Streaming Euclidean MST to a Constant Factor

Amit Levi
Erik Waingarten
Xi Chen
54rd Annual ACM Symposium on Theory of Computing (STOC'23)(2023)
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We study streaming algorithms for the fundamental geometric problem of computing the cost of the Euclidean Minimum Spanning Tree (MST) on an $n$-point set $X \subset \R^d$. In the streaming model, the points in $X$ can be added and removed arbitrarily, and the goal is to maintain an approximation in small space. In low dimensions, $(1+\epsilon)$ approximations are possible in sublinear space. However, for high dimensional space the best known approximation for this problem was $\tilde{O}(\log n)$, due to [Chen, Jayaram, Levi, Waingarten, STOC'22], improving on the prior $O(\log^2 n)$ bound due to [Andoni, Indyk, Krauthgamer, SODA '08]. In this paper, we break the logarithmic barrier, and give the first constant factor sublinear space approximation to Euclidean MST. For any $\epsilon\geq 1$, our algorithm achieves an $\tilde{O}(\epsilon^{-2})$ approximation in $n^{O(\epsilon)} d^{O(1)}$ space. We complement this by demonstrating that any single pass algorithm which obtains a better than $1.10$-approximation must use $\Omega(\sqrt{n})$ space, demonstrating that $(1+\epsilon)$ approximations are not possible in high-dimensions, and that our algorithm is tight up to a constant. Nevertheless, we demonstrate that $(1+\epsilon)$ approximations are possible in sublinear space with $O(1/\epsilon)$ passes over the stream. More generally, for any $\alpha \geq 2$, we give a $\alpha$-pass streaming algorithm which achieves a $O(\frac{1}{ \alpha \epsilon})$ approximation in $n^{O(\epsilon)} d^{O(1)}$ space. All our streaming algorithms are linear sketches, and therefore extend to the massively-parallel computation model (MPC). Thus, our results imply the first $(1+\epsilon)$-approximation in a constant number of rounds in the MPC model. Previously, such a result was only known for low-dimensional space ([Andoni, Nikolov, Onak, Yaroslavtsev, STOC'15]), or either required $O(\log n)$ rounds or suffered a $O(\log n)$ approximation.