Approximating the Maximum Independent Set of Convex Polygons with a Bounded Number of Directions
February 12, 2024 Β· Declared Dead Β· π International Symposium on Computational Geometry
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Fabrizio Grandoni, Edin HusiΔ, Mathieu Mari, Antoine Tinguely
arXiv ID
2402.07666
Category
cs.CG: Computational Geometry
Cross-listed
cs.DS
Citations
1
Venue
International Symposium on Computational Geometry
Last Checked
3 months ago
Abstract
In the maximum independent set of convex polygons problem, we are given a set of $n$ convex polygons in the plane with the objective of selecting a maximum cardinality subset of non-overlapping polygons. Here we study a special case of the problem where the edges of the polygons can take at most $d$ fixed directions. We present an $8d/3$-approximation algorithm for this problem running in time $O((nd)^{O(d4^d)})$. The previous-best polynomial-time approximation (for constant $d$) was a classical $n^\varepsilon$ approximation by Fox and Pach [SODA'11] that has recently been improved to a $OPT^{\varepsilon}$-approximation algorithm by Cslovjecsek, Pilipczuk and WΔgrzycki [SODA '24], which also extends to an arbitrary set of convex polygons. Our result builds on, and generalizes the recent constant factor approximation algorithms for the maximum independent set of axis-parallel rectangles problem (which is a special case of our problem with $d=2$) by Mitchell [FOCS'21] and GΓ‘lvez, Khan, Mari, MΓΆmke, Reddy, and Wiese [SODA'22].
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