eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2020-06-29
64:1
64:17
10.4230/LIPIcs.ICALP.2020.64
article
Active Learning a Convex Body in Low Dimensions
Har-Peled, Sariel
1
Jones, Mitchell
1
Rahul, Saladi
2
Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA
Dept. of Computer Science and Automation, Indian Institute of Science, Bangalore, India
Consider a set P ⊆ ℝ^d of n points, and a convex body C provided via a separation oracle. The task at hand is to decide for each point of P if it is in C using the fewest number of oracle queries. We show that one can solve this problem in two and three dimensions using O(⬡_P log n) queries, where ⬡_P is the largest subset of points of P in convex position. In 2D, we provide an algorithm which efficiently generates these adaptive queries.
Furthermore, we show that in two dimensions one can solve this problem using O(⊚(P,C) log² n) oracle queries, where ⊚(P,C) is a lower bound on the minimum number of queries that any algorithm for this specific instance requires. Finally, we consider other variations on the problem, such as using the fewest number of queries to decide if C contains all points of P.
As an application of the above, we show that the discrete geometric median of a point set P in ℝ² can be computed in O(n log² n (log n log log n + ⬡(P))) expected time.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol168-icalp2020/LIPIcs.ICALP.2020.64/LIPIcs.ICALP.2020.64.pdf
Approximation algorithms
computational geometry
separation oracles
active learning