glauber 。这里的算法比较简单,因为我们使用经典的Glauber 动态。此外,我们所证明的混合时间界限比Bezkova 和 al. 的模型要小,因此我们的算法也更快。我们的主要挑战就是用未界度的温度值来处理我们所处的螺旋值。 我们的硬值的模型, 我们用最硬的正的直值 显示的透的透的根的分数 。