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Voronoi diagrams  a survey of a fundamental geometric data structure
 ACM COMPUTING SURVEYS
, 1991
"... This paper presents a survey of the Voronoi diagram, one of the most fundamental data structures in computational geometry. It demonstrates the importance and usefulness of the Voronoi diagram in a wide variety of fields inside and outside computer science and surveys the history of its development. ..."
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Cited by 707 (5 self)
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This paper presents a survey of the Voronoi diagram, one of the most fundamental data structures in computational geometry. It demonstrates the importance and usefulness of the Voronoi diagram in a wide variety of fields inside and outside computer science and surveys the history of its development. The paper puts particular emphasis on the unified exposition of its mathematical and algorithmic properties. Finally, the paper provides the first comprehensive bibliography on Voronoi diagrams and related structures.
Finding the k Smallest Spanning Trees
, 1992
"... We give improved solutions for the problem of generating the k smallest spanning trees in a graph and in the plane. Our algorithm for general graphs takes time O(m log #(m, n)+k 2 ); for planar graphs this bound can be improved to O(n + k 2 ). We also show that the k best spanning trees for a set of ..."
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Cited by 20 (2 self)
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We give improved solutions for the problem of generating the k smallest spanning trees in a graph and in the plane. Our algorithm for general graphs takes time O(m log #(m, n)+k 2 ); for planar graphs this bound can be improved to O(n + k 2 ). We also show that the k best spanning trees for a set of points in the plane can be computed in time O(min(k 2 n + n log n, k 2 + kn log(n/k))). The k best orthogonal spanning trees in the plane can be found in time O(n log n + kn log log(n/k)+k 2 ).
Splitting a Delaunay triangulation in linear time
 Algorithmica
"... Computing the Delaunay triangulation of n points is well known to have an Ω(n log n) lower bound. Researchers have attempted to break that bound in special cases where additional information is known. The Delaunay triangulation of the vertices of a convex polygon is such ..."
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Cited by 16 (3 self)
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Computing the Delaunay triangulation of n points is well known to have an Ω(n log n) lower bound. Researchers have attempted to break that bound in special cases where additional information is known. The Delaunay triangulation of the vertices of a convex polygon is such
Delaunay Triangulations in O(sort(n)) Time and More
"... We present several results about Delaunay triangulations (DTs) and convex hulls in transdichotomous and hereditary settings: (i) the DT of a planar point set can be computed in expected time O(sort(n)) on a word RAM, where sort(n) is the time to sort n numbers. We assume that the word RAM supports ..."
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Cited by 13 (5 self)
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We present several results about Delaunay triangulations (DTs) and convex hulls in transdichotomous and hereditary settings: (i) the DT of a planar point set can be computed in expected time O(sort(n)) on a word RAM, where sort(n) is the time to sort n numbers. We assume that the word RAM supports the shuffleoperation in constant time; (ii) if we know the ordering of a planar point set in x and in ydirection, its DT can be found by a randomized algebraic computation tree of expected linear depth; (iii) given a universe U of points in the plane, we construct a data structure D for Delaunay queries: for any P ⊆ U, D can find the DT of P in time O(P  log log U); (iv) given a universe U of points in 3space in general convex position, there is a data structure D for convex hull queries: for any P ⊆ U, D can find the convex hull of P in time O(P (log log U) 2); (v) given a convex polytope in 3space with n vertices which are colored with χ> 2 colors, we can split it into the convex hulls of the individual color classes in time O(n(log log n) 2). The results (i)–(iii) generalize to higher dimensions. We need a wide range of techniques. Most prominently, we describe a reduction from DTs to nearestneighbor graphs that relies on a new variant of randomized incremental constructions using dependent sampling.
Approximate Data Structures with Applications (Extended Abstract)
, 1994
"... In this paper we introduce the notion of approximate data structures, in which a small amount of error is tolerated in the output. Approximate data structures trade error of approximation for faster operation, leading to theoretical and practical speedups for a wide variety of algorithms. We give a ..."
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Cited by 12 (7 self)
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In this paper we introduce the notion of approximate data structures, in which a small amount of error is tolerated in the output. Approximate data structures trade error of approximation for faster operation, leading to theoretical and practical speedups for a wide variety of algorithms. We give approximate variants of the van Emde Boas data structure, which support the same dynamic operations as the standard van Emde Boas data structure [28, 20], except that answers to queries are approximate. The variants support all operations in constant time provided the error of approximation is 1/polylog(n), and in O(loglog n) time provided the error is 1/polynomial(n), for n elements in the data structure. We consider
On Enumerating and Selecting Distances
 Int. J. Comput. Geom. Appl
, 1999
"... Given an npoint set, the problems of enumerating the k closest pairs and selecting the kth smallest distance are revisited. For the enumeration problem, we give simpler randomized and deterministic algorithms with O(n log n + k) running time in any fixeddimensional Euclidean space. For the selec ..."
