Approximating Multiplicatively Weighted Voronoi Diagrams: Efficient Construction with Linear Size

TL;DR

This paper introduces efficient approximation algorithms for constructing multiplicatively weighted Voronoi diagrams with linear size, achieving near-optimal subdivision size and improved query times, applicable in high-dimensional spaces.

Contribution

It presents the first approximation algorithms with optimal size for weighted Voronoi diagrams, introducing cores, bisector coresets, and adaptive refinement techniques.

Findings

Diagram size is $O_d(n rac{ ext{log}(1/ ext{ε})}{ ext{ε}^{d-1}})$

Construction time is near-linear in the size of the diagram

Improves point-location query bounds by a factor of $d$

Abstract

Given a set of $n$ sites from $\mathbb{R}^d$, each having some positive weight factor, the Multiplicatively Weighted Voronoi Diagram is a subdivision of space that associates each cell to the site whose weighted Euclidean distance is minimal for all points in the cell. We give novel approximation algorithms that output a cube-based subdivision such that the weighted distance of a point with respect to the associated site is at most $(1+\varepsilon)$ times the minimum weighted distance, for any fixed parameter $\varepsilon \in (0,1)$. The diagram size is $O_d(n \log(1/\varepsilon)/\varepsilon^{d-1})$ and the construction time is within an $O_D(\log(n)/\varepsilon^{(d+5)/2})$-factor of the size bound. We also prove a matching lower bound for the size, showing that the proposed method is the first to achieve \emph{optimal size}, up to $\Theta(1)^d$-factors. In particular, the obscure $\log(1/\varepsilon)$ factor is unavoidable. As a by-product, we obtain a factor $d^{O(d)}$ improvement in size for the unweighted case and $O(d \log(n) + d^2 \log(1/\varepsilon))$ point-location time in the subdivision, improving the known query bound by one $d$-factor. The key ingredients of our approximation algorithms are the study of convex regions that we call cores, an adaptive refinement algorithm to obtain optimal size, and a novel notion of \emph{bisector coresets}, which may be of independent interest. In particular, we show that coresets with $O_d(1/\varepsilon^{(d+3)/2})$ worst-case size can be computed in near-linear time.