Almost Optimal Exact Distance Oracles for Planar Graphs

IF 2.3 2区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Panagiotis Charalampopoulos, Paweł Gawrychowski, Yaowei Long, Shay Mozes, Seth Pettie, Oren Weimann, Christian Wulff-Nilsen
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引用次数: 0

Abstract

We consider the problem of preprocessing a weighted directed planar graph in order to quickly answer exact distance queries. The main tension in this problem is between space S and query time Q, and since the mid-1990s all results had polynomial time-space tradeoffs, e.g., Q = ~ Θ(n/√ S) or Q = ~Θ(n5/2/S3/2).

In this article we show that there is no polynomial tradeoff between time and space and that it is possible to simultaneously achieve almost optimal space n1+o(1) and almost optimal query time no(1). More precisely, we achieve the following space-time tradeoffs:

  • n1+o(1) space and log2+o(1) n query time,

  • n log2+o(1) n space and no(1) query time,

  • n4/3+o(1) space and log1+o(1) n query time.

We reduce a distance query to a variety of point location problems in additively weighted Voronoi diagrams and develop new algorithms for the point location problem itself using several partially persistent dynamic tree data structures.

几乎最优的精确距离预言为平面图形
为了快速回答精确距离查询,我们研究了加权有向平面图的预处理问题。这个问题的主要紧张关系是在空间S和查询时间Q之间,自20世纪90年代中期以来,所有结果都有多项式的时空权衡,例如,Q = ~Θ(n /√S)或Q = ~Θ(n5/2/S3/2)。在本文中,我们展示了时间和空间之间不存在多项式权衡,并且可以同时实现几乎最优的空间n1+o(1)和几乎最优的查询时间no(1)。更准确地说,我们实现了以下时空权衡:n1+o(1)空间和log2+o(1) n查询时间,n log2+o(1) n空间和no(1)查询时间,n1 /3+o(1)空间和log1+o(1) n查询时间。我们将距离查询简化为各种加性加权Voronoi图中的点定位问题,并使用几个部分持久的动态树数据结构开发了点定位问题本身的新算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the ACM
Journal of the ACM 工程技术-计算机:理论方法
CiteScore
7.50
自引率
0.00%
发文量
51
审稿时长
3 months
期刊介绍: The best indicator of the scope of the journal is provided by the areas covered by its Editorial Board. These areas change from time to time, as the field evolves. The following areas are currently covered by a member of the Editorial Board: Algorithms and Combinatorial Optimization; Algorithms and Data Structures; Algorithms, Combinatorial Optimization, and Games; Artificial Intelligence; Complexity Theory; Computational Biology; Computational Geometry; Computer Graphics and Computer Vision; Computer-Aided Verification; Cryptography and Security; Cyber-Physical, Embedded, and Real-Time Systems; Database Systems and Theory; Distributed Computing; Economics and Computation; Information Theory; Logic and Computation; Logic, Algorithms, and Complexity; Machine Learning and Computational Learning Theory; Networking; Parallel Computing and Architecture; Programming Languages; Quantum Computing; Randomized Algorithms and Probabilistic Analysis of Algorithms; Scientific Computing and High Performance Computing; Software Engineering; Web Algorithms and Data Mining
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