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New Algorithms and Hardness for Incremental Single-Source Shortest Paths in Directed Graphs
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incsssp.pdf
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Accepted version
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901.8 KB
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Author(s)
Probst Gutenberg, Maximilian
Vassilevska Williams, Virginia
Wein, Nicole
Date Issued
June 2020
Journal
Proceedings of the Annual ACM Symposium on Theory of Computing
Publisher
ACM
Citation
Probst Gutenberg, Maximilian, Vassilevska Williams, Virginia and Wein, Nicole. 2020. "New Algorithms and Hardness for Incremental Single-Source Shortest Paths in Directed Graphs." Proceedings of the Annual ACM Symposium on Theory of Computing.
Version
Author's final manuscript
Abstract
© 2020 ACM. In the dynamic Single-Source Shortest Paths (SSSP) problem, we are given a graph G=(V,E) subject to edge insertions and deletions and a source vertex sg V, and the goal is to maintain the distance d(s,t) for all tg V. Fine-grained complexity has provided strong lower bounds for exact partially dynamic SSSP and approximate fully dynamic SSSP [ESA'04, FOCS'14, STOC'15]. Thus much focus has been directed towards finding efficient partially dynamic (1+")-approximate SSSP algorithms [STOC'14, ICALP'15, SODA'14, FOCS'14, STOC'16, SODA'17, ICALP'17, ICALP'19, STOC'19, SODA'20, SODA'20]. Despite this rich literature, for directed graphs there are no known deterministic algorithms for (1+")-approximate dynamic SSSP that perform better than the classic ES-tree [JACM'81]. We present the first such algorithm. We present a deterministic data structure for incremental SSSP in weighted directed graphs with total update time Õ(n2 logW/"O(1)) which is near-optimal for very dense graphs; here W is the ratio of the largest weight in the graph to the smallest. Our algorithm also improves over the best known partially dynamic randomized algorithm for directed SSSP by Henzinger et al. [STOC'14, ICALP'15] if m=ω(n1.1). Complementing our algorithm, we provide improved conditional lower bounds. Henzinger et al. [STOC'15] showed that under the OMv Hypothesis, the partially dynamic exact s-t Shortest Path problem in undirected graphs requires amortized update or query time m1/2-o(1), given polynomial preprocessing time. Under a new hypothesis about finding Cliques, we improve the update and query lower bound for algorithms with polynomial preprocessing time to m0.626-o(1). Further, under the k-Cycle hypothesis, we show that any partially dynamic SSSP algorithm with O(m2-") preprocessing time requires amortized update or query time m1-o(1), which is essentially optimal. All previous conditional lower bounds that come close to our bound [ESA'04,FOCS'14] only held for "combinatorial" algorithms, while our new lower bound does not make such restrictions.
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DOI of Published Version
10.1145/3357713.3384236
