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28
Separators for spherepackings and nearest neighbor graphs
 J. ACM
, 1997
"... Abstract. A collection of n balls in d dimensions forms a kply system if no point in the space is covered by more than k balls. We show that for every kply system �, there is a sphere S that intersects at most O(k 1/d n 1�1/d) balls of � and divides the remainder of � into two parts: those in the ..."
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Cited by 74 (7 self)
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Abstract. A collection of n balls in d dimensions forms a kply system if no point in the space is covered by more than k balls. We show that for every kply system �, there is a sphere S that intersects at most O(k 1/d n 1�1/d) balls of � and divides the remainder of � into two parts: those in the interior and those in the exterior of the sphere S, respectively, so that the larger part contains at most (1 � 1/(d � 2))n balls. This bound of O(k 1/d n 1�1/d) is the best possible in both n and k. We also present a simple randomized algorithm to find such a sphere in O(n) time. Our result implies that every knearest neighbor graphs of n points in d dimensions has a separator of size O(k 1/d n 1�1/d). In conjunction with a result of Koebe that every triangulated planar graph is isomorphic to the intersection graph of a diskpacking, our result not only gives a new geometric proof of the planar separator theorem of Lipton and Tarjan, but also generalizes it to higher dimensions. The separator algorithm can be used for point location and geometric divide and conquer in a fixed dimensional space.
Modular Operads
 COMPOSITIO MATH
, 1994
"... We develop a "higher genus" analogue of operads, which we call modular operads, in which graphs replace trees in the definition. We study a functor F on the category of modular operads, the Feynman transform, which generalizes Kontsevich's graph complexes and also the bar construction for operads. ..."
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Cited by 68 (5 self)
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We develop a "higher genus" analogue of operads, which we call modular operads, in which graphs replace trees in the definition. We study a functor F on the category of modular operads, the Feynman transform, which generalizes Kontsevich's graph complexes and also the bar construction for operads. We calculate the Euler characteristic of the Feynman transform, using the theory of symmetric functions: our formula is modelled on Wick's theorem. We give applications to the theory of moduli spaces of pointed algebraic curves.
Combinatorial preconditioners for sparse, symmetric, diagonally dominant linear systems
, 1996
"... ..."
Approximating Treewidth, Pathwidth, Frontsize, and Shortest Elimination Tree
, 1995
"... Various parameters of graphs connected to sparse matrix factorization and other applications can be approximated using an algorithm of Leighton et al. that finds vertex separators of graphs. The approximate values of the parameters, which include minimum front size, treewidth, pathwidth, and minimum ..."
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Cited by 52 (4 self)
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Various parameters of graphs connected to sparse matrix factorization and other applications can be approximated using an algorithm of Leighton et al. that finds vertex separators of graphs. The approximate values of the parameters, which include minimum front size, treewidth, pathwidth, and minimum elimination tree height, are no more than O(logn) (minimum front size and treewidth) and O(log^2 n) (pathwidth and minimum elimination tree height) times the optimal values. In addition, we show that unless P = NP there are no absolute approximation algorithms for any of the parameters.
The power range assignment problem in radio networks on the plane
 Proc. 17th Annual Symposium on Theoretical Aspects of Computer Science (STACS
, 2000
"... Abstract. Given a finite set S of points (i.e. the stations of a radio network) on the plane and a positive integer 1 ≤ h ≤ S  −1, the 2d Min h R. Assign. problem consists of assigning transmission ranges to the stations so as to minimize the total power consumption provided that the transmission ..."
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Cited by 37 (9 self)
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Abstract. Given a finite set S of points (i.e. the stations of a radio network) on the plane and a positive integer 1 ≤ h ≤ S  −1, the 2d Min h R. Assign. problem consists of assigning transmission ranges to the stations so as to minimize the total power consumption provided that the transmission ranges of the stations ensure the communication between any pair of stations in at most h hops. We provide a lower bound on the total power consumption opt h (S) yielded by an optimal range assignment for any instance (S, h) of2d Min h R. Assign., for any positive constant h>0. The lower bound is a function of S, h and the minimum distance over all the pairs of stations in S. Then, we derive a constructive upper bound for the same problem as a function of S, h and the maximum distance over all the pairs of stations in S (i.e. the diameter of S). Finally, by combining the above bounds, we obtain a polynomialtime approximation algorithm for 2d Min h R. Assign. restricted to wellspread instances, for any positive constant h. Previous results for this problem were known only in special 1dimensional configurations (i.e. when points are arranged on a line).
