Computing Circle Packing Representations of Planar Graphs
November 01, 2019 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
Sally Dong, Yin Tat Lee, Kent Quanrud
arXiv ID
1911.00612
Category
cs.CG: Computational Geometry
Cross-listed
cs.DS
Citations
4
Venue
ACM-SIAM Symposium on Discrete Algorithms
Last Checked
6 months ago
Abstract
The Circle Packing Theorem states that every planar graph can be represented as the tangency graph of a family of internally-disjoint circles. A well-known generalization is the Primal-Dual Circle Packing Theorem for 3-connected planar graphs. The existence of these representations has widespread applications in theoretical computer science and mathematics; however, the algorithmic aspect has received relatively little attention. In this work, we present an algorithm based on convex optimization for computing a primal-dual circle packing representation of maximal planar graphs, i.e. triangulations. This in turn gives an algorithm for computing a circle packing representation of any planar graph. Both take $\widetilde{O}(n \log(R/\varepsilon))$ expected run-time to produce a solution that is $\varepsilon$ close to a true representation, where $R$ is the ratio between the maximum and minimum circle radius in the true representation.
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