CGDSJun 26

Closest Pair Queries in Vertical Slabs and Tight Bounds on the Number of Possible Answers

arXiv:2502.17600h-index: 33
Originality Incremental advance
AI Analysis

For computational geometry researchers, this provides the first linear-space data structure for vertical slab closest-pair queries with sublinear query time, solving an open problem.

The paper proves tight bounds on the number of distinct closest pairs across vertical slabs of a point set, and uses these bounds to construct a linear-space data structure that answers closest-pair queries in any vertical slab in O(n^{1/2+ε}) time, achieving the first sublinear query time with linear space.

Let $S$ be a set of $n$ points in $\mathbb{R}^d$, where $d \geq 2$ is a constant, and let $H_1,H_2,\ldots,H_{m+1}$ be a sequence of vertical hyperplanes that are sorted by their first coordinates, such that exactly $n/m$ points of $S$ are between any two successive hyperplanes. Let $A(S,m)$ be the set of different closest pairs in the ${{m+1} \choose 2}$ vertical slabs that are bounded by $H_i$ and $H_j$, over all $1 \leq i < j \leq m+1$. We prove tight bounds for the largest possible size of $A(S,m)$, over all point sets of size $n$, and for all values of $1 \leq m \leq n$. As a result of these bounds, we obtain, for any constant $ε>0$, a data structure of size $O(n)$, such that for any vertical query slab $Q$, the closest pair in the set $Q \cap S$ can be reported in $O(n^{1/2+ε})$ time. Prior to this work, no linear space data structure with sublinear query time was known.

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