论文标题

从随机高斯预测中构建跨度

On Constructing Spanners from Random Gaussian Projections

论文作者

Assadi, Sepehr, Kapralov, Michael, Yu, Huacheng

论文摘要

图素描是一种强大的范式,用于通过AHN,Guha和McGregor(Soda'12)引入的线性测量分析图结构,此后在流,分布式计算和大量并行算法等中发现了许多应用。事实证明,图形草图对于各种问题,例如连通性,最小跨越树,边缘或顶点连接性以及剪切或光谱稀疏器都非常成功。然而,从成功列表中特别缺少图形的最短路径度量,特别是计算扳手的问题。这将这个基本问题的地位变成了该领域最长期以来的开放问题之一。 我们通过证明了大型图形素描算法的强大下限,对缺乏成功的部分解释,该算法涵盖了先前的跨度工作,许多(但重要的是也不是所有)相关的基于剪切的问题。我们的下边界与Filtser,Kapralov和Nouri(Soda'21)的最新结果的算法界限匹配到较低的术语,以通过相同的图形素描系列构建跨度。这建立了这些界限的几乎很当地,至少仅限于这种图形草素描技术,并在后一种工作中提出的猜想上取得了进步。

Graph sketching is a powerful paradigm for analyzing graph structure via linear measurements introduced by Ahn, Guha, and McGregor (SODA'12) that has since found numerous applications in streaming, distributed computing, and massively parallel algorithms, among others. Graph sketching has proven to be quite successful for various problems such as connectivity, minimum spanning trees, edge or vertex connectivity, and cut or spectral sparsifiers. Yet, the problem of approximating shortest path metric of a graph, and specifically computing a spanner, is notably missing from the list of successes. This has turned the status of this fundamental problem into one of the most longstanding open questions in this area. We present a partial explanation of this lack of success by proving a strong lower bound for a large family of graph sketching algorithms that encompasses prior work on spanners and many (but importantly not also all) related cut-based problems mentioned above. Our lower bound matches the algorithmic bounds of the recent result of Filtser, Kapralov, and Nouri (SODA'21), up to lower order terms, for constructing spanners via the same graph sketching family. This establishes near-optimality of these bounds, at least restricted to this family of graph sketching techniques, and makes progress on a conjecture posed in this latter work.

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