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SpaceEfficient Algorithms for Computing the Convex Hull of a Simple Polygonal Line in Linear Time
"... We present spaceefficient algorithms for computing the convex hull of a simple polygonal line inplace, in linear time. It turns out that the problem is as hard as stable partition, i.e., if there were a truly simple solution then stable partition would also have a truly simple solution, and vice v ..."
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Cited by 15 (2 self)
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We present spaceefficient algorithms for computing the convex hull of a simple polygonal line inplace, in linear time. It turns out that the problem is as hard as stable partition, i.e., if there were a truly simple solution then stable partition would also have a truly simple solution, and vice versa. Nevertheless, we present a simple selfcontained solution that uses O(log n) space, and indicate how to improve it to O(1) space with the same techniques used for stable partition. If the points inside the convex hull can be discarded, then there is a truly simple solution that uses a single call to stable partition, and even that call can be spared if only extreme points are desired (and not their order). If the polygonal line is closed, then the problem admits a very simple solution which does not call for stable partitioning at all.
Linesegment intersection made inplace
, 2007
"... We present a spaceefficient algorithm for reporting all k intersections induced by a set of n line segments in the plane. Our algorithm is an inplace variant of Balaban’s algorithm and, in the worst case, runs in O(n log2 n+k) time using O(1) extra words of memory in addition to the space used f ..."
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Cited by 8 (2 self)
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We present a spaceefficient algorithm for reporting all k intersections induced by a set of n line segments in the plane. Our algorithm is an inplace variant of Balaban’s algorithm and, in the worst case, runs in O(n log2 n+k) time using O(1) extra words of memory in addition to the space used for the input to the algorithm.
Inplace algorithms for computing (layers of) maxima
 In: Proceedings of the 10th Scandinavian Workshop on Algorithm Theory (SWAT ’06
, 2006
"... Abstract. We describe spaceefficient algorithms for solving problems related to finding maxima among points in two and three dimensions. Our algorithms run in optimal O(n log n) time and occupy only constant extra space in addition to the space needed for representing the input. 1 ..."
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Cited by 6 (1 self)
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Abstract. We describe spaceefficient algorithms for solving problems related to finding maxima among points in two and three dimensions. Our algorithms run in optimal O(n log n) time and occupy only constant extra space in addition to the space needed for representing the input. 1
Speculative Parallelization of a Randomized Incremental Convex Hull Algorithm
 Proc. Int’l Workshop Computational Geometry and Applications
, 2004
"... Abstract. Finding the fastest algorithm to solve a problem is one of the main issues in Computational Geometry. Focusing only on worst case analysis or asymptotic computations leads to the development of complex data structures or hard to implement algorithms. Randomized algorithms appear in this sc ..."
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Cited by 5 (4 self)
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Abstract. Finding the fastest algorithm to solve a problem is one of the main issues in Computational Geometry. Focusing only on worst case analysis or asymptotic computations leads to the development of complex data structures or hard to implement algorithms. Randomized algorithms appear in this scenario as a very useful tool in order to obtain easier implementations within a good expected time bound. However, parallel implementations of these algorithms are hard to develop and require an indepth understanding of the language, the compiler and the underlying parallel computer architecture. In this paper we show how we can use speculative parallelization techniques to execute in parallel iterative algorithms such as randomized incremental constructions. In this paper we focus on the convex hull problem, and show that, using our speculative parallelization engine, the sequential algorithm can be automatically executed in parallel, obtaining speedups with as little as four processors, and reaching 5.15x speedup with 28 processors. 1
SpaceEfficient Algorithms for Klee’s Measure Problem
, 2005
"... We give spaceefficient geometric algorithms for three related problems. Given a set of n axisaligned rectangles in the plane, we calculate the area covered by the union of these rectangles (Klee’s measure problem) in O(n 3/2 log n) time with O(√n) extra space. If the input can be destroyed and the ..."
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Cited by 5 (0 self)
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We give spaceefficient geometric algorithms for three related problems. Given a set of n axisaligned rectangles in the plane, we calculate the area covered by the union of these rectangles (Klee’s measure problem) in O(n 3/2 log n) time with O(√n) extra space. If the input can be destroyed and there are no degenerate cases and input coordinates are all integers, we can solve Klee’s measure problem in O(n log² n) time with O(log² n) extra space. Given a set of n points in the plane, we find the axisaligned unit square that covers the maximum number of points in O(n log³ n) time with O(log² n) extra space.
