Almost Shortest Paths with Near-Additive Error in Weighted Graphs

Michael Elkin, Yuval Gitlitz, Ofer Neiman
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引用次数: 14

Abstract

Let $G=(V,E,w)$ be a weighted undirected graph with $n$ vertices and $m$ edges, and fix a set of $s$ sources $S\subseteq V$. We study the problem of computing {\em almost shortest paths} (ASP) for all pairs in $S \times V$ in both classical centralized and parallel (PRAM) models of computation. Consider the regime of multiplicative approximation of $1+\epsilon$, for an arbitrarily small constant $\epsilon > 0$ . In this regime existing centralized algorithms require $\Omega(\min\{|E|s,n^\omega\})$ time, where $\omega < 2.372$ is the matrix multiplication exponent. Existing PRAM algorithms with polylogarithmic depth (aka time) require work $\Omega(\min\{|E|s,n^\omega\})$. Our centralized algorithm has running time $O((m+ ns)n^\rho)$, and its PRAM counterpart has polylogarithmic depth and work $O((m + ns)n^\rho)$, for an arbitrarily small constant $\rho > 0$. For a pair $(s,v) \in S\times V$, it provides a path of length $\hat{d}(s,v)$ that satisfies $\hat{d}(s,v) \le (1+\epsilon)d_G(s,v) + \beta \cdot W(s,v)$, where $W(s,v)$ is the weight of the heaviest edge on some shortest $s-v$ path. Hence our additive term depends linearly on a {\em local} maximum edge weight, as opposed to the global maximum edge weight in previous works. Finally, our $\beta = (1/\rho)^{O(1/\rho)}$. We also extend a centralized algorithm of Dor et al. \cite{DHZ00}. For a parameter $\kappa = 1,2,\ldots$, this algorithm provides for {\em unweighted} graphs a purely additive approximation of $2(\kappa -1)$ for {\em all pairs shortest paths} (APASP) in time $\tilde{O}(n^{2+1/\kappa})$. Within the same running time, our algorithm for {\em weighted} graphs provides a purely additive error of $2(\kappa - 1) W(u,v)$, for every vertex pair $(u,v) \in {V \choose 2}$, with $W(u,v)$ defined as above. On the way to these results we devise a suit of novel constructions of spanners, emulators and hopsets.
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加权图中具有近加性误差的几乎最短路径
设$G=(V,E,w)$为一个有$n$个顶点和$m$条边的加权无向图,并固定一组$s$个源$S\subseteq V$。我们研究了在经典的集中式和并行(PRAM)计算模型下{\em}$S \times V$中所有对的(ASP)的计算问题。考虑对于任意小的常数$\epsilon > 0$,乘以近似$1+\epsilon$的情形。在这种情况下,现有的集中式算法需要$\Omega(\min\{|E|s,n^\omega\})$时间,其中$\omega < 2.372$是矩阵乘法指数。现有的PRAM算法与多对数深度(即时间)需要工作$\Omega(\min\{|E|s,n^\omega\})$。我们的集中式算法的运行时间为$O((m+ ns)n^\rho)$,对于任意小的常数$\rho > 0$,其对应的PRAM具有多对数深度和工作$O((m + ns)n^\rho)$。对于一对$(s,v) \in S\times V$,它提供了一条长度为$\hat{d}(s,v)$的路径,满足$\hat{d}(s,v) \le (1+\epsilon)d_G(s,v) + \beta \cdot W(s,v)$,其中$W(s,v)$是某个最短$s-v$路径上最重边的权值。因此,我们的加性项线性依赖于{\em局部}最大边权,而不是以前的研究中的全局最大边权。最后,我们的$\beta = (1/\rho)^{O(1/\rho)}$。我们还扩展了Dor等人的集中式算法\cite{DHZ00}。对于参数$\kappa = 1,2,\ldots$,该算法为{\em未加权}图提供了时间$\tilde{O}(n^{2+1/\kappa})$中{\em所有对最短路径}(APASP)的$2(\kappa -1)$的纯加性近似。在相同的运行时间内,我们的{\em加权}图算法为每个顶点对$(u,v) \in {V \choose 2}$提供了一个纯加性误差$2(\kappa - 1) W(u,v)$,其中$W(u,v)$定义如上所述。在获得这些结果的过程中,我们设计了一套新的扳手,模拟器和hopsets结构。
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