Ramsey partitions and proximity data structures

TL;DR

This paper introduces Ramsey partitions for finite metric spaces, constructs optimal ones, and applies them to improve approximate distance oracles and other geometric data structures, advancing theoretical understanding and practical algorithms.

Contribution

It defines Ramsey partitions, constructs optimal versions, and applies them to enhance approximate distance oracles with large distortion, simplifying proofs and improving bounds.

Findings

Existence of good Ramsey partitions implies solutions to the metric Ramsey problem.

Constructed optimal Ramsey partitions for metric spaces.

Designed the best known approximate distance oracles with large distortion.

Abstract

This paper addresses two problems lying at the intersection of geometric analysis and theoretical computer science: The non-linear isomorphic Dvoretzky theorem and the design of good approximate distance oracles for large distortion. We introduce the notion of Ramsey partitions of a finite metric space, and show that the existence of good Ramsey partitions implies a solution to the metric Ramsey problem for large distortion (a.k.a. the non-linear version of the isomorphic Dvoretzky theorem, as introduced by Bourgain, Figiel, and Milman). We then proceed to construct optimal Ramsey partitions, and use them to show that for every e\in (0,1), any n-point metric space has a subset of size n^{1-e} which embeds into Hilbert space with distortion O(1/e). This result is best possible and improves part of the metric Ramsey theorem of Bartal, Linial, Mendel and Naor, in addition to considerably simplifying its proof. We use our new Ramsey partitions to design the best known approximate distance oracles when the distortion is large, closing a gap left open by Thorup and Zwick. Namely, we show that for any $n$ point metric space X, and k>1, there exists an O(k)-approximate distance oracle whose storage requirement is O(n^{1+1/k}), and whose query time is a universal constant. We also discuss applications of Ramsey partitions to various other geometric data structure problems, such as the design of efficient data structures for approximate ranking.