Fr\'echet Distance in the Imbalanced Case

TL;DR

This paper establishes tight computational hardness bounds for approximating the Fréchet distance in imbalanced and high-dimensional cases, and introduces near-optimal algorithms for certain scenarios.

Contribution

It provides new lower bounds for approximating the Fréchet distance in imbalanced and high-dimensional settings, and offers efficient algorithms with near-optimal approximation factors.

Findings

Hardness of approximation for 1D discrete Fréchet distance

Extended lower bounds to 2D continuous and discrete Fréchet distances

A near-optimal approximation algorithm for curves in any Lp space

Abstract

Given two polygonal curves $P$ and $Q$ defined by $n$ and $m$ vertices with $m\leq n$, we show that the discrete Fr\'echet distance in 1D cannot be approximated within a factor of $2-\varepsilon$ in $\mathcal{O}((nm)^{1-\delta})$ time for any $\varepsilon, \delta>0$ unless OVH fails. Using a similar construction, we extend this bound for curves in 2D under the continuous or discrete Fr\'echet distance and increase the approximation factor to $1+\sqrt{2}-\varepsilon$ (resp. $3-\varepsilon$) if the curves lie in the Euclidean space (resp. in the $L_\infty$-space). This strengthens the lower bound by Buchin, Ophelders, and Speckmann to the case where $m=n^{\alpha}$ for $\alpha\in(0,1)$ and increases the approximation factor of $1.001$ by Bringmann. For the discrete Fr\'echet distance in 1D, we provide an approximation algorithm with optimal approximation factor and almost optimal running time. Further, for curves in any dimension embedded in any $L_p$ space, we present a $(3+\varepsilon)$-approximation algorithm for the continuous and discrete Fr\'echet distance using $\mathcal{O}((n+m^2)\log n)$ time, which almost matches the approximation factor of the lower bound for the $L_\infty$ metric.