The Voronoi Diagram of Weakly Smooth Planar Point Sets in $O(\log n)$ Deterministic Rounds on the Congested Clique
April 09, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jesper Jansson, Christos Levcopoulos, Andrzej Lingas
arXiv ID
2404.06068
Category
cs.CG: Computational Geometry
Cross-listed
cs.DS
Citations
0
Venue
arXiv.org
Last Checked
3 months ago
Abstract
We study the problem of computing the Voronoi diagram of a set of $n^2$ points with $O(\log n)$-bit coordinates in the Euclidean plane in a substantially sublinear in $n$ number of rounds in the congested clique model with $n$ nodes. Recently, Jansson et al. have shown that if the points are uniformly at random distributed in a unit square then their Voronoi diagram within the square can be computed in $O(1)$ rounds with high probability (w.h.p.). We show that if a very weak smoothness condition is satisfied by an input set of $n^2$ points with $O(\log n)$-bit coordinates in the unit square then the Voronoi diagram of the point set within the unit square can be computed in $O(\log n)$ rounds in this model.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Computational Geometry
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Dynamic Planar Convex Hull
R.I.P.
π»
Ghosted
TEMPO: Feature-Endowed TeichmΓΌller Extremal Mappings of Point Clouds
R.I.P.
π»
Ghosted
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
R.I.P.
π»
Ghosted
Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal
R.I.P.
π»
Ghosted
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
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