Discrete Mathematics & Theoretical Computer Science· 2026Q2
On strictly output sensitive color frequency reporting
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- 2026year
Short summary
A new data structure for $\mathbb{R}^2$ allows reporting color frequencies in query regions with $O(\log n + k\log_s n)$ time, where $k$ is the number of colors, and $s$ is a parameter controlling space-time trade-offs.
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Key points
- A data structure for $\mathbb{R}^2$ supports color frequency reporting in $O(\log n + k\log_s n)$ time, with $O(ns\log_s n)$ size.
- A lower bound in the arithmetic model shows that achieving query times better than $Ω(ϕ rac{\log (n / ϕ)}{\log (m / n)})$ with $O(m)$ space is impossible for the weighted version.
- The space complexity of the data structure can be reduced to $O(n(s ϕ)^ε \log_s n)$ using a transformation.
- An $O(n^{1+\varepsilon} + m \log n + K)$-time algorithm answers $m$ dominance queries in $\mathbb{R}^2$ with total output $K$ using linear working space.
AI-generated from the title and abstract; the full text is not read.
Abstract
Given a set of $n$ colored points $P \subset \mathbb{R}^d$ we wish to store $P$ such that, given some query region $Q$, we can efficiently report the colors of the points appearing in the query region, along with their frequencies. This is the \emph{color frequency reporting} problem. We study the case where query regions $Q$ are axis-aligned boxes or dominance ranges. If $Q$ contains $k$ colors, the main goal is to achieve ``strictly output sensitive'' query time $O(f(n) + k)$. Firstly, we show that, for every $s \in \{ 2, \dots, n \}$, there exists a simple $O(ns\log_s n)$ size data structure for points in $\mathbb{R}^2$ that allows frequency reporting queries in $O(\log n + k\log_s n)$ time. Secondly, we give a lower bound for the weighted version of the problem in the arithmetic model of computation, proving that with $O(m)$ space one can not achieve query times better than $Ω\left(ϕ\frac{\log (n / ϕ)}{\log (m / n)}\right)$, where $ϕ$ is the number of possible colors. This means that our data structure is near-optimal. We extend these results to higher dimensions as well. Thirdly, we present a transformation that allows us to reduce the space usage of the aforementioned data structure to $O(n(s ϕ)^\varepsilon \log_s n)$. Finally, we give an $O(n^{1+\varepsilon} + m \log n + K)$-time algorithm that can answer $m$ dominance queries in $\mathbb{R}^2$ with total output complexity $K$, while using only linear working space. new version for submission to DMTCS journal
The authors' abstract, as published at the source. Discrete Mathematics & Theoretical Computer Science, 2026 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science