Entropy reduction in Euclidean first-passage percolation

TL;DR

This paper introduces an entropy reduction method in Euclidean first-passage percolation, providing tighter bounds on the expected passage time and suggesting a potential link between mean discrepancy and variance.

Contribution

The authors develop an inductive entropy reduction technique that improves bounds on expected passage time in Euclidean FPP, advancing understanding of its concentration properties.

Findings

Stronger upper bounds on expected passage time with iterative logarithmic corrections.

Evidence supporting the inequality  T_n -  T_n b1                                           

Abstract

The Euclidean first-passage percolation (FPP) model of Howard and Newman is a rotationally invariant model of FPP which is built on a graph whose vertices are the points of homogeneous Poisson point process. It was shown that one has (stretched) exponential concentration of the passage time $T_n$ from $0$ to $n\mathbf{e}_1$ about its mean on scale $\sqrt{n}$, and this was used to show the bound $\mu n \leq \mathbb{E}T_n \leq \mu n + C\sqrt{n} (\log n)^a$ for $a,C>0$ on the discrepancy between the expected passage time and its deterministic approximation $\mu = \lim_n \frac{\mathbb{E}T_n}{n}$. In this paper, we introduce an inductive entropy reduction technique that gives the stronger upper bound $\mathbb{E}T_n \leq \mu n + C_k\psi(n) \log^{(k)}n$, where $\psi(n)$ is a general scale of concentration and $\log^{(k)}$ is the $k$-th iterate of $\log$. This gives evidence that the inequality $\mathbb{E}T_n - \mu n \leq C\sqrt{\mathrm{Var}~T_n}$ may hold.