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Faster Algorithms for Incremental Topological Ordering

机译:增量拓扑排序的更快算法

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We present two online algorithms for maintaining a topological order of a directed acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm takes O(m{sup}(1/2)) amortized time per arc and our second algorithm takes O(n{sup}2.5/m) amortized time per arc, where n is the number of vertices and m is the total number of arcs. For sparse graphs, our O(m{sup}(1/2)) bound improves the best previous bound by a factor of log n and is tight to within a constant factor for a natural class of algorithms that includes all the existing ones. Our main insight is that the two-way search method of previous algorithms does not require an ordered search, but can be more general, allowing us to avoid the use of heaps (priority queues). Instead, the deterministic version of our algorithm uses (approximate) median-finding; the randomized version of our algorithm uses uniform random sampling. For dense graphs, our O(n{sup}2.5/m) bound improves the best previously published bound by a factor of n{sup}(1/4) and a recent bound obtained independently of our work by a factor of log n. Our main insight is that graph search is wasteful when the graph is dense and can be avoided by searching the topological order space instead. Our algorithms extend to the maintenance of strong components, in the same asymptotic time bounds.
机译:我们提出了两种在线算法用于维持在添加弧有向非循环图的拓扑次序,并创建一个时检测一个周期。我们的第一个算法需要O(M {SUP}(1/2))摊销时间每弧和我们的第二算法需要O(N {SUP} 2.5 / m)的每摊销电弧时间,其中n是顶点,且m的数量是总数弧。对于稀疏图,结合我们的O(米{SUP}(1/2))改进前最好用log n的因子结合并且是紧一种用于天然类算法包括所有现有的一个常数因子内。我们的主要观点是,以前的算法的双向搜索方法并不需要一个有序的搜索,但可以更一般的,使我们能够避免使用堆(优先级队列)的。相反,我们的算法使用(近似值)中位数调查的确定性版本;我们的算法的随机版本使用统一的随机抽样。对于密集图形,我们的O(N {SUP} 2.5 / m)的结合提高了先前公开的由n个{SUP}的(1/4)倍,并且由log n的因子结合独立地我们的工作获得的最近结合最佳。我们的主要观点是,图搜索是一种浪费,当图是密集的,并且可以通过而不是搜索拓扑顺序空间来避免。我们的算法扩展到维护强大的组件,在相同的时间渐近界限。

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