Google Research

New Streaming Algorithms for High Dimensional EMD and MST

To be submitted to ACM Symposium on Theory of Computing (STOC) (2022)


We study streaming algorithms for two fundamental geometric problems: computing the cost of a Minimum Spanning Tree (MST) of an $n$-point set $X \subset \{1,2,\dots,\Delta\}^d$, and~computing the Earth Mover Distance (EMD) between two multi-sets $A,B \subset \{1,2,\dots,\Delta\}^d$ of size $n$. We~consider the turnstile model, where points can be added and removed. We give a one-pass streaming algorithm for MST and a two-pass streaming algorithm~for EMD, both achieving an approximation factor of $\tilde{O}(\log n)$ and using $\polylog(n,d,\Delta)$-space only. Furthermore, our algorithm for EMD can be compressed to a single pass using $\Delta d \cdot \polylog( n,d)$-space whenever ${|A \cap B|}/{|A \cup B|} \leq 1-\Omega(1)$. Previously, the best known sublinear-space streaming algorithms for either problem achieved an approximation of $O(\min\{ \log n , \log (\Delta d)\} \log n)$ \cite{AIK08, BDIRW20}. For MST, we also prove that any constant space streaming algorithm can only achieve an approximation of $\Omega(\log n)$, analogous to the $\Omega(\log n)$ lower bound for EMD of \cite{AIK08}.

Our algorithms are based on an improved analysis of a recursive space partitioning method known generically as the Quadtree. Specifically, we show that the Quadtree achieves an $\tilde{O}(\log n)$ approximation for both EMD and MST, improving on the $O(\min\{ \log n , \log (\Delta d)\} \log n)$ approximation of \cite{AIK08, BDIRW20}. %The main conceptual contribution is an analytical framework for studying the quadtree which goes beyond worst-case distortion of randomized tree embeddings.

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