50,691 research outputs found

    A distributed approximation algorithm for the minimum degree minimum weight spanning trees

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    International audienceFischer has shown how to compute a minimum weight spanning tree of degree at most bΔ+logbnb \Delta^* + \lceil \log_b n\rceil in time O(n4+1/ ⁣lnb)O(n^{4 + 1/\!\ln b}) for any constant b>1b > 1, where Δ\Delta^* is the value of an optimal solution and nn is the number of nodes in the network. In this paper, we propose a distributed version of Fischer's algorithm that requires messages and time complexity O(n2+1/ ⁣lnb)O(n^{2 + 1/\!\ln b}), and O(n)O(n) space per node

    Polynomial-Time Space-Optimal Silent Self-Stabilizing Minimum-Degree Spanning Tree Construction

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    Motivated by applications to sensor networks, as well as to many other areas, this paper studies the construction of minimum-degree spanning trees. We consider the classical node-register state model, with a weakly fair scheduler, and we present a space-optimal \emph{silent} self-stabilizing construction of minimum-degree spanning trees in this model. Computing a spanning tree with minimum degree is NP-hard. Therefore, we actually focus on constructing a spanning tree whose degree is within one from the optimal. Our algorithm uses registers on O(logn)O(\log n) bits, converges in a polynomial number of rounds, and performs polynomial-time computation at each node. Specifically, the algorithm constructs and stabilizes on a special class of spanning trees, with degree at most OPT+1OPT+1. Indeed, we prove that, unless NP == coNP, there are no proof-labeling schemes involving polynomial-time computation at each node for the whole family of spanning trees with degree at most OPT+1OPT+1. Up to our knowledge, this is the first example of the design of a compact silent self-stabilizing algorithm constructing, and stabilizing on a subset of optimal solutions to a natural problem for which there are no time-efficient proof-labeling schemes. On our way to design our algorithm, we establish a set of independent results that may have interest on their own. In particular, we describe a new space-optimal silent self-stabilizing spanning tree construction, stabilizing on \emph{any} spanning tree, in O(n)O(n) rounds, and using just \emph{one} additional bit compared to the size of the labels used to certify trees. We also design a silent loop-free self-stabilizing algorithm for transforming a tree into another tree. Last but not least, we provide a silent self-stabilizing algorithm for computing and certifying the labels of a NCA-labeling scheme

    Universal Loop-Free Super-Stabilization

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    We propose an univesal scheme to design loop-free and super-stabilizing protocols for constructing spanning trees optimizing any tree metrics (not only those that are isomorphic to a shortest path tree). Our scheme combines a novel super-stabilizing loop-free BFS with an existing self-stabilizing spanning tree that optimizes a given metric. The composition result preserves the best properties of both worlds: super-stabilization, loop-freedom, and optimization of the original metric without any stabilization time penalty. As case study we apply our composition mechanism to two well known metric-dependent spanning trees: the maximum-flow tree and the minimum degree spanning tree

    Distributed Edge Connectivity in Sublinear Time

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    We present the first sublinear-time algorithm for a distributed message-passing network sto compute its edge connectivity λ\lambda exactly in the CONGEST model, as long as there are no parallel edges. Our algorithm takes O~(n11/353D1/353+n11/706)\tilde O(n^{1-1/353}D^{1/353}+n^{1-1/706}) time to compute λ\lambda and a cut of cardinality λ\lambda with high probability, where nn and DD are the number of nodes and the diameter of the network, respectively, and O~\tilde O hides polylogarithmic factors. This running time is sublinear in nn (i.e. O~(n1ϵ)\tilde O(n^{1-\epsilon})) whenever DD is. Previous sublinear-time distributed algorithms can solve this problem either (i) exactly only when λ=O(n1/8ϵ)\lambda=O(n^{1/8-\epsilon}) [Thurimella PODC'95; Pritchard, Thurimella, ACM Trans. Algorithms'11; Nanongkai, Su, DISC'14] or (ii) approximately [Ghaffari, Kuhn, DISC'13; Nanongkai, Su, DISC'14]. To achieve this we develop and combine several new techniques. First, we design the first distributed algorithm that can compute a kk-edge connectivity certificate for any k=O(n1ϵ)k=O(n^{1-\epsilon}) in time O~(nk+D)\tilde O(\sqrt{nk}+D). Second, we show that by combining the recent distributed expander decomposition technique of [Chang, Pettie, Zhang, SODA'19] with techniques from the sequential deterministic edge connectivity algorithm of [Kawarabayashi, Thorup, STOC'15], we can decompose the network into a sublinear number of clusters with small average diameter and without any mincut separating a cluster (except the `trivial' ones). Finally, by extending the tree packing technique from [Karger STOC'96], we can find the minimum cut in time proportional to the number of components. As a byproduct of this technique, we obtain an O~(n)\tilde O(n)-time algorithm for computing exact minimum cut for weighted graphs.Comment: Accepted at 51st ACM Symposium on Theory of Computing (STOC 2019

    Brief Announcement: Faster Asynchronous MST and Low Diameter Tree Construction with Sublinear Communication

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    Building a spanning tree, minimum spanning tree (MST), and BFS tree in a distributed network are fundamental problems which are still not fully understood in terms of time and communication cost. The first work to succeed in computing a spanning tree with communication sublinear in the number of edges in an asynchronous CONGEST network appeared in DISC 2018. That algorithm which constructs an MST is sequential in the worst case; its running time is proportional to the total number of messages sent. Our paper matches its message complexity but brings the running time down to linear in n. Our techniques can also be used to provide an asynchronous algorithm with sublinear communication to construct a tree in which the distance from a source to each node is within an additive term of sqrt{n} of its actual distance
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