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arXiv 2606.26554cs.DS
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几乎最优的多源最短路径和可达性

Almost Optimal Multiple Source Shortest Paths and Reachability

University of California San Diego(加州大学圣地亚哥分校)

Barna Saha, Yinzhan Xu, Christopher Ye

AI总结:

针对无向无权图的多源最短路径和可达性问题,提出基于新颖图分解的算法,运行时间匹配布尔矩阵乘法的复杂度,实现从单源到全源的最优平滑过渡。

摘要:

给定一个图,从小顶点集到全图计算距离和可达性在理论和实践中都是重要的原语。在无向无权图中,虽然单源最短路径(SSSP)在稠密图中需要$O(n^2)$时间,但全源最短路径(APSP)可以在$\hat{O}(n^\omega) = O(n^{2.372})$时间内计算[Seidel '95],比分别运行$n$次SSSP实例节省了大量时间。然而,如果需要从$n^\sigma$个顶点计算多源最短路径(MSSP),先前已知的最佳运行时间是$\hat{O}(\min\{n^\omega, n^{2 + \sigma}\})$:要么计算APSP,要么从每个源运行SSSP。另一方面,MSSP只与计算一个$n^\sigma \times n$矩阵和一个$n \times n$矩阵的布尔矩阵乘法(BMM)一样难,这留下了显著的差距。我们的第一个主要结果是在无向无权图上运行时间为$\hat{O}(n^{\omega(\sigma, 1, 1)})$的几乎最优的MSSP算法,它在SSSP和APSP算法之间给出了平滑的插值。我们结果背后的主要技术工具是一种新颖的图分解,它可能具有独立的意义。接下来,我们研究多源可达性问题,其中我们需要确定给定有向图中一组$n^\sigma$个顶点是否能到达每个顶点。多源可达性也可以在$\hat{O}(\min\{n^\omega, n^{2 + \sigma}\})$时间内解决,并且具有与矩形BMM相同的下界。我们给出了一个最优算法,运行时间为$\hat{O}(n^{\omega(\sigma, 1, 1)})$,再次匹配BMM的运行时间。我们的多源可达性算法可以推广到DAG上的MSSP。作为一个应用,我们提供了一个$O(n^{2.084})$时间的算法来计算一个$\widetilde{O}(n)$大小的捷径集,将直径减少到$O(n^{1/3})$。

英文摘要:

Given a graph, computing distances and reachabilities from a small set of vertices to the whole graph is an important primitive both in theory and in practice. In undirected unweighted graphs, while computing single-source shortest path (SSSP) requires $O(n^2)$ time in dense graphs, all-pairs shortest paths (APSP) can be computed in $\hat{O}(n^ω) = O(n^{2.372})$ time [Seidel '95] providing significant savings over running $n$ SSSP instances separately. However, if one needs to compute multiple-source shortest paths (MSSP) from a set of $n^σ$ vertices, the previously best known running time was $\hat{O}(\min\{n^ω, n^{2 + σ}\})$: either compute APSP or run SSSP from each source. On the other hand, MSSP is only as hard as computing Boolean matrix product (BMM) between an $n^σ\times n$ matrix and $n \times n$ matrix, leaving a significant gap. Our first main result is an almost optimal algorithm for MSSP on undirected unweighted graphs running in $\hat{O}(n^{ω(σ, 1, 1)})$ time, which gives a smooth interpolation between the SSSP and APSP algorithms. The main technical tool behind our result is a novel graph decomposition, which may be of independent interest. Next, we study the multiple-source reachability problem, where we need to determine whether a given set of $n^σ$ vertices can reach each of the vertices in a given directed graph. Multiple-source reachability can also be solved in $\hat{O}(\min\{n^ω, n^{2 + σ}\})$ time, with the same lower bound from rectangular BMM. We give an optimal algorithm that runs in $\hat{O}(n^{ω(σ, 1, 1)})$ time, again matching the running time for BMM. Our algorithm for multiple-source reachability can be generalized to MSSP on DAGs. As an application, we provide an $O(n^{2.084})$ time algorithm for computing an $\widetilde{O}(n)$-size shortcut set that reduces diameter to $O(n^{1/3})$.

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