Results 1  10
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15
Spanning Trees Short Or Small
 SIAM JOURNAL ON DISCRETE MATHEMATICS
"... We study the problem of finding small trees. Classical network design problems are considered with the additional constraint that only a specified number k of nodes are required to be connected in the solution. A prototypical example is the kMST problem in which we require a tree of minimum weight s ..."
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Cited by 66 (2 self)
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We study the problem of finding small trees. Classical network design problems are considered with the additional constraint that only a specified number k of nodes are required to be connected in the solution. A prototypical example is the kMST problem in which we require a tree of minimum weight spanning at least k nodes in an edgeweighted graph. We show that the kMST problem is NPhard even for points in the Euclidean plane. We provide approximation algorithms with performance ratio 2 p k for the general edgeweighted case and O(k 1=4 ) for the case of points in the plane. Polynomialtime exact solutions are also presented for the class of treewidthbounded graphs which includes trees, seriesparallel graphs, and bounded bandwidth graphs, and for points on the boundary of a convex region in the Euclidean plane. We also investigate the problem of finding short trees, and more generally, that of finding networks with minimum diameter. A simple technique is used to prov...
ClosestPoint Problems in Computational Geometry
, 1997
"... This is the preliminary version of a chapter that will appear in the Handbook on Computational Geometry, edited by J.R. Sack and J. Urrutia. A comprehensive overview is given of algorithms and data structures for proximity problems on point sets in IR D . In particular, the closest pair problem, th ..."
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Cited by 65 (14 self)
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This is the preliminary version of a chapter that will appear in the Handbook on Computational Geometry, edited by J.R. Sack and J. Urrutia. A comprehensive overview is given of algorithms and data structures for proximity problems on point sets in IR D . In particular, the closest pair problem, the exact and approximate postoffice problem, and the problem of constructing spanners are discussed in detail. Contents 1 Introduction 1 2 The static closest pair problem 4 2.1 Preliminary remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Algorithms that are optimal in the algebraic computation tree model . 5 2.2.1 An algorithm based on the Voronoi diagram . . . . . . . . . . . 5 2.2.2 A divideandconquer algorithm . . . . . . . . . . . . . . . . . . 5 2.2.3 A plane sweep algorithm . . . . . . . . . . . . . . . . . . . . . . 6 2.3 A deterministic algorithm that uses indirect addressing . . . . . . . . . 7 2.3.1 The degraded grid . . . . . . . . . . . . . . . . . . ...
Iterated Nearest Neighbors and Finding Minimal Polytopes
, 1994
"... Weintroduce a new method for finding several types of optimal kpoint sets, minimizing perimeter, diameter, circumradius, and related measures, by testing sets of the O(k) nearest neighbors to each point. We argue that this is better in a number of ways than previous algorithms, whichwere based o ..."
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Cited by 56 (6 self)
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Weintroduce a new method for finding several types of optimal kpoint sets, minimizing perimeter, diameter, circumradius, and related measures, by testing sets of the O(k) nearest neighbors to each point. We argue that this is better in a number of ways than previous algorithms, whichwere based on high order Voronoi diagrams. Our technique allows us for the first time to efficiently maintain minimal sets as new points are inserted, to generalize our algorithms to higher dimensions, to find minimal convex kvertex polygons and polytopes, and to improvemany previous results. Weachievemany of our results via a new algorithm for finding rectilinear nearest neighbors in the plane in time O(n log n+kn). We also demonstrate a related technique for finding minimum area kpoint sets in the plane, based on testing sets of nearest vertical neighbors to each line segment determined by a pair of points. A generalization of this technique also allows us to find minimum volume and boundary measure sets in arbitrary dimensions.
Finding Minimum Area kgons
 DISCRETE & COMPUTATIONAL GEOMETRY
, 1992
"... Given a set P of n points in the plane and a number k, we want to find a polygon ~ with vertices in P of minimum area that satisfies one of the following properties: (1) cK is a convex kgon, (2) ~ is an empty convex kgon, or (3) ~ is the convex hull of exactly k points of P. We give algorithms ..."
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Cited by 20 (4 self)
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Given a set P of n points in the plane and a number k, we want to find a polygon ~ with vertices in P of minimum area that satisfies one of the following properties: (1) cK is a convex kgon, (2) ~ is an empty convex kgon, or (3) ~ is the convex hull of exactly k points of P. We give algorithms for solving each of these three problems in time O(kn3). The space complexity is O(n) for k = 4 and O(kn 2) for k> 5. The algorithms are based on a dynamic ptogramming approach. We generalize this approach to polygons with minimum perimeter, polygons with maximum perimeter or area, polygons containing the maximum or minimum number of points, polygons with minimum weight (for some weights added to vertices), etc., in similar time bounds.
