Results 1  10
of
67
Applications of Random Sampling in Computational Geometry, II
 Discrete Comput. Geom
, 1995
"... We use random sampling for several new geometric algorithms. The algorithms are "Las Vegas," and their expected bounds are with respect to the random behavior of the algorithms. These algorithms follow from new general results giving sharp bounds for the use of random subsets in geometric ..."
Abstract

Cited by 452 (12 self)
 Add to MetaCart
We use random sampling for several new geometric algorithms. The algorithms are "Las Vegas," and their expected bounds are with respect to the random behavior of the algorithms. These algorithms follow from new general results giving sharp bounds for the use of random subsets in geometric algorithms. These bounds show that random subsets can be used optimally for divideandconquer, and also give bounds for a simple, general technique for building geometric structures incrementally. One new algorithm reports all the intersecting pairs of a set of line segments in the plane, and requires O(A + n log n) expected time, where A is the number of intersecting pairs reported. The algorithm requires O(n) space in the worst case. Another algorithm computes the convex hull of n points in E d in O(n log n) expected time for d = 3, and O(n bd=2c ) expected time for d ? 3. The algorithm also gives fast expected times for random input points. Another algorithm computes the diameter of a set of n...
Efficient algorithms for geometric optimization
 ACM Comput. Surv
, 1998
"... We review the recent progress in the design of efficient algorithms for various problems in geometric optimization. We present several techniques used to attack these problems, such as parametric searching, geometric alternatives to parametric searching, pruneandsearch techniques for linear progra ..."
Abstract

Cited by 117 (12 self)
 Add to MetaCart
We review the recent progress in the design of efficient algorithms for various problems in geometric optimization. We present several techniques used to attack these problems, such as parametric searching, geometric alternatives to parametric searching, pruneandsearch techniques for linear programming and related problems, and LPtype problems and their efficient solution. We then describe a variety of applications of these and other techniques to numerous problems in geometric optimization, including facility location, proximity problems, statistical estimators and metrology, placement and intersection of polygons and polyhedra, and ray shooting and other querytype problems.
Slowing down sorting networks to obtain faster sorting algorithms
 Journal of the ACM
, 1987
"... Abstract. Megiddo introduced a technique for using a parallel algorithm for one problem to construct an efftcient serial algorithm for a second problem. This paper provides a general method that trims a factor of O(log n) time (or more) for many applications of this technique. ..."
Abstract

Cited by 112 (0 self)
 Add to MetaCart
(Show Context)
Abstract. Megiddo introduced a technique for using a parallel algorithm for one problem to construct an efftcient serial algorithm for a second problem. This paper provides a general method that trims a factor of O(log n) time (or more) for many applications of this technique.
Improved Bounds for Planar kSets and Related Problems
, 1998
"... We prove an O.n.k C 1/1=3 / upper bound for planar ksets. This is the first considerable improvement on this bound after its early solution approximately 27 years ago. Our proof technique also applies to improve the current bounds on the combinatorial complexities of klevels in the arrangement o ..."
Abstract

Cited by 110 (1 self)
 Add to MetaCart
We prove an O.n.k C 1/1=3 / upper bound for planar ksets. This is the first considerable improvement on this bound after its early solution approximately 27 years ago. Our proof technique also applies to improve the current bounds on the combinatorial complexities of klevels in the arrangement of line segments, k convex polygons in the union of n lines, parametric minimum spanning trees, and parametric matroids in general.
Arrangements and Their Applications
 Handbook of Computational Geometry
, 1998
"... The arrangement of a finite collection of geometric objects is the decomposition of the space into connected cells induced by them. We survey combinatorial and algorithmic properties of arrangements of arcs in the plane and of surface patches in higher dimensions. We present many applications of arr ..."
Abstract

