Tree Embedding in High Dimensions: Dynamic and Massively Parallel

TL;DR

This paper introduces a new framework for high-dimensional tree embedding that enables efficient dynamic and parallel algorithms with low distortion, applicable to various metric spaces including Euclidean spaces.

Contribution

It presents a novel metric decomposition framework and develops dynamic and massively parallel algorithms for low-distortion tree embedding in high dimensions.

Findings

Dynamic algorithm maintains $O_\ ext{\epsilon}(\log n)$-distortion with efficient updates.

Massively parallel algorithm achieves low distortion in constant rounds.

Applications include improved algorithms for $k$-median and earth-mover distance.

Abstract

Tree embedding has been a fundamental method in algorithm design with wide applications. We focus on the efficiency of building tree embedding in various computational settings under high-dimensional Euclidean $\mathbb{R}^d$. We devise a new tree embedding construction framework that operates on an arbitrary metric decomposition with bounded diameter, offering a tradeoff between distortion and the locality of its algorithmic steps. This framework works for general metric spaces and may be of independent interest beyond the Euclidean setting. Using this framework, we obtain a dynamic algorithm that maintains an $O_\epsilon(\log n)$-distortion tree embedding with update time $\tilde O(n^\epsilon + d)$ subject to point insertions/deletions, and a massively parallel algorithm that achieves $O_\epsilon(\log n)$-distortion in $O(1)$ rounds and total space $\tilde O(n^{1 + \epsilon})$ (for constant $\epsilon \in (0, 1)$). These new tree embedding results allow for a wide range of applications. Notably, under a similar performance guarantee as in our tree embedding algorithms, i.e., $\tilde O(n^\epsilon + d)$ update time and $O(1)$ rounds, we obtain $O_\epsilon(\log n)$-approximate dynamic and MPC algorithms for $k$-median and earth-mover distance in $\mathbb{R}^d$.