Report on Nearest Dominating Point Queries

TL;DR

This paper studies the problem of finding, for each point in a set, the nearest point that dominates it in multi-dimensional space, with improvements over existing algorithms under certain assumptions.

Contribution

It introduces an enhanced algorithm for nearest dominating point queries, improving efficiency under specific assumptions compared to prior methods.

Findings

Proposes a new algorithm for nearest dominating point queries.

Achieves better performance under certain data assumptions.

Provides theoretical analysis of the algorithm's complexity.

Abstract

Given two points $p, q \in \mathbb R^d$, we say that $p$ dominates $q$ and write $p \succ q$ if each coordinate of $p$ is larger than the corresponding coordinate of $q$. That is, if $p = (p^{(1)}, p^{(2)}, \ldots, p^{(d)})$ and $q = (q^{(1)}, q^{(2)}, \ldots, q^{(d)})$, $p \succ q$ if and only if $p^{(i)} > q^{(i)}$ for all $1 \le i \le d$. For example, $p$ and $q$ could represent various ratings for $2$ restaurants, based on different metrics like taste, affordability, ratings on different platforms, et cetera. $p \succ q$ then means that the first restaurant outperformed the second on each metric. Given a list of restaurants and their rating, we solve the problem of determining, for each restaurant, the closest restaurant to it that dominates it. We improve upon the algorithm under some assumptions towards the end.