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Fast approximate shortest paths in the congested clique

机译:在拥挤的集团中快速近似最短路径

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We design fast deterministic algorithms for distance computation in the Congested Clique model. Our key contributions include:- A (2 + epsilon)-approximation for all-pairs shortest paths in O(log(2) n/epsilon) rounds on unweighted undirected graphs. With a small additional additive factor, this also applies for weighted graphs. This is the first sub-polynomial constant-factor approximation for APSP in this model.- A(1 + epsilon)-approximation for multi-source shortest paths from O(root n) sources in O(log(2) n/epsilon) rounds on weighted undirected graphs. This is the first sub-polynomial algorithm obtaining this approximation for a set of sources of polynomial size.Our main techniques are new distance tools that are obtained via improved algorithms for sparse matrix multiplication, which we leverage to construct efficient hopsets and shortest paths. Furthermore, our techniques extend to additional distance problems for which we improve upon the state-of-the-art, including diameter approximation, and an exact single-source shortest paths algorithm for weighted undirected graphs in (O) over tilde (n(1/6)) rounds.
机译:我们设计了用于拥塞集团模型中距离计算的快速确定性算法。我们的主要贡献包括:- A (2 + epsilon) - 近似在未加权无向图上 O(log(2) n/epsilon) 圆角中所有对最短路径的近似。使用少量附加因子,这也适用于加权图。这是该模型中APSP的第一个子多项式常数因子近似-A(1 + epsilon)-加权无向图上O(log(2) n/epsilon)舍入中来自O(根n)源的多源最短路径的近似。这是第一个获得多项式大小的集合源的近似值的子多项式算法。我们的主要技术是新的距离工具,这些工具是通过改进的稀疏矩阵乘法算法获得的,我们利用它来构建高效的跳跃集和最短路径。此外,我们的技术扩展到其他距离问题,我们改进了最先进的方法,包括直径近似,以及用于波浪号(n(1/6))回合(O)加权无向图的精确单源最短路径算法。

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