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Cited by 10 (3 self)
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Given an npoint set, the problems of enumerating the k closest pairs and selecting the kth smallest distance are revisited. For the enumeration problem, we give simpler randomized and deterministic algorithms with O(n log n + k) running time in any fixeddimensional Euclidean space. For the selection problem, we give a randomized algorithm with running time O(n log n + n 2=3 k 1=3 log 5=3 n). We also describe outputsensitive results for halfspace range counting that are of use in more general distance selection problems. None of our algorithms requires parametric search. Keywords: distance enumeration, distance selection, closest pairs, range counting, randomized algorithms. 1 Introduction Finding the closest pair of an npoint set has a long history in computational geometry (see [34] for a nice survey). In the plane, the problem can be solved in O(n log n) time using the Delaunay triangulation. In an arbitrary fixed dimension d, the first O(n log n) algorithm, based on di...
Computing Hereditary Convex Structures
 SCG'09
, 2009
"... Color red and blue the n vertices of a convex polytope P in R³. Can we compute the convex hull of each color class in o(n log n)? What if we have χ> 2 colors? What if the colors are random? Consider an arbitrary query halfspace and call the vertices of P inside it blue: can the convex hull of the ..."
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Cited by 9 (6 self)
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Color red and blue the n vertices of a convex polytope P in R³. Can we compute the convex hull of each color class in o(n log n)? What if we have χ> 2 colors? What if the colors are random? Consider an arbitrary query halfspace and call the vertices of P inside it blue: can the convex hull of the blue points be computed in time linear in their number? More generally, can we quickly compute the blue hull without looking at the whole polytope? This paper considers several instances of hereditary computation and provides new results for them. In particular, we resolve an eightyear old open problem by showing how to split a convex polytope in linear expected time.
Computational Geometry through the Information Lens
, 2007
"... revisits classic problems in computational geometry from the modern algorithmic ..."
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revisits classic problems in computational geometry from the modern algorithmic
Chapter 22 Approximate Data Structures with Applications
"... In this paper we introduce the notion of approximate da2a siruclures, in which a small amount of error is tolerated in the output. Approximate data structures trade error of approximation for faster operation, leading to theoretical and practical speedups for a wide variety of algorithms. We give ..."
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In this paper we introduce the notion of approximate da2a siruclures, in which a small amount of error is tolerated in the output. Approximate data structures trade error of approximation for faster operation, leading to theoretical and practical speedups for a wide variety of algorithms. We give approximate variants of the van Emde Boas data structure, which support the same dynamic operations as the standard van Emde Boas data structure [28, 201, except that answers to queries are approximate. The variants support all operations in constant time provided the error of approximation is l/polylog(n), and in O(loglog n) time provided the error is l/polynomial(n), for n elements in the data structure. We consider the tolerance of prototypical algorithms to approximate data structures. We study in particular Prim’s minimumspanning tree algorithm, Dijkstra’s singlesource shortest paths algorithm, and an online variant of Graham’s convex hull algorithm. To obtain output which approximates the desired output with the error of approximation tending to zero, Prim’s algorithm requires only linear time, Dijkstra’s algorithm requires O(mloglogn) time, and the online variant of Graham’s algorithm requires constant amortized time per operation.
Point Location in Ó ÐÓ � Ò Time, Voronoi Diagrams in Ó Ò ÐÓ � Ò Time, and Other Transdichotomous Results in Computational Geometry
"... Given Ò points in the plane with integer coordinates bounded by Í � Û, we show that the Voronoi diagram can be constructed in Ç Ñ�Ò�Ò ÐÓ � Ò � ÐÓ � ÐÓ � Ò � Ò Ô ÐÓ � Í� expected time by a randomized algorithm on the unitcost RAM with word size Û. Similar results are also obtained for many other fun ..."
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Given Ò points in the plane with integer coordinates bounded by Í � Û, we show that the Voronoi diagram can be constructed in Ç Ñ�Ò�Ò ÐÓ � Ò � ÐÓ � ÐÓ � Ò � Ò Ô ÐÓ � Í� expected time by a randomized algorithm on the unitcost RAM with word size Û. Similar results are also obtained for many other fundamental problems in computational geometry, such as constructing the convex hull of adimensional point set, computing the Euclidean minimum spanning tree of a planar point set, triangulating a polygon with holes, and finding intersections among a set of line segments. These are the first results to beat the ª Ò ÐÓ � Ò algebraicdecisiontree lower bounds known for these problems. The results are all derived from a new twodimensional version of fusion trees that can answer point location queries in Ç Ñ�Ò�ÐÓ � Ò � ÐÓ � ÐÓ � Ò � Ô ÐÓ � Í � time with linear space. Higherdimensional extensions and applications are also mentioned in the paper. 1.