arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1504.08360cs.DS
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一种求递减有向图可达性的简单快速算法

Simple and Faster algorithm for Reachability in a Decremental Directed Graph

Manoj Gupta

摘要:

考虑边递减有向图中保持源-汇可达性($st$ -Reachability)、单源可达性(SSR)和强连通分量(SCC)的问题。特别地,我们设计了一种随机化算法,其以高概率保持:1)$st$ -在$\tilde{O}(mn^{4/5})$总更新时间中的可达性。2)$st$ -稠密图中$\tilde{O}(n^{8/3})$的总更新时间的可达性。3)SSR的总更新时间为$\tilde{O}(m n^{9/10})$。4)SCC的总更新时间为$\tilde{O}(m n^{9/10})$。针对上述问题,我们改进了以前的最佳算法(由Henzinger等。(STOC 2014))。我们主要关注的是在边递减有向图中维护$st$ -可达性(其他问题可以简化为$st$ -可达性)。Even和Shiloach(JACM 81)的经典算法在$O(1)$查询时间和$O(mn)$总更新时间上解决了这个问题。最近,Henzinger,Krinninger和Nanongkai(STOC 2014)设计了一种随机算法,该算法实现了$\tilde{O}(m n^{0.98})$的更新时间,并打破了Even和Shiloach长期存在的$O(mn)$界限。然而,他们设计了四种算法$A_i (1\le i \le 4)$,使得对于具有介于$m_i$和$m_{i+1}$之间的总边数($m_{i+1} > m_i$)的图,$A_i$优于其他三种算法。也就是说,四种算法中的一种对于边缘的特定密度范围可能更快,但对于其他范围可能渐近地太慢。我们的主要贡献是,我们设计了一个{ \it单}算法,适用于所有类型的图。我们的算法不仅更快,而且比Henzinger等人设计的算法简单得多(STOC 2014)。

英文摘要:

Consider the problem of maintaining source sink reachability($st$-Reachability), single source reachability(SSR) and strongly connected component(SCC) in an edge decremental directed graph. In particular, we design a randomized algorithm that maintains with high probability: 1) $st$-Reachability in $\tilde{O}(mn^{4/5})$ total update time. 2) $st$-Reachability in a total update time of $\tilde{O}(n^{8/3})$ in a dense graph. 3) SSR in a total update time of $\tilde{O}(m n^{9/10})$. 4) SCC in a total update time of $\tilde{O}(m n^{9/10})$. For all the above problems, we improve upon the previous best algorithm (by Henzinger et. al. (STOC 2014)). Our main focus is maintaining $st$-Reachability in an edge decremental directed graph (other problems can be reduced to $st$-Reachability). The classical algorithm of Even and Shiloach (JACM 81) solved this problem in $O(1)$ query time and $O(mn)$ total update time. Recently, Henzinger, Krinninger and Nanongkai (STOC 2014) designed a randomized algorithm which achieves an update time of $\tilde{O}(m n^{0.98})$ and broke the long-standing $O(mn)$ bound of Even and Shiloach. However, they designed four algorithms $A_i (1\le i \le 4)$ such that for graphs having total number of edges between $m_i$ and $m_{i+1}$ ($m_{i+1} > m_i$), $A_i$ outperforms other three algorithms. That is, one of the four algorithms may be faster for a particular density range of edges, but it may be too slow asymptotically for the other ranges. Our main contribution is that we design a {\it single} algorithm which works for all types of graphs. Not only is our algorithm faster, it is much simpler than the algorithm designed by Henzinger et.al. (STOC 2014).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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