TY - GEN
T1 - Dynamic geometric data structures via shallow cuttings
AU - Chan, Timothy M.
N1 - Publisher Copyright:
© Timothy M. Chan.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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 n11/12 for (i) and (ii), n7/8 for (iii) and (iv), and n2/3 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(log2 n), and the amortized update time is O(log4 n) instead of O(log5 n) [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(log4 n) instead of O(log7 n) [Eppstein 1995; Chan, SODA 2006; Kaplan et al., SODA 2017].
AB - 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 n11/12 for (i) and (ii), n7/8 for (iii) and (iv), and n2/3 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(log2 n), and the amortized update time is O(log4 n) instead of O(log5 n) [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(log4 n) instead of O(log7 n) [Eppstein 1995; Chan, SODA 2006; Kaplan et al., SODA 2017].
KW - Closest pair
KW - Convex hulls
KW - Dynamic data structures
KW - Nearest neighbor search
KW - Shallow cuttings
UR - http://www.scopus.com/inward/record.url?scp=85066791494&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066791494&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.SoCG.2019.24
DO - 10.4230/LIPIcs.SoCG.2019.24
M3 - Conference contribution
AN - SCOPUS:85066791494
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 35th International Symposium on Computational Geometry, SoCG 2019
A2 - Barequet, Gill
A2 - Wang, Yusu
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 35th International Symposium on Computational Geometry, SoCG 2019
Y2 - 18 June 2019 through 21 June 2019
ER -