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Solving the all-pair shortest path problem on interval and circular-arc graphs

机译:在间隔和圆弧图上解决全部对最短路径问题

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We show that, given the interval model of an unweighted n-vertex interval graph, the information for the all-pair shortest paths can be made available very efficiently, both in parallel and sequentially. After sorting the input intervals by their endpoints, an O(n) space data structure can be constructed optimally in parallel, in O(log n) time using O(n/log n) CREW PRAM processors. Using the data structure, a query on the length of the shortest path between any two input intervals can be answered in O(1) time using one processor, and a query on the actual shortest path can be answered in O(1) time using k processors, where k is the number of intervals on that path. Our parallel algorithm immediately implies a new sequential result: After an O(n) time preprocess, shortest paths can be reported optimally. Our techniques can be extended to solve the problem on circular-arc graphs, both in parallel and sequentially, in the same complexity bounds. The previously best known sequential algorithm for computing the all-pair shortest paths in interval graphs lakes O(n/sup 2/) time and uses O(n/sup 2/) space to store the lengths of the all-pair shortest paths.
机译:我们表明,考虑到未加权的N角间隔图的间隔模型,可以在并行和顺序上非常有效地提供全对最短路径的信息。通过其端点对输入间隔进行排序,可以使用O(n / log n)船员PRAM处理器在O(log n)时间内并行地构造O(n)空间数据结构。使用数据结构,可以使用一个处理器在O(1)时间内回答所有两个输入间隔之间的最短路径的长度的查询,并且可以在O(1)时间内回答实际最短路径上的查询K处理器,其中k是该路径上的间隔数。我们的并行算法立即意味着新的顺序结果:在O(n)时间预处理之后,可以最佳地报告最短路径。我们的技术可以扩展以解决在相同的复杂性范围内并行和顺序循环弧图上的问题。以前最熟知的序列算法用于计算间隔图中的全对最短路径湖泊湖泊o(n / sup 2 /)时间,并使用O(n / sup 2 /)空间来存储全部对最短路径的长度。

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