图直径和偏心率的逼近边界

A. Backurs, L. Roditty, Gilad Segal, V. V. Williams, Nicole Wein
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引用次数: 51

摘要

最重要的图形参数之一是直径,即任意两个顶点之间的最大距离。目前还没有已知的非常有效的精确计算直径的算法。因此,许多研究都致力于如何快速地逼近这个参数。Chechik等人[SODA 2014]表明,在Õ(m3/2)时间内,直径可以在3/2的乘法因子内近似。此外,Roditty和Vassilevska W. [STOC 13]表明,除非强指数时间假设(Strong Exponential Time Hypothesis, SETH)失效,否则没有O(n2−ε)时间算法可以在稀疏图中获得优于3/2的近似因子。因此,对于近似因子小于3/2的稀疏图,上述算法本质上是最优的。然而,在线性时间内,3/2近似是完全可能的。在这项工作中,我们有条件地排除了这种可能性,表明除非SETH失败,否则没有O(m3/2−ε)时间算法可以获得优于5/3的近似因子。另一组基本的图形参数是离心率。顶点v的偏心率是v到v的最远顶点之间的距离。Chechik等人[SODA 2014]表明,在Õ(m3/2)时间内,所有顶点的偏心率可以在5/3的因子内近似,Abboud等人[SODA 2016]表明,在稀疏图中,没有O(n2−ε)算法可以达到优于5/3的近似。我们通过证明在SETH下,没有O(m3/2−ε)算法能达到比9/5近似更好的结果,证明了5/3近似算法的运行时间也是最优的。我们还表明,没有任何近线性时间算法可以获得优于2的偏心距近似。这是解决近线性时间计算的细粒度复杂性的第一个下界。我们表明,我们的近线性时间算法的下界本质上是紧密的,通过给出一个算法,该算法在Õ(m/δ)时间内的2+δ因子内近似偏心,对于任何0<δ<1。这击败了Cairo等人[SODA 2016]中的所有偏心算法,并且是有向图中偏心的第一个常数因子近似。为了建立上述下界,我们研究了S-T直径问题:给定一个图和两个顶点子集S和T,输出S中的顶点和T中的顶点之间的最大距离。我们给出了新的算法并显示了作为所有其他硬度结果起点的紧密下界。我们的下界只适用于稀疏图。我们表明,对于密集图,存在S-T直径,直径和偏心的近线性时间算法,具有与Õ(m3/2)对应的几乎相同的近似保证,改进了最著名的密集图算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards tight approximation bounds for graph diameter and eccentricities
Among the most important graph parameters is the Diameter, the largest distance between any two vertices. There are no known very efficient algorithms for computing the Diameter exactly. Thus, much research has been devoted to how fast this parameter can be approximated. Chechik et al. [SODA 2014] showed that the diameter can be approximated within a multiplicative factor of 3/2 in Õ(m3/2) time. Furthermore, Roditty and Vassilevska W. [STOC 13] showed that unless the Strong Exponential Time Hypothesis (SETH) fails, no O(n2−ε) time algorithm can achieve an approximation factor better than 3/2 in sparse graphs. Thus the above algorithm is essentially optimal for sparse graphs for approximation factors less than 3/2. It was, however, completely plausible that a 3/2-approximation is possible in linear time. In this work we conditionally rule out such a possibility by showing that unless SETH fails no O(m3/2−ε) time algorithm can achieve an approximation factor better than 5/3. Another fundamental set of graph parameters are the Eccentricities. The Eccentricity of a vertex v is the distance between v and the farthest vertex from v. Chechik et al. [SODA 2014] showed that the Eccentricities of all vertices can be approximated within a factor of 5/3 in Õ(m3/2) time and Abboud et al. [SODA 2016] showed that no O(n2−ε) algorithm can achieve better than 5/3 approximation in sparse graphs. We show that the runtime of the 5/3 approximation algorithm is also optimal by proving that under SETH, there is no O(m3/2−ε) algorithm that achieves a better than 9/5 approximation. We also show that no near-linear time algorithm can achieve a better than 2 approximation for the Eccentricities. This is the first lower bound in fine-grained complexity that addresses near-linear time computation. We show that our lower bound for near-linear time algorithms is essentially tight by giving an algorithm that approximates Eccentricities within a 2+δ factor in Õ(m/δ) time for any 0<δ<1. This beats all Eccentricity algorithms in Cairo et al. [SODA 2016] and is the first constant factor approximation for Eccentricities in directed graphs. To establish the above lower bounds we study the S-T Diameter problem: Given a graph and two subsets S and T of vertices, output the largest distance between a vertex in S and a vertex in T. We give new algorithms and show tight lower bounds that serve as a starting point for all other hardness results. Our lower bounds apply only to sparse graphs. We show that for dense graphs, there are near-linear time algorithms for S-T Diameter, Diameter and Eccentricities, with almost the same approximation guarantees as their Õ(m3/2) counterparts, improving upon the best known algorithms for dense graphs.
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