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72
A Decomposition of MultiDimensional Point Sets with Applications to kNearestNeighbors and nBody Potential Fields
 J. ACM
, 1992
"... We define the notion of a wellseparated pair decomposition of points in ddimensional space. We then develop efficient sequential and parallel algorithms for computing such a decomposition. We apply the resulting decomposition to the efficient computation of knearest neighbors and nbody potential ..."
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Cited by 244 (4 self)
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We define the notion of a wellseparated pair decomposition of points in ddimensional space. We then develop efficient sequential and parallel algorithms for computing such a decomposition. We apply the resulting decomposition to the efficient computation of knearest neighbors and nbody potential fields.
Ambivalent Data Structures For Dynamic 2EdgeConnectivity And k Smallest Spanning Trees
 SIAM J. Comput
, 1991
"... . Ambivalent data structures are presented for several problems on undirected graphs. These data structures are used in finding the k smallest spanning trees of a weighted undirected graph in O(m log #(m, n) + min{k 3/2 ,km 1/2 }) time, where m is the number of edges and n the number of vertice ..."
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Cited by 83 (1 self)
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. Ambivalent data structures are presented for several problems on undirected graphs. These data structures are used in finding the k smallest spanning trees of a weighted undirected graph in O(m log #(m, n) + min{k 3/2 ,km 1/2 }) time, where m is the number of edges and n the number of vertices in the graph. The techniques are extended to find the k smallest spanning trees in an embedded planar graph in O(n + k(log n) 3 ) time. Ambivalent data structures are also used to dynamically maintain 2edgeconnectivity information. Edges and vertices can be inserted or deleted in O(m 1/2 ) time, and a query as to whether two vertices are in the same 2edgeconnected component can be answered in O(log n) time, where m and n are understood to be the current number of edges and vertices, respectively. Key words. analysis of algorithms, data structures, embedded planar graph, fully persistent data structures, k smallest spanning trees, minimum spanning tree, online updating, topology tr...
Parallel Algorithms with Optimal Speedup for Bounded Treewidth
 Proceedings 22nd International Colloquium on Automata, Languages and Programming
, 1995
"... We describe the first parallel algorithm with optimal speedup for constructing minimumwidth tree decompositions of graphs of bounded treewidth. On nvertex input graphs, the algorithm works in O((logn)^2) time using O(n) operations on the EREW PRAM. We also give faster parallel algorithms with opti ..."
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Cited by 33 (10 self)
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We describe the first parallel algorithm with optimal speedup for constructing minimumwidth tree decompositions of graphs of bounded treewidth. On nvertex input graphs, the algorithm works in O((logn)^2) time using O(n) operations on the EREW PRAM. We also give faster parallel algorithms with optimal speedup for the problem of deciding whether the treewidth of an input graph is bounded by a given constant and for a variety of problems on graphs of bounded treewidth, including all decision problems expressible in monadic secondorder logic. On nvertex input graphs, the algorithms use O(n) operations together with O(log n log n) time on the EREW PRAM, or O(log n) time on the CRCW PRAM.
Logarithmic Time Parallel Bayesian Inference
 Proc. 14th Conf. Uncertainty in Artificial Intelligence
, 1998
"... I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worstcase time complexity is O(logn) on a CREW PRAM (concurrentread, exclusivewrite parallel randomaccess machine) with n processors, for any constant number of evide ..."
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Cited by 30 (0 self)
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I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worstcase time complexity is O(logn) on a CREW PRAM (concurrentread, exclusivewrite parallel randomaccess machine) with n processors, for any constant number of evidence variables. For arbitrary networks, the time complexity is O(r 3w log n) for n processors, or O(w log n) for r 3w n processors, where r is the maximum range of any variable, and w is the induced width (the maximum clique size), after moralizing and triangulating the network. 1 INTRODUCTION Two key breakthroughs make representation of and reasoning with probabilities practical, and have led to a proliferation of related research within the artificial intelligence community. Bayesian networks exploit conditional independence to represent joint probability distributions compactly, and associated inference algorithms evaluate arbitrary conditional probabilities implied by the network r...
Visibility with a moving point of view
 Algorithmica
, 1994
"... We investigate 3d visibility problems in which the viewing position moves along a straight flightpath. Specifically we focus on two problems: determining the points along the flightpath at which the topology of the viewed scene changes, and answering rayshooting queries for rays with origin on the ..."
