arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.02606math.PRmath-phmath.MP
收藏

随机环境下非对称简单排他过程的混合时间

Mixing time for the asymmetric simple exclusion process in a random environment

Hubert Lacoin, Shangjie Yang

AI总结:

本文研究了随机环境下非对称简单排他过程的混合时间,在粒子具有向右移动趋势的设定下,证明了其混合时间以 $N$ 的幂律增长,并给出了具体的渐近上下界。

摘要:

我们考虑整数区间 $ [1, N]$ 上具有 $k\le N/2$ 个粒子以及空间非均匀跳跃率的简单排他过程。如果目标位置未被占据,位于位置 $x\in [ 1, N]$ 的粒子以速率 $1-ω_x$ 跳到位置 $x-1$(若 $x\ge 2$),并以速率 $ω_x$ 跳到位置 $x+1$(若 $x \le N-1$)。序列 $ω=(ω_x)_{ x \in \mathbb{Z}}$ 通过对某概率律进行 IID 采样得到,该概率律的支撑集远离零和一(换言之,随机环境满足一致椭圆性条件)。我们进一步假设 $\mathbb{E}[ \log ρ_1 ]<0$,其中 $ρ_1:= (1-ω_1)/ω_1$,这意味着粒子有向右移动的趋势。我们证明,在此设定下排他过程的混合时间以 $N$ 的幂律增长。更确切地说,对于具有 $N^{β+o(1)}$ 个粒子(其中 $β\in [0,1)$)的排他过程,在 $N$ 渐近大的情况下,有 $$ N^{\max\left(1,\frac {1}λ, β+ \frac 1 {2λ}\right)+o(1)} \le t_{\mathrm{Mix}}^{N,k} \le N^{C+o(1)}$$ 其中 $λ>0$ 满足 $\mathbb{E}[ρ_1^λ]=1$(若该方程无正根则 $λ=\infty$),且 $C$ 是一个依赖于 $ω$ 分布的常数。我们推测下界在次多项式修正范围内是紧的。

英文摘要:

We consider the simple exclusion process in the integer segment $ [1, N]$ with $k\le N/2$ particles and spatially inhomogenous jumping rates. A particle at site $x\in [ 1, N]$ jumps to site $x-1$ (if $x\ge 2$) at rate $1-ω_x$ and to site $x+1$ (if $x \le N-1$) at rate $ω_x$ if the target site is not occupied. The sequence $ω=(ω_x)_{ x \in \mathbb{Z}}$ is chosen by IID sampling from a probability law whose support is bounded away from zero and one (in other words the random environment satisfies the uniform ellipticity condition). We further assume $\mathbb{E}[ \log ρ_1 ]<0$ where $ρ_1:= (1-ω_1)/ω_1$, which implies that our particles have a tendency to move to the right. We prove that the mixing time of the exclusion process in this setup grows like a power of $N$. More precisely, for the exclusion process with $N^{β+o(1)}$ particles where $β\in [0,1)$, we have in the large $N$ asymptotic $$ N^{\max\left(1,\frac {1}λ, β+ \frac 1 {2λ}\right)+o(1)} \le t_{\mathrm{Mix}}^{N,k} \le N^{C+o(1)}$$ where $λ>0$ is such that $\mathbb{E}[ρ_1^λ]=1$ ($λ=\infty$ if the equation has no positive root) and $C$ is a constant which depends on the distribution of $ω$. We conjecture that our lower bound is sharp up to sub-polynomial correction.

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。

↑