New Data Structures for Orthogonal Range Reporting and Range Minima Queries
July 21, 2020 Β· Declared Dead Β· π ACM-SIAM Symposium on Discrete Algorithms
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yakov Nekrich
arXiv ID
2007.11094
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.CG
Citations
12
Venue
ACM-SIAM Symposium on Discrete Algorithms
Last Checked
3 months ago
Abstract
In this paper we present new data structures for two extensively studied variants of the orthogonal range searching problem. First, we describe a data structure that supports two-dimensional orthogonal range minima queries in $O(n)$ space and $O(\log^{\varepsilon} n)$ time, where $n$ is the number of points in the data structure and $\varepsilon$ is an arbitrarily small positive constant. Previously known linear-space solutions for this problem require $O(\log^{1+\varepsilon} n)$ (Chazelle, 1988) or $O(\log n\log \log n)$ time (Farzan et al., 2012). A modification of our data structure uses space $O(n\log \log n)$ and supports range minima queries in time $O(\log \log n)$. Both results can be extended to support three-dimensional five-sided reporting queries. Next, we turn to the four-dimensional orthogonal range reporting problem and present a data structure that answers queries in optimal $O(\log n/\log \log n + k)$ time, where $k$ is the number of points in the answer. This is the first data structure that achieves the optimal query time for this problem. Our results are obtained by exploiting the properties of three-dimensional shallow cuttings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted