Semi-algebraic Range Reporting and Emptiness Searching with Applications

TL;DR

This paper extends semi-algebraic range searching techniques to more general cases, providing more efficient solutions for range emptiness and reporting problems without linearization, with applications in computational geometry.

Contribution

It introduces a new approach to semi-algebraic range searching that avoids linearization, improving efficiency and applicability over previous methods.

Findings

Extended analysis to general semi-algebraic ranges.

Achieved more efficient range searching solutions.

Demonstrated applications in four computational geometry problems.

Abstract

In a typical range emptiness searching (resp., reporting) problem, we are given a set $P$ of $n$ points in $\reals^d$, and wish to preprocess it into a data structure that supports efficient range emptiness (resp., reporting) queries, in which we specify a range $\sigma$, which, in general, is a semi-algebraic set in $\reals^d$ of constant description complexity, and wish to determine whether $P\cap\sigma=\emptyset$, or to report all the points in $P\cap\sigma$. Range emptiness searching and reporting arise in many applications, and have been treated by Matou\v{s}ek \cite{Ma:rph} in the special case where the ranges are halfspaces bounded by hyperplanes. As shown in \cite{Ma:rph}, the two problems are closely related, and have solutions (for the case of halfspaces) with similar performance bounds. In this paper we extend the analysis to general semi-algebraic ranges, and show how to adapt Matou\v{s}ek's technique, without the need to {\em linearize} the ranges into a higher-dimensional space. This yields more efficient solutions to several useful problems, and we demonstrate the new technique in four applications.