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297
Computing the editdistance between unrooted ordered trees
 In Proceedings of the 6th annual European Symposium on Algorithms (ESA
, 1998
"... Abstract. An ordered tree is a tree in which each node’s incident edges are cyclically ordered; think of the tree as being embedded in the plane. Let A and B be two ordered trees. The edit distance between A and B is the minimum cost of a sequence of operations (contract an edge, uncontract an edge, ..."
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Cited by 80 (0 self)
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Abstract. An ordered tree is a tree in which each node’s incident edges are cyclically ordered; think of the tree as being embedded in the plane. Let A and B be two ordered trees. The edit distance between A and B is the minimum cost of a sequence of operations (contract an edge, uncontract an edge, modify the label of an edge) needed to transform A into B. WegiveanO(n 3 log n) algorithm to compute the edit distance between two ordered trees. 1
Using firstorder logic to reason about policies
 In Proc. 16th IEEE Computer Security Foundations Workshop (CSFW’03
, 2003
"... A policy describes the conditions under which an action is permitted or forbidden. We show that a fragment of (multisorted) firstorder logic can be used to represent and reason about policies. Because we use firstorder logic, policies have a clear syntax and semantics. We show that further restri ..."
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Cited by 78 (5 self)
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A policy describes the conditions under which an action is permitted or forbidden. We show that a fragment of (multisorted) firstorder logic can be used to represent and reason about policies. Because we use firstorder logic, policies have a clear syntax and semantics. We show that further restricting the fragment results in a language that is still quite expressive yet is also tractable. More precisely, questions about entailment, such as ‘May Alice access the file?’, can be answered in time that is a loworder polynomial (indeed, almost linear in some cases), as can questions about the consistency of policy sets.
Nearest Common Ancestors: A survey and a new distributed algorithm
, 2002
"... Several papers describe linear time algorithms to preprocess a tree, such that one can answer subsequent nearest common ancestor queries in constant time. Here, we survey these algorithms and related results. A common idea used by all the algorithms for the problem is that a solution for complete ba ..."
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Cited by 76 (11 self)
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Several papers describe linear time algorithms to preprocess a tree, such that one can answer subsequent nearest common ancestor queries in constant time. Here, we survey these algorithms and related results. A common idea used by all the algorithms for the problem is that a solution for complete balanced binary trees is straightforward. Furthermore, for complete balanced binary trees we can easily solve the problem in a distributed way by labeling the nodes of the tree such that from the labels of two nodes alone one can compute the label of their nearest common ancestor. Whether it is possible to distribute the data structure into short labels associated with the nodes is important for several applications such as routing. Therefore, related labeling problems have received a lot of attention recently.
Minimum Cuts in NearLinear Time
, 1999
"... We significantly improve known time bounds for solving the minimum cut problem on undirected graphs. We use a "semiduality" between minimum cuts and maximum spanning tree packings combined with our previously developed random sampling techniques. We give a randomized (Monte Carlo) algorit ..."
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Cited by 71 (10 self)
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We significantly improve known time bounds for solving the minimum cut problem on undirected graphs. We use a "semiduality" between minimum cuts and maximum spanning tree packings combined with our previously developed random sampling techniques. We give a randomized (Monte Carlo) algorithm that finds a minimum cut in an medge, nvertex graph with high probability in O(m log³ n) time. We also give a simpler randomized algorithm that finds all minimum cuts with high probability in O(n² log n) time. This variant has an optimal RNC parallelization. Both variants improve on the previous best time bound of O(n² log³ n). Other applications of the treepacking approach are new, nearly tight bounds on the number of near minimum cuts a graph may have and a new data structure for representing them in a spaceefficient manner.
A theory of parameterized pattern matching: algorithms and applications
 In Proc. 25th STOC
, 1993
"... ..."
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 . . . . . . . . . . . . . . . . . . ...
Scaling Algorithms for Network Problems
, 1985
"... This paper gives algorithms for network problems that work by scaling the numeric parameters. Assume all parameters are integers. Let n, m, and N denote the number of vertices, number of edges, and largest parameter of the network, respectively. A scaling algorithm for maximum weight matching on a b ..."
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Cited by 60 (2 self)
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This paper gives algorithms for network problems that work by scaling the numeric parameters. Assume all parameters are integers. Let n, m, and N denote the number of vertices, number of edges, and largest parameter of the network, respectively. A scaling algorithm for maximum weight matching on a bipartite graph runs in O(n3 % log N) time. For appropriate N this improves the traditional Hungarian method, whose most efftcient implementation is O(n(m + n log n)). The speedup results from finding augmenting paths in batches. The matching algorithm gives similar improvements for the following problems: singlesource shortest paths for arbitrary edge lengths (Bellman’s algorithm); maximum weight degreeconstrained subgraph; minimum cost flow on a cl network. Scaling gives a simple maximum value flow algorithm that matches the best known bound (Sleator and Tarjan’s algorithm) when log N = O(log n). Scaling also gives a good algorithm for shortest paths on a directed graph with nonnegative edge lengths (Dijkstra’s algorithm).
Optimal preprocessing for answering online product queries
, 1987
"... We examine the amount of preprocessing needed for answering certain online queries as fast as possible. We start with the following basic problem. Suppose we are given a semigroup (S,°). Let s 1,..., sn be elements of S. We want to answer online queries of the form, "What is the product s ..."
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Cited by 56 (3 self)
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We examine the amount of preprocessing needed for answering certain online queries as fast as possible. We start with the following basic problem. Suppose we are given a semigroup (S,°). Let s 1,..., sn be elements of S. We want to answer online queries of the form, &quot;What is the product s i°s i +1 ° j −1 j n 1... °s °s? &quot; for any give ≤ i ≤ j ≤ n. We show that a preprocessing of Θ(n λ(k,n)) time and space is both necessary and sufficient to answer each such query in at most k steps, for any fixed k. The function λ(k,.) is the inverse of a certain function at the �k /2 �−th level of the primitive recursive hierarchy. In case linear preprocessing is desired, we show that one can answer each such query in at most O (α(n)) steps and that this is best possible. The function α(n) is the inverse Ackermann function. We also consider the following extended problem. Let T be a tree with an element of S associated with each of its vertices. We want to answer online queries of the form, &quot; What is the product of the elements associated with the vertices along the path from u to v? &quot; for any pair of vertices u and v in T. We derive results, which are similar to the above, for the preprocessing needed for answering such queries. All our sequential preprocessing algorithms can be parallelized efficiently to give optimal parallel algorithms which run in O (logn) time on a CREW PRAM. These parallel algorithms are optimal in both running time and total number of operations.
Planar Separators and Parallel Polygon Triangulation
"... We show how to construct an O ( p n)separator decomposition of a planar graph G in O(n) time. Such a decomposition defines a binary tree where each node corresponds to a subgraph of G and stores an O ( p n)separator of that subgraph. We also show how to construct an O(n)way decomposition tree in ..."
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Cited by 52 (8 self)
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We show how to construct an O ( p n)separator decomposition of a planar graph G in O(n) time. Such a decomposition defines a binary tree where each node corresponds to a subgraph of G and stores an O ( p n)separator of that subgraph. We also show how to construct an O(n)way decomposition tree in parallel in O(log n) time so that each node corresponds to a subgraph of G and stores an O(n 1=2+)separator of that subgraph. We demonstrate the utility of such a separator decomposition by showing how it can be used in the design of a parallel algorithm for triangulating a simple polygon deterministically in O(log n) time using O(n = log n) processors on a CRCW PRAM.