Distributed Minimum Degree Spanning Trees

M. Dinitz, M. Halldórsson, Taisuke Izumi, Calvin C. Newport
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引用次数: 8

Abstract

The minimum degree spanning tree (MDST) problem requires the construction of a spanning tree T for graph G, such that the maximum degree of T is the smallest among all spanning trees of G. Let d be this MDST degree for a given graph. In this paper, we present a randomized distributed approximation algorithm for the MDST problem that constructs a spanning tree with maximum degree in O(d+log n ). With high probability in n, the algorithm runs in O((D + √n) log4 n) rounds, in the broadcast-CONGEST model, where D is the graph diameter and n is the graph size. We then show how to derandomize this algorithm, obtaining the same asymptotic guarantees on degree and time complexity, but now requiring the standard CONGEST model. Although efficient approximation algorithms for the MDST problem have been known in the sequential setting since the 1990's (finding an exact solution is NP-hard), our algorithms are the first efficient distributed solutions. We conclude by proving a lower bound that establishes that any randomized MDST algorithm that guarantees a maximum degree in ∼Ω (d) requires &Ø#8764;Ω (n1/3) rounds, and any deterministic solution requires ∼Ω (n1/2) rounds. These bounds proves our deterministic algorithm to be asymptotically optimal, and eliminates the possibility of significantly more efficient randomized solutions.
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分布式最小度生成树
最小度生成树(MDST)问题要求为图G构造一棵生成树T,使得T的最大度是G的所有生成树中最小的。设d为给定图的这个MDST度。在本文中,我们提出了一种随机分布逼近算法,该算法在O(d+log n)内构造了一棵最大度的生成树。在broadcast-CONGEST模型中,算法以n为高概率运行O((D +√n) log4n)轮,其中D为图直径,n为图大小。然后,我们展示了如何对该算法进行非随机化,在程度和时间复杂度上获得相同的渐近保证,但现在需要标准的CONGEST模型。尽管自20世纪90年代以来,MDST问题的有效近似算法已经在顺序设置中被发现(找到精确解是np困难的),但我们的算法是第一个有效的分布式解。我们通过证明一个下界得出结论,该下界建立了任何保证在~ Ω (d)中获得最大度的随机MDST算法都需要&Ø#8764;Ω (n1/3)轮,而任何确定性解都需要~ Ω (n1/2)轮。这些边界证明了我们的确定性算法是渐近最优的,并且消除了显著更有效的随机解的可能性。
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