The Squishy Grid Problem

TL;DR

This paper explores methods to assign edge weights in an integer grid graph to approximate Euclidean distances asymptotically, introducing three approaches including tiling, hierarchical highways, and probabilistic sampling, with theoretical and experimental results.

Contribution

It presents three novel methods for assigning edge weights in grid graphs to approximate Euclidean distances, including a tiling-based, hierarchical, and probabilistic approach, with quantitative bounds and experimental validation.

Findings

Pinwheel tiling achieves distortion $(1+1/ ext{log}^ ext{ξ} ext{log D})$

Hierarchical highways achieve distortion $(1 + 1/ ext{D}^{1/9})$

A 2-point distribution can approximate Euclidean distances within 1% in experiments

Abstract

In this paper we consider the problem of approximating Euclidean distances by the infinite integer grid graph. Although the topology of the graph is fixed, we have control over the edge-weight assignment $w:E\to \mathbb{R}_{\ge 0}$, and hope to have grid distances be asymptotically isometric to Euclidean distances, that is, for all grid points $u,v$, $\mathrm{dist}_w(u,v) = (1\pm o(1))\|u-v\|_2$. We give three methods for solving this problem, each attractive in its own way. * Our first construction is based on an embedding of the recursive, non-periodic pinwheel tiling of Radin and Conway into the integer grid. Distances in the pinwheel graph are asymptotically isometric to Euclidean distances, but no explicit bound on the rate of convergence was known. We prove that the multiplicative distortion of the pinwheel graph is $(1+1/\Theta(\log^\xi \log D))$, where $D$ is the Euclidean distance and $\xi=\Theta(1)$. The pinwheel tiling approach is conceptually simple, but can be improved quantitatively. * Our second construction is based on a hierarchical arrangement of "highways." It is simple, achieving stretch $(1 + 1/\Theta(D^{1/9}))$, which converges doubly exponentially faster than the pinwheel tiling approach. * The first two methods are deterministic. An even simpler approach is to sample the edge weights independently from a common distribution $\mathscr{D}$. Whether there exists a distribution $\mathscr{D}^*$ that makes grid distances Euclidean, asymptotically and in expectation, is major open problem in the theory of first passage percolation. Previous experiments show that when $\mathscr{D}$ is a Fisher distribution, grid distances are within 1\% of Euclidean. We demonstrate experimentally that this level of accuracy can be achieved by a simple 2-point distribution that assigns weights 0.41 or 4.75 with probability 44\% and 56\%, respectively.