38,539 research outputs found

    A linear algorithm for the cutting center of a tree

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    As a measure of the extent to which the removal of a node disconnects a graph, the cutting number c(v) of a node v in a connected graph G has been defined to be the number of pairs of nodes in different components of G-{v}. We present a linear algorithm for determining c(v) for all nodes of a tree, and hence for identifying the cutting center, which consists of the nodes v at which c(v) is maximized.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25975/1/0000041.pd

    Output-Sensitive Tools for Range Searching in Higher Dimensions

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    Let PP be a set of nn points in Rd{\mathbb R}^{d}. A point pPp \in P is kk\emph{-shallow} if it lies in a halfspace which contains at most kk points of PP (including pp). We show that if all points of PP are kk-shallow, then PP can be partitioned into Θ(n/k)\Theta(n/k) subsets, so that any hyperplane crosses at most O((n/k)11/(d1)log2/(d1)(n/k))O((n/k)^{1-1/(d-1)} \log^{2/(d-1)}(n/k)) subsets. Given such a partition, we can apply the standard construction of a spanning tree with small crossing number within each subset, to obtain a spanning tree for the point set PP, with crossing number O(n11/(d1)k1/d(d1)log2/(d1)(n/k))O(n^{1-1/(d-1)}k^{1/d(d-1)} \log^{2/(d-1)}(n/k)). This allows us to extend the construction of Har-Peled and Sharir \cite{hs11} to three and higher dimensions, to obtain, for any set of nn points in Rd{\mathbb R}^{d} (without the shallowness assumption), a spanning tree TT with {\em small relative crossing number}. That is, any hyperplane which contains wn/2w \leq n/2 points of PP on one side, crosses O(n11/(d1)w1/d(d1)log2/(d1)(n/w))O(n^{1-1/(d-1)}w^{1/d(d-1)} \log^{2/(d-1)}(n/w)) edges of TT. Using a similar mechanism, we also obtain a data structure for halfspace range counting, which uses O(nloglogn)O(n \log \log n) space (and somewhat higher preprocessing cost), and answers a query in time O(n11/(d1)k1/d(d1)(log(n/k))O(1))O(n^{1-1/(d-1)}k^{1/d(d-1)} (\log (n/k))^{O(1)}), where kk is the output size

    Minimum Weight Perfect Matching via Blossom Belief Propagation

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    Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM). It has been shown that BP can solve a number of combinatorial optimization problems including minimum weight matching, shortest path, network flow and vertex cover under the following common assumption: the respective Linear Programming (LP) relaxation is tight, i.e., no integrality gap is present. However, when LP shows an integrality gap, no model has been known which can be solved systematically via sequential applications of BP. In this paper, we develop the first such algorithm, coined Blossom-BP, for solving the minimum weight matching problem over arbitrary graphs. Each step of the sequential algorithm requires applying BP over a modified graph constructed by contractions and expansions of blossoms, i.e., odd sets of vertices. Our scheme guarantees termination in O(n^2) of BP runs, where n is the number of vertices in the original graph. In essence, the Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom algorithm by jumping at once over many sub-steps with a single BP. Moreover, our result provides an interpretation of the Edmonds' algorithm as a sequence of LPs

    Low-Degree Spanning Trees of Small Weight

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    The degree-d spanning tree problem asks for a minimum-weight spanning tree in which the degree of each vertex is at most d. When d=2 the problem is TSP, and in this case, the well-known Christofides algorithm provides a 1.5-approximation algorithm (assuming the edge weights satisfy the triangle inequality). In 1984, Christos Papadimitriou and Umesh Vazirani posed the challenge of finding an algorithm with performance guarantee less than 2 for Euclidean graphs (points in R^n) and d > 2. This paper gives the first answer to that challenge, presenting an algorithm to compute a degree-3 spanning tree of cost at most 5/3 times the MST. For points in the plane, the ratio improves to 3/2 and the algorithm can also find a degree-4 spanning tree of cost at most 5/4 times the MST.Comment: conference version in Symposium on Theory of Computing (1994
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