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A fast distributed approximation algorithm for minimum spanning trees
 IN PROCEEDINGS OF THE 20TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING (DISC
, 2006
"... We present a distributed algorithm that constructs an O(log n)approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time Õ(D(G) + L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our ..."
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Cited by 24 (7 self)
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We present a distributed algorithm that constructs an O(log n)approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time Õ(D(G) + L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our algorithm is existentially optimal (up to polylogarithmic factors), i.e., there exists graphs which need Ω(D(G) + L(G, w)) time to compute an Happroximation to the MST for any H ∈ [1, Θ(log n)]. Our result also shows that there can be a significant time gap between exact and approximate MST computation: there exists graphs in which the running time of our approximation algorithm is exponentially faster than the timeoptimal distributed algorithm that computes the MST. Finally, we show that our algorithm can be used to find an approximate MST in wireless networks and in random weighted networks in almost optimal Õ(D(G)) time.
Local MST Computation with Short Advice
 SPAA
, 2007
"... We use the recently introduced advising scheme framework for measuring the difficulty of locally distributively computing a Minimum Spanning Tree (MST). An (m, t)advising scheme for a distributed problem P is a way, for every possible input I of P, to provide an ”advice” (i.e., a bit string) about ..."
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Cited by 21 (10 self)
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We use the recently introduced advising scheme framework for measuring the difficulty of locally distributively computing a Minimum Spanning Tree (MST). An (m, t)advising scheme for a distributed problem P is a way, for every possible input I of P, to provide an ”advice” (i.e., a bit string) about I to each node so that: (1) the maximum size of the advices is at most m bits, and (2) the problem P can be solved distributively in at most t rounds using the advices as inputs. In case of MST, the output returned by each node of a weighted graph G is the edge leading to its parent in some rooted MST T of G. Clearly, there is a trivial (⌈log n⌉, 0)advising scheme for MST (each node is given the local port number of the edge leading to the root of some MST T), and it is known that any (0, t)advising scheme satisfies t ≥ ˜ Ω ( √ n). Our main result is the construction of an (O(1), O(log n))advising scheme for MST. That is, by only giving a constant number of bits of advice to each node, one can decrease exponentially the distributed computation time of MST in arbitrary graph, compared to algorithms dealing with the problem in absence of any a priori information. We also consider the average size of the advices. On the one hand, we show that any (m, 0)advising scheme for MST gives advices of average size Ω(log n). On the other hand we design an (m, 1)advising scheme for MST with advices of constant average size, that is one round is enough to decrease the average size of the advices from log n to constant.
A LatticeStructured Proof Technique Applied to a Minimum Spanning Tree Algorithm (Extended Abstract)
 Laboratory for Computer Science, Massachusetts Institute of Technology
, 1988
"... Jennifer Lundelius Welch Leslie Lamport Digital Equipment Corporation, Systems Research Center Abstract: rithms are often hard to prove correct because they have no natural decomposition into separately provable parts. This paper presents a proof technique for the modular verification of su ..."
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Cited by 12 (3 self)
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Jennifer Lundelius Welch Leslie Lamport Digital Equipment Corporation, Systems Research Center Abstract: rithms are often hard to prove correct because they have no natural decomposition into separately provable parts. This paper presents a proof technique for the modular verification of such nonmodular algorithms. It generalizes existing verification techniques based on a totallyordered hierarchy of refinements to allow a partiallyordered hierarchythat is; a lattice of different views of the algorithm. The technique is applied to the wellknown distributed minimum spanning tree algorithm of Gallager, Humblet and Spira, which has until recently lacked a rigorous proof. 1.
A Simple and Fast Distributed Algorithm to Compute a Minimum Spanning Tree in the Internet
 IN THE INTERNET. PROC. OF JOINT CONFERENCE ON INFORMATION SCIENCES '95
, 1995
"... ... this paper we propose a new fully distributed algorithm to build a minimum spanning tree (MST) in a generic communication network. During the execution, the algorithm maintains a collection of disjoint trees spanning all the group members. Every tree, which initially consists of only one node, i ..."
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Cited by 2 (0 self)
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... this paper we propose a new fully distributed algorithm to build a minimum spanning tree (MST) in a generic communication network. During the execution, the algorithm maintains a collection of disjoint trees spanning all the group members. Every tree, which initially consists of only one node, independently expands by joining the closest tree, until all the nodes are connected in a single tree. The resulting communication topology is both robust (there are no singularities subject to failures) and scalable (every node stores a limited amount of local information that is independent of the size of the network).
Learning automatabased algorithms for solving stochastic minimum spanning tree problem
 Applied Soft Computing
, 2011
"... Due to the hardness of solving the minimum spanning tree (MST) problem in stochastic environments, the stochastic MST (SMST) problem has not received the attention it merits, specifically when the probability distribution function (PDF) of the edge weight is not a priori known. In this paper, we fir ..."
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Cited by 2 (2 self)
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Due to the hardness of solving the minimum spanning tree (MST) problem in stochastic environments, the stochastic MST (SMST) problem has not received the attention it merits, specifically when the probability distribution function (PDF) of the edge weight is not a priori known. In this paper, we first propose a learning automata‐based sampling algorithm (Algorithm 1) to solve the MST problem in stochastic graphs where the PDF of the edge weight is assumed to be unknown. At each stage of the proposed algorithm, a set of learning automata is randomly activated and determines the graph edges that must be sampled in that stage. As the proposed algorithm proceeds, the sampling process focuses on the spanning tree with the minimum expected weight. Therefore, the proposed sampling method is capable of decreasing the rate of unnecessary samplings and shortening the time required for finding the SMST. The convergence of this algorithm is theoretically proved and it is shown that by a proper choice of the learning rate the spanning tree with the minimum expected weight can be found with a probability close enough to unity. Numerical results show that Algorithm 1 outperforms the standard sampling method. Selecting a proper learning rate is the most challenging issue in learning automata theory by which a good trade off can be achieved between the cost and efficiency of algorithm. To improve the efficiency (i.e., the convergence speed and convergence rate) of Algorithm 1, we