Cited by 89 (20 self)
 Add to MetaCart
(Show Context)
The arrangement of a finite collection of geometric objects is the decomposition of the space into connected cells induced by them. We survey combinatorial and algorithmic properties of arrangements of arcs in the plane and of surface patches in higher dimensions. We present many applications of arrangements to problems in motion planning, visualization, range searching, molecular modeling, and geometric optimization. Some results involving planar arrangements of arcs have been presented in a companion chapter in this book, and are extended in this chapter to higher dimensions. Work by P.A. was supported by Army Research Office MURI grant DAAH049610013, by a Sloan fellowship, by an NYI award, and by a grant from the U.S.Israeli Binational Science Foundation. Work by M.S. was supported by NSF Grants CCR9122103 and CCR9311127, by a MaxPlanck Research Award, and by grants from the U.S.Israeli Binational Science Foundation, the Israel Science Fund administered by the Israeli Ac...
Geometric Range Searching
, 1994
"... In geometric range searching, algorithmic problems of the following type are considered: Given an npoint set P in the plane, build a data structure so that, given a query triangle R, the number of points of P lying in R can be determined quickly. Problems of this type are of crucial importance in c ..."
Abstract

Cited by 54 (3 self)
 Add to MetaCart
(Show Context)
In geometric range searching, algorithmic problems of the following type are considered: Given an npoint set P in the plane, build a data structure so that, given a query triangle R, the number of points of P lying in R can be determined quickly. Problems of this type are of crucial importance in computational geometry, as they can be used as subroutines in many seemingly unrelated algorithms. We present a survey of results and main techniques in this area.
Approximating center points with iterative radon points
 Internat. J. Comput. Geom. Appl
, 1996
"... ..."
(Show Context)
Point Sets With Many KSets
, 1999
"... For any n, k, n 2k > 0, we construct a set of n points in the plane with ne p log k ksets. This improves the bounds of Erd}os, Lovasz, et al. As a consequence, we also improve the lower bound of Edelsbrunner for the number of halving hyperplanes in higher dimensions. 1 Introduction For ..."
Abstract

Cited by 49 (0 self)
 Add to MetaCart
For any n, k, n 2k > 0, we construct a set of n points in the plane with ne p log k ksets. This improves the bounds of Erd}os, Lovasz, et al. As a consequence, we also improve the lower bound of Edelsbrunner for the number of halving hyperplanes in higher dimensions. 1 Introduction For a set P of n points in the ddimensional space, a kset is subset P 0 P such that P 0 = P \H for some halfspace H, and jP 0 j = k. The problem is to determine the maximum number of ksets of an npoint set in the ddimensional space. Even in the most studied two dimensional case, we are very far from the solution, and in higher dimensions even much less is known. The rst results in the two dimensional case are due to Erd}os, Lovasz, Simmons and Straus [L71], [ELSS73]. They established an upper bound O(n p k), and a lower bound (n log k). Despite great interest in this problem [W86], [E87], [S91], [EVW97], [AACS98], partly due to its importance in the analysis of geometric alg...
Random Sampling, Halfspace Range Reporting, and Construction of (≤k)Levels in Three Dimensions
 SIAM J. COMPUT
, 1999
"... Given n points in three dimensions, we show how to answer halfspace range reporting queries in O(logn+k) expected time for an output size k. Our data structure can be preprocessed in optimal O(n log n) expected time. We apply this result to obtain the first optimal randomized algorithm for the co ..."
Abstract

Cited by 38 (8 self)
 Add to MetaCart
(Show Context)
Given n points in three dimensions, we show how to answer halfspace range reporting queries in O(logn+k) expected time for an output size k. Our data structure can be preprocessed in optimal O(n log n) expected time. We apply this result to obtain the first optimal randomized algorithm for the construction of the ( k)level in an arrangement of n planes in three dimensions. The algorithm runs in O(n log n+nk²) expected time. Our techniques are based on random sampling. Applications in two dimensions include an improved data structure for "k nearest neighbors" queries, and an algorithm that constructs the orderk Voronoi diagram in O(n log n + nk log k) expected time.