Computational geometry  a survey
 IEEE TRANSACTIONS ON COMPUTERS
, 1984
"... We survey the state of the art of computational geometry, a discipline that deals with the complexity of geometric problems within the framework of the analysis ofalgorithms. This newly emerged area of activities has found numerous applications in various other disciplines, such as computeraided de ..."
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Cited by 19 (3 self)
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We survey the state of the art of computational geometry, a discipline that deals with the complexity of geometric problems within the framework of the analysis ofalgorithms. This newly emerged area of activities has found numerous applications in various other disciplines, such as computeraided design, computer graphics, operations research, pattern recognition, robotics, and statistics. Five major problem areasconvex hulls, intersections, searching, proximity, and combinatorial optimizationsare discussed. Seven algorithmic techniques incremental construction, planesweep, locus, divideandconquer, geometric transformation, pruneandsearch, and dynamizationare each illustrated with an example.Acollection of problem transformations to establish lower bounds for geometric problems in the algebraic computation/decision model is also included.
New Algorithms for Minimum Area kgons
, 1991
"... Given a set P of n points in the plane, we wish to find a set Q P of k points for which the convex hull conv(Q) has the minimum area. ..."
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Cited by 9 (1 self)
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Given a set P of n points in the plane, we wish to find a set Q P of k points for which the convex hull conv(Q) has the minimum area.
Smallest colorspanning objects
 IN PROC. 9TH ANNU. EUROPEAN SYMPOS. ALGORITHMS
, 2001
"... Motivated by questions in location planning, we show for a set of colored point sites in the plane how to compute the smallest— by perimeter or area—axisparallel rectangle and the narrowest strip enclosing at least one site of each color. ..."
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Cited by 4 (2 self)
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Motivated by questions in location planning, we show for a set of colored point sites in the plane how to compute the smallest— by perimeter or area—axisparallel rectangle and the narrowest strip enclosing at least one site of each color.
Efficient Parallel Algorithms for Geometric Clustering and Partitioning Problems
, 1994
"... We present efficient parallel algorithms for some geometric clustering and partitioning problems. Our algorithms run in the CREW PRAM model of parallel computation. Given a point set P of n points in two dimensions, the clustering problems are to find a kpoint subset such that some measure for ..."
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Cited by 3 (0 self)
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We present efficient parallel algorithms for some geometric clustering and partitioning problems. Our algorithms run in the CREW PRAM model of parallel computation. Given a point set P of n points in two dimensions, the clustering problems are to find a kpoint subset such that some measure for this subset is minimized. We consider the problems of finding a kpoint subset with minimum L1 perimeter and minimum L1 diameter. For the L1 perimeter case, our algorithm runs in O(log 2 n) time and O(n log 2 n + nk 2 log 2 k) work. For the L1 diameter case, our algorithm runs in O(log 2 n+ log 2 k log log k log k) time and O(n log 2 n) work. We consider partitioning problems of the following nature. Given a planar point set S (jSj = n), a measure acting on S and a pair of values 1 and 2 , does there exist a bipartition S = S 1 [S 2 such that (S 1 ) i for i = 1; 2? We consider several measures like diameter under L1 and L 1 metric; area, perimeter of the smallest...
Removing outliers to minimize area and perimeter
 In Proceedings of the 18th Canadian Conference on Computational Geometry (CCCG
, 2006
"... We consider the problem of removing c points from a set S of n points so that the resulting point set has the smallest possible convex hull. Our main result is an O � n � � � � � 4c c 2c (3c) + log n time algorithm that solves this problem when “smallest ” is taken to mean least area or least perim ..."
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Cited by 3 (3 self)
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We consider the problem of removing c points from a set S of n points so that the resulting point set has the smallest possible convex hull. Our main result is an O � n � � � � � 4c c 2c (3c) + log n time algorithm that solves this problem when “smallest ” is taken to mean least area or least perimeter. 1
ALGORITHMS FOR OPTIMAL OUTLIER REMOVAL ∗
"... Abstract. We consider the problem of removing c points from a set S of n points so that the remaining point set is optimal in some sense. Definitions of optimality we consider include having minimum diameter, having minimum area (perimeter) bounding box, having minimum area (perimeter) convex hull. ..."
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Cited by 2 (0 self)
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Abstract. We consider the problem of removing c points from a set S of n points so that the remaining point set is optimal in some sense. Definitions of optimality we consider include having minimum diameter, having minimum area (perimeter) bounding box, having minimum area (perimeter) convex hull. For constant values of c, all our algorithms run in O(n log n) time. 1