几乎最优的超实体-传递流媒体可达性下界

IF 1.2 3区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS
Lijie Chen, Gillat Kol, Dmitry Paramonov, Raghuvansh R. Saxena, Zhao Song, Huacheng Yu
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引用次数: 0

摘要

SIAM 计算期刊》,提前印刷。 摘要我们给出了求解(有向)[math]-[math]可达性问题的任何[math]通量流算法的空间使用量的几乎二次[math]下限。这意味着任何此类算法基本上都必须存储整个图。作为推论,我们还得到了其他基本问题的几乎二次空间下界,包括最大匹配、最短路径、矩阵秩和线性规划。我们的主要技术贡献是定义和构建了集合隐藏图,这可能会引起人们的独立兴趣:我们给出了将集合 [math] 编码为具有 [math] 顶点的有向图的一般方法,这样,对于图中特定的一对顶点 [math],判断 [math] 是否可以到达就可以归结为判断 [math] 是否可以从 [math] 到达。此外,我们还证明了我们的图 "隐藏 "了[math],也就是说,任何低空间流算法都无法通过少量传递了解到(几乎)任何关于[math]的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Almost Optimal SuperConstant-Pass Streaming Lower Bounds for Reachability
SIAM Journal on Computing, Ahead of Print.
Abstract. We give an almost quadratic [math] lower bound on the space usage of any [math]-pass streaming algorithm solving the (directed) [math]-[math] reachability problem. This means that any such algorithm must essentially store the entire graph. As corollaries, we obtain almost quadratic space lower bounds for additional fundamental problems, including maximum matching, shortest path, matrix rank, and linear programming. Our main technical contribution is the definition and construction of set hiding graphs, that may be of independent interest: we give a general way of encoding a set [math] as a directed graph with [math] vertices, such that deciding whether [math] boils down to deciding if [math] is reachable from [math], for a specific pair of vertices [math] in the graph. Furthermore, we prove that our graph “hides” [math], in the sense that no low-space streaming algorithm with a small number of passes can learn (almost) anything about [math].
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来源期刊
SIAM Journal on Computing
SIAM Journal on Computing 工程技术-计算机:理论方法
CiteScore
4.60
自引率
0.00%
发文量
68
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Computing aims to provide coverage of the most significant work going on in the mathematical and formal aspects of computer science and nonnumerical computing. Submissions must be clearly written and make a significant technical contribution. Topics include but are not limited to analysis and design of algorithms, algorithmic game theory, data structures, computational complexity, computational algebra, computational aspects of combinatorics and graph theory, computational biology, computational geometry, computational robotics, the mathematical aspects of programming languages, artificial intelligence, computational learning, databases, information retrieval, cryptography, networks, distributed computing, parallel algorithms, and computer architecture.
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