Optimal Approximate Polytope Membership

TL;DR

This paper introduces a new data structure for approximate polytope membership that achieves logarithmic query time with optimal storage, improving efficiency and matching theoretical lower bounds in high-dimensional spaces.

Contribution

It presents a novel hierarchy of ellipsoids based on Macbeath regions, enabling optimal approximate polytope membership queries with reduced storage requirements.

Findings

Achieves logarithmic query time with optimal storage of O(1/ε^{(d-1)/2})

Significantly improves approximate nearest neighbor search space bounds

Halves the ε-dependency exponent in nearest neighbor data structures

Abstract

In the polytope membership problem, a convex polytope $K$ in $R^d$ is given, and the objective is to preprocess $K$ into a data structure so that, given a query point $q \in R^d$, it is possible to determine efficiently whether $q \in K$. We consider this problem in an approximate setting and assume that $d$ is a constant. Given an approximation parameter $\varepsilon > 0$, the query can be answered either way if the distance from $q$ to $K$'s boundary is at most $\varepsilon$ times $K$'s diameter. Previous solutions to the problem were on the form of a space-time trade-off, where logarithmic query time demands $O(1/\varepsilon^{d-1})$ storage, whereas storage $O(1/\varepsilon^{(d-1)/2})$ admits roughly $O(1/\varepsilon^{(d-1)/8})$ query time. In this paper, we present a data structure that achieves logarithmic query time with storage of only $O(1/\varepsilon^{(d-1)/2})$, which matches the worst-case lower bound on the complexity of any $\varepsilon$-approximating polytope. Our data structure is based on a new technique, a hierarchy of ellipsoids defined as approximations to Macbeath regions. As an application, we obtain major improvements to approximate Euclidean nearest neighbor searching. Notably, the storage needed to answer $\varepsilon$-approximate nearest neighbor queries for a set of $n$ points in $O(\log \frac{n}{\varepsilon})$ time is reduced to $O(n/\varepsilon^{d/2})$. This halves the exponent in the $\varepsilon$-dependency of the existing space bound of roughly $O(n/\varepsilon^d)$, which has stood for 15 years (Har-Peled, 2001).