Distributed Edge Coloring and a Special Case of the Constructive Lovász Local Lemma

Yi-Jun Chang, Qizheng He, Wenzheng Li, S. Pettie, Jara Uitto
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引用次数: 19

Abstract

The complexity of distributed edge coloring depends heavily on the palette size as a function of the maximum degree Δ. In this article, we explore the complexity of edge coloring in the LOCAL model in different palette size regimes. Our results are as follows. Lower Bounds: First, we simplify the round elimination technique of Brandt et al. [16] and prove that (2Δ −2)-edge coloring requires Ω (logΔ log n) time with high probability and Ω (logΔ n) time deterministically, even on trees. Second, we show that a natural approach to computing (Δ +1)-edge colorings (Vizing’s theorem), namely, extending an arbitrary partial coloring by iteratively recoloring subgraphs, requires Ω (Δ log n) time. Upper Bounds on General Graphs: We give a randomized edge coloring algorithm that can use palette sizes as small as Δ + Õ(√Δ), which is a natural barrier for randomized approaches. The running time of our (1+ε)Δ-edge coloring algorithm is usually dominated by O(\log ε−1) calls to a distributed Lovász local lemma (LLL) algorithm. For example, using the Chung-Pettie-Su LLL algorithm, we compute a (1+ε)Δ-edge coloring in O(log n) time when ε ≥ (log3 Δ) / √ Δ , or O(logΔ n) + (log log n)3 + o(1) time when ε = Ω (1). When Δ is sublogarithmic in n the performance is improved with the Ghaffari-Harris-Kuhn LLL algorithm. Upper Bounds on Trees: We show that the Ω (logΔ log n) lower bound can be nearly matched on trees. To establish this result, we develop a new distributed Lovász local lemma algorithm for tree-structured dependency graphs, which arise naturally from O(1)-round probabilistic algorithms run on trees. Specifically, our (1+ε)Δ-edge coloring algorithm for trees takes O(log (1 / ε)) ⋅ max { log log n\ log log log n, loglog Δ log n} time when ε ≥ (log3 Δ) / √ Δ, or O(max { log log n\ log log log n, logΔ log n}) time when ε = Ω (1).
分布边着色及建设性Lovász局部引理的一个特例
分布式边缘着色的复杂性在很大程度上取决于调色板大小作为最大程度Δ的函数。在这篇文章中,我们探讨了在不同的调色板大小制度下的边缘上色在LOCAL模型中的复杂性。我们的结果如下。下界:首先,我们简化了Brandt等人[16]的轮消技术,并证明(2Δ−2)边着色高概率地需要Ω (logΔ log n)时间,确定性地需要Ω (logΔ n)时间,即使在树上也是如此。其次,我们证明了计算(Δ +1)边着色(Vizing定理)的自然方法,即通过迭代地重新着色子图来扩展任意部分着色,需要Ω (Δ log n)时间。一般图的上界:我们给出了一个随机化的边缘着色算法,它可以使用小到Δ + Õ(√Δ)的调色板大小,这是随机化方法的天然屏障。我们的(1+ε)Δ-edge着色算法的运行时间通常由O(\log ε−1)次调用分布式Lovász局部引理(LLL)算法支配。例如,使用chong - pettie - su LLL算法,当ε≥(log3 Δ) /√Δ时,我们在O(log n)时间内计算(1+ε)Δ-edge着色,或者当ε = Ω(1)时,我们在O(logΔ n) + (log log n)3 + O(1)时间内计算(1+ε)Δ-edge着色。当Δ是n的次对数时,使用Ghaffari-Harris-Kuhn LLL算法提高性能。树的上界:我们证明了Ω (logΔ log n)下界可以在树上几乎匹配。为了建立这一结果,我们为树结构依赖图开发了一个新的分布式Lovász局部引理算法,它自然地产生于运行在树上的O(1)轮概率算法。具体来说,我们的(1+ε)Δ-edge树着色算法在ε≥(log3 Δ) /√Δ时需要O(log (1 / ε))⋅max {loglog n\ loglog log n, loglog Δ log n}时间,或者当ε = Ω(1)时需要O(max {loglog n\ loglog log n, logΔ log n})时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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