Duality-based approximation algorithms for depth queries and maximum depth

TL;DR

This paper introduces a duality-based data structure for efficiently approximating the depth of query points in arrangements of geometric objects, enabling faster identification of points with near-maximum depth in various dimensions.

Contribution

The authors develop a linear-time data structure for approximate depth queries that improves dependence on the error parameter, especially for triangles and higher dimensions.

Findings

Algorithms run in linear time relative to input size and queries.

Dependence on the error parameter  is significantly improved over previous methods.

Effective in higher dimensions and with complex geometric objects like triangles.

Abstract

We design an efficient data structure for computing a suitably defined approximate depth of any query point in the arrangement $\mathcal{A}(S)$ of a collection $S$ of $n$ halfplanes or triangles in the plane or of halfspaces or simplices in higher dimensions. We then use this structure to find a point of an approximate maximum depth in $\mathcal{A}(S)$. Specifically, given an error parameter $\epsilon>0$, we compute, for any query point $q$, an underestimate $d^-(q)$ of the depth of $q$, that counts only objects containing $q$, but is allowed to exclude objects when $q$ is $\epsilon$-close to their boundary. Similarly, we compute an overestimate $d^+(q)$ that counts all objects containing $q$ but may also count objects that do not contain $q$ but $q$ is $\epsilon$-close to their boundary. Our algorithms for halfplanes and halfspaces are linear in the number of input objects and in the number of queries, and the dependence of their running time on $\epsilon$ is considerably better than that of earlier techniques. Our improvements are particularly substantial for triangles and in higher dimensions.