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Cited by 28 (1 self)
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We investigate 3d visibility problems in which the viewing position moves along a straight flightpath. Specifically we focus on two problems: determining the points along the flightpath at which the topology of the viewed scene changes, and answering rayshooting queries for rays with origin on the flightpath. Three progressively more specialized problems are considered: general scenes, terrains, and terrains with vertical flightpaths. 1.
Optimal Parallel AllNearestNeighbors Using the WellSeparated Pair Decomposition
 In Proc. 34th IEEE Symposium on Foundations of Computer Science
, 1993
"... We present an optimal parallel algorithm to construct the wellseparated pair decomposition of a point set P in ! d . We show how this leads to a deterministic optimal O(logn) time parallel algorithm for finding the knearestneighbors of each point in P , where k is a constant. We discuss severa ..."
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Cited by 27 (1 self)
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We present an optimal parallel algorithm to construct the wellseparated pair decomposition of a point set P in ! d . We show how this leads to a deterministic optimal O(logn) time parallel algorithm for finding the knearestneighbors of each point in P , where k is a constant. We discuss several additional applications of the wellseparated pair decomposition for which we can derive faster parallel algorithms. 1 Introduction In [4] we introduced the wellseparated pair decomposition of a set P of n points in ! d , and showed how to apply this decomposition to develop efficient parallel algorithms for two problems posed on multidimensional point sets. One of these applications led to the fastest known deterministic parallel algorithm for finding the knearestneighbors of each point in P using O(n) processors. The time required for this algorithm is \Theta(log 2 n), which is within a log n factor of optimal. In this paper, we close the gap by developing an optimal O(log n) ti...
Parallel Algorithms for Hierarchical Clustering and Applications to Split Decomposition and Parity Graph Recognition
 JOURNAL OF ALGORITHMS
, 1998
"... We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solve some algorithmic problems in graphs. This includes split decomposition. We show that efficient parallel split decomposition induces an efficient para ..."
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Cited by 24 (1 self)
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We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solve some algorithmic problems in graphs. This includes split decomposition. We show that efficient parallel split decomposition induces an efficient parallel parity graph recognition algorithm. This is a consequence of the result of [7] that parity graphs are exactly those graphs that can be split decomposed into cliques and bipartite graphs.
Parallel Implementation of Tree Skeletons
 Journal of Parallel and Distributed Computing
, 1995
"... Trees are a useful data type, but they are not routinely included in parallel programming systems because their irregular structure makes them seem hard to compute with efficiently. We present a method for constructing implementations of skeletons, highlevel homomorphic operations on trees, that ex ..."
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Cited by 18 (2 self)
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Trees are a useful data type, but they are not routinely included in parallel programming systems because their irregular structure makes them seem hard to compute with efficiently. We present a method for constructing implementations of skeletons, highlevel homomorphic operations on trees, that execute in parallel. In particular, we consider the case where the size of the tree is much larger than the the number of processors available, so that tree data must be partitioned. The approach uses the theory of categorical data types to derive implementation templates based on tree contraction. Many useful tree operations can be computed in time logarithmic in the size of their argument, on a wide range of parallel systems. 1 Contribution One common approach to generalpurpose parallel computation is based on packaging complex operations as templates, or skeletons [3, 12]. Skeletons encapsulate the control and data flow necessary to compute useful operations. This permits software to be...
Efficient Parallel Algorithms for Tree Accumulations
 Science of Computer Programming
, 1994
"... Accumulations are higherorder operations on structured objects; they leave the shape of an object unchanged, but replace elements of that object with accumulated information about other elements. Upwards and downwards accumulations on trees are two such operations; they form the basis of many tree ..."
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Cited by 18 (7 self)
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Accumulations are higherorder operations on structured objects; they leave the shape of an object unchanged, but replace elements of that object with accumulated information about other elements. Upwards and downwards accumulations on trees are two such operations; they form the basis of many tree algorithms. We present two Erew Pram algorithms for computing accumulations on trees taking O(log n) time on O(n= log n) processors, which is optimal.
Algorithms for Boolean Formula Evaluation and for Tree Contraction
 Arithmetic, Proof Theory and Computational Complexity
, 1991
"... This paper presents a new, simpler ALOGTIME algorithm for the Boolean sentence value problem (BSVP). Unlike prior work, this algorithm avoids the use of postfixlongeroperandfirst formulas. This paper also shows that treecontraction can be made ALOGTIME uniform. ..."
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Cited by 18 (0 self)
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This paper presents a new, simpler ALOGTIME algorithm for the Boolean sentence value problem (BSVP). Unlike prior work, this algorithm avoids the use of postfixlongeroperandfirst formulas. This paper also shows that treecontraction can be made ALOGTIME uniform.