2026-07-24T06:45:45Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:10428 2024-03-06T10:45:58Z ddc:004 open_access

Dynamic Geometric Data Structures via Shallow Cuttings Chan, Timothy M. dynamic data structures convex hulls nearest neighbor search closest pair shallow cuttings We present new results on a number of fundamental problems about dynamic geometric data structures: 1) We describe the first fully dynamic data structures with sublinear amortized update time for maintaining (i) the number of vertices or the volume of the convex hull of a 3D point set, (ii) the largest empty circle for a 2D point set, (iii) the Hausdorff distance between two 2D point sets, (iv) the discrete 1-center of a 2D point set, (v) the number of maximal (i.e., skyline) points in a 3D point set. The update times are near n^{11/12} for (i) and (ii), n^{7/8} for (iii) and (iv), and n^{2/3} for (v). Previously, sublinear bounds were known only for restricted "semi-online" settings [Chan, SODA 2002]. 2) We slightly improve previous fully dynamic data structures for answering extreme point queries for the convex hull of a 3D point set and nearest neighbor search for a 2D point set. The query time is O(log^2n), and the amortized update time is O(log^4n) instead of O(log^5n) [Chan, SODA 2006; Kaplan et al., SODA 2017]. 3) We also improve previous fully dynamic data structures for maintaining the bichromatic closest pair between two 2D point sets and the diameter of a 2D point set. The amortized update time is O(log^4n) instead of O(log^7n) [Eppstein 1995; Chan, SODA 2006; Kaplan et al., SODA 2017]. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Timothy M. Chan 2019 Is Part Of LIPIcs, Volume 129, 35th International Symposium on Computational Geometry (SoCG 2019) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2019.24 urn:nbn:de:0030-drops-104288 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2019.24 eng https://creativecommons.org/licenses/by/3.0/legalcode