Greedy Heuristics and an Evolutionary Algorithm for the BoundedDiameter Minimum Spanning Tree Problem
 Proceedings of the 2003 ACM Symposium on Applied Computing
, 2003
"... bound D, the boundeddiameter minimum spanning tree problem seeks a spanning tree on G of lowest weight in which no path between two vertices contains more than D edges. This problem is NPhard for 4 1, where n is the number of vertices in G. An existing greedy heuristic for the problem, called ..."
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Cited by 35 (13 self)
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bound D, the boundeddiameter minimum spanning tree problem seeks a spanning tree on G of lowest weight in which no path between two vertices contains more than D edges. This problem is NPhard for 4 1, where n is the number of vertices in G. An existing greedy heuristic for the problem, called OTTC, is based on Prim's algorithm. OTTC usually yields poor results on instances in which the triangle inequality approximately holds; it always uses the lowestweight edges that it can, but such edges do not in general connect the interior nodes of lowweight boundeddiameter trees. A new randomized greedy heuristic builds a boundeddiameter spanning tree from its center vertex or vertices. It chooses each next vertex at random but attaches the vertex with the lowestweight eligible edge. This algorithm is faster than OTTC and yields substantially better solutions on Euclidean instances. An evolutionary algorithm encodes spanning trees as lists of their edges, augmented with their center vertices. It applies operators that maintain the diameter bound and always generate valid o#spring trees. These operators are e#cient, so the algorithm scales well to larger problem instances. On 25 Euclidean instances of up to 1 000 vertices, the EA improved substantially on solutions found by the randomized greedy heuristic.
Efficient Randomized Dictionary Matching Algorithms (Extended Abstract)
, 1992
"... The standard string matching problem involves finding all occurrences of a single pattern in a single text. While this approach works well in many application areas, there are some domains in which it is more appropriate to deal with dictionaries of patterns. A dictionary is a set of patterns; the ..."
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Cited by 18 (5 self)
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The standard string matching problem involves finding all occurrences of a single pattern in a single text. While this approach works well in many application areas, there are some domains in which it is more appropriate to deal with dictionaries of patterns. A dictionary is a set of patterns; the goal of dictionary matching is to find all dictionary patterns in a given text, simultaneously. In string matching, randomized algorithms have primarily made use of randomized hashing functions which convert strings into "signatures" or "finger prints". We explore the use of finger prints in conjunction with other randomized and deterministic techniques and data structures. We present several new algorithms for dictionary matching, along with parallel algorithms which are simpler of more efficient than previously known algorithms.
Centers of complex networks
 J Theor Biol
, 2003
"... Abstract. The central vertices in complex networks are of particular interest because they might play the role of organizational hubs. Here, we consider three different geometric centrality measures, eccentricity, status, and centroid value, that were originally used in the context of resource place ..."
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Cited by 14 (0 self)
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Abstract. The central vertices in complex networks are of particular interest because they might play the role of organizational hubs. Here, we consider three different geometric centrality measures, eccentricity, status, and centroid value, that were originally used in the context of resource placement problems. We show that these quantities lead to useful descriptions of the centers of biological networks which often, but not always, correlate with a purely local notion of centrality such as the vertex degree. We introduce the notion of local centers as local optima of a centrality value “landscape ” on a network and discuss briefly their role. 1 S. Wuchty, P.F. Stadler: Centers of Complex Networks 2
Moments of Inertia and Graph Separators
 JOURNAL OF COMBINATORIAL OPTIMIZATION, 1:79104
, 1997
"... ..."
The complexity of constructing evolutionary trees using experiments
, 2001
"... We present tight upper and lower bounds for the problem of constructing evolutionary trees in the experiment model. We describe an algorithm which constructs an evolutionary tree of n species in time O(nd log d n) using at most n⌈d/2⌉(log 2⌈d/2⌉−1 n+O(1)) experiments for d> 2, and at most n(log n+ ..."
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Cited by 8 (1 self)
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We present tight upper and lower bounds for the problem of constructing evolutionary trees in the experiment model. We describe an algorithm which constructs an evolutionary tree of n species in time O(nd log d n) using at most n⌈d/2⌉(log 2⌈d/2⌉−1 n+O(1)) experiments for d> 2, and at most n(log n+O(1)) experiments for d = 2, where d is the degree of the tree. This improves the previous best upper bound by a factor Θ(log d). For d = 2 the previously best algorithm with running time O(n log n) had a bound of 4n log n on the number of experiments. By an explicit adversary argument, we show an Ω(nd log d n) lower bound, matching our upper bounds and improving the previous best lower bound by a factor Θ(log d n). Central to our algorithm is the construction and maintenance of separator trees of small height, which may be of independent interest.