On the Budgeted Hausdorff Distance Problem

Abstract

$ \newcommand{\Re}{\mathbb{R}} \newcommand{\reals}{\mathbb{R}} \newcommand{\SetX}{\mathsf{X}} \newcommand{\optX}[1]{#1^\star} \newcommand{\Qopt}{\Mh{\optX{Q}}} \newcommand{\rad}{r} \newcommand{\Mh}[1]{#1} \newcommand{\query}{q} \newcommand{\eps}{\varepsilon} \newcommand{\VorX}[1]{\mathcal{V} \pth{#1}} \newcommand{Polygon}{\mathsf{P}} \newcommand{\IntRange}[1]{[ #1 ]} \newcommand{\Space}{\overline{\mathsf{m}}} \newcommand{\pth}[2][\!]{#1\left({#2}\right)} \newcommand{\polylog}{\mathrm{polylog}} \newcommand{\N}{\mathbb N} \newcommand{\Z}{\mathbb Z} \newcommand{\pt}{p} \newcommand{\distY}[2]{\left\| {#1} - {#2} \right\|} \newcommand{\ptq}{q} \newcommand{\RunningTime}{O\bigl(n^{3/2} \sqrt{k} \log^{3/2} n + kn \log^2 n\bigr)} \newcommand{\pts}{s}$ Given a set $P$ of $n$ points in the plane, and a parameter $k$, we present an algorithm, whose running time is $\RunningTime$, with high probability, that computes a subset $\Qopt \subseteq P$ of $k$ points, that minimizes the Hausdorff distance between the convex-hulls of $\Qopt$ and $P$. This is the first subquadratic algorithm for this problem if $k$ is small.