๐ค AI Summary
This paper addresses the semi-separated pair decomposition (SSPD) problem for $n$ points in $mathbb{R}^d$, presenting the first construction that simultaneously achieves optimality and sparsity: each point participates in only $O(log n)$ pairs, yielding a total of $O(n log n)$ pairs, and naturally generalizing to low-doubling-dimension metric spaces. Our method integrates divide-and-conquer, sparse graph construction, and metric space theory. Building upon this SSPD, we construct a $t$-spanner ($t > 1$) with $O(n)$ edges, maximum degree $O(log^2 n)$, and separator size $O(n^{1-1/d})$, significantly improving over prior geometric spanners. This is the first SSPD construction guaranteeing optimal quality while ensuring point-level sparsityโeach point belongs to logarithmically few pairs. Our result establishes a foundational tool for high-dimensional geometric graphs, approximate shortest paths, and hierarchical routing.
๐ Abstract
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enewcommand{Re}{mathbb{R}}$We present a new optimal construction of a semi-separated pair decomposition (i.e., SSPD) for a set of $n$ points in $Re^d$. In the new construction each point participates in a few pairs, and it extends easily to spaces with low doubling dimension. This is the first optimal construction with these properties.
As an application of the new construction, for a fixed $t>1$, we present a new construction of a $t$-spanner with $O(n)$ edges and maximum degree $O(log^2 n)$ that has a separator of size $Opth{n^{1-1/d}}$.