MemoryConstrained Algorithms for Simple Polygons
, 2011
"... A constantworkspace algorithm has readonly access to an input array and may use only O(1) additional words of O(log n) bits, where n is the size of the input. We show that we can find a triangulation of a plane straightline graph with n vertices in O(n²) time. We also consider preprocessing a sim ..."
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Cited by 2 (2 self)
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A constantworkspace algorithm has readonly access to an input array and may use only O(1) additional words of O(log n) bits, where n is the size of the input. We show that we can find a triangulation of a plane straightline graph with n vertices in O(n²) time. We also consider preprocessing a simple ngon, which is given by the ordered sequence of its vertices, for shortest path queries when the space constraint is relaxed to allow s words of working space. After a preprocessing of O(n²) time, we are able to solve shortest path queries between any two points inside the polygon in O(n²/s) time.
InPlace 2d Nearest Neighbor Search
, 2007
"... Abstract We revisit a classic problem in computational geometry: preprocessing a planar npoint set to answer nearest neighbor queries. In SoCG 2004, Br"onnimann, Chan, and Chen showed that it is possible to design an efficient data structure that takes no extra space at all other than the inpu ..."
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Cited by 1 (1 self)
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Abstract We revisit a classic problem in computational geometry: preprocessing a planar npoint set to answer nearest neighbor queries. In SoCG 2004, Br"onnimann, Chan, and Chen showed that it is possible to design an efficient data structure that takes no extra space at all other than the input array holding a permutation of the points. The best query time known for such "inplace data structures " is O(log 2 n). In this paper, we break the O(log 2 n) barrier by providing a method that answers nearest neighbor queries in time O((log n) log3=2 2 log log n) = O(log
InPlace Randomized Slope Selection
"... Abstract. Slope selection is a wellknown algorithmic tool used in the context of computing robust estimators for fitting a line to a collection P of n points in the plane. We demonstrate that it is possible to perform slope selection in expected O(n log n) time using only constant extra space in ad ..."
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Abstract. Slope selection is a wellknown algorithmic tool used in the context of computing robust estimators for fitting a line to a collection P of n points in the plane. We demonstrate that it is possible to perform slope selection in expected O(n log n) time using only constant extra space in addition to the space needed for representing the input. Our solution is based upon a spaceefficient variant of Matouˇsek’s randomized interpolation search, and we believe that the techniques developed in this paper will prove helpful in the design of spaceefficient randomized algorithms using samples. To underline this, we also sketch how to compute the repeated median line estimator in an inplace setting. 1
Spaceefficient Algorithms for Empty Space Recognition among a Point Set in 2D and 3D
"... In this paper, we consider the problem of designing inplace algorithms for computing the maximum area empty rectangle of arbitrary orientation among a set of points in 2D, and the maximum volume empty axisparallel cuboid among a set of points in 3D. If n points are given in an array of size n, the ..."
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In this paper, we consider the problem of designing inplace algorithms for computing the maximum area empty rectangle of arbitrary orientation among a set of points in 2D, and the maximum volume empty axisparallel cuboid among a set of points in 3D. If n points are given in an array of size n, the worst case time complexity of our proposed algorithms for both the problems is O(n 3); both the algorithms use O(1) extra space in addition to the array containing the input points. 1
An InPlace MinMax Priority Search Tree ∗
"... One of the classic data structures for storing point sets in R 2 is the priority search tree, introduced by McCreight in 1985. We show that this data structure can be made inplace, i.e., it can be stored in an array such that each entry stores only one point of the point set and no entry is stored ..."
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One of the classic data structures for storing point sets in R 2 is the priority search tree, introduced by McCreight in 1985. We show that this data structure can be made inplace, i.e., it can be stored in an array such that each entry stores only one point of the point set and no entry is stored in more than one location of that array. It combines a binary search tree with a heap. We show that all the standard query operations can be answered within the same time bounds as for the original priority search tree, while using only O(1) extra space. We introduce the minmax priority search tree which is a combination of a binary search tree and a minmax heap. We show that all the standard queries which can be done in two separate versions of a priority search tree can be done with a single minmax priority search tree. As an application, we present an inplace algorithm to enumerate all maximal empty axisparallel rectangles amongst points in a rectangular region R in R 2 in O(m log n) time with O(1) extraspace, where m is the total number of maximal empty rectangles. 1