Extraction Theorems With Small Extraction Numbers
Arjun Agarwal, Sayan Bandyapadhyay

TL;DR
This paper introduces Extraction Theorems for specific geometric object classes, establishing tight bounds on their extraction numbers and providing examples that demonstrate the bounds' optimality.
Contribution
It presents new Extraction Theorems with small, tight bounds for classes like intervals, segments, rays, and octants, advancing geometric combinatorics.
Findings
Established small bounds on extraction numbers for various geometric classes
Proved the tightness of bounds with matching lower bound examples
Extended the understanding of extraction properties in geometric objects
Abstract
In this work, we develop Extraction Theorems for classes of geometric objects with small extraction numbers. These classes include intervals, axis-parallel segments, axis-parallel rays, and octants. We investigate these classes of objects and prove small bounds on the extraction numbers. The tightness of these bounds is demonstrated by examples with matching lower bounds.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications
