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Discrete & Computational Geometry

, Volume 53, Issue 3, pp 489–513 | Cite as

On Constant Factors in Comparison-Based Geometric Algorithms and Data Structures

  • Timothy M. Chan
  • Patrick Lee
Article
  • 105 Downloads

Abstract

Many standard problems in computational geometry have been solved asymptotically optimally as far as comparison-based algorithms are concerned, but there has been little work focusing on improving the constant factors hidden in big-Oh bounds on the number of comparisons needed. In this paper, we consider orthogonal-type problems and present a number of results that achieve optimality in the constant factors of the leading terms, including
  • an algorithm for the 2D maxima problem that uses \(n\lg h + O(n\sqrt{\lg h})\) comparisons, where \(h\) denotes the output size;

  • a randomized algorithm for the 3D maxima problem that uses \(n\lg h + O(n\lg ^{2/3} h)\) expected number of comparisons;

  • a randomized algorithm for detecting intersections among a set of orthogonal line segments that uses \(n\lg n + O(n\sqrt{\lg n})\) expected number of comparisons;

  • a data structure for point location among 3D disjoint axis-parallel boxes that can answer queries in \((3/2)\lg n + O(\lg \lg n)\) comparisons;

  • a data structure for point location in a 3D box subdivision that can answer queries in \((4/3)\lg n + O(\sqrt{\lg n})\) comparisons.

Some of the results can be adapted to solve nonorthogonal problems, such as 2D convex hulls and general line segment intersection. Our algorithms and data structures use a variety of techniques, including Seidel and Adamy’s planar point location method, weighted binary search, and height-optimal BSP trees.

Keywords

Comparison-based algorithms Maxima Convex hull  Line segment intersection Point location Binary space partition 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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