Directed Shortest Paths via Approximate Cost Balancing

IF 2.3 2区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of the ACM Pub Date : 2022-12-19 DOI:https://dl.acm.org/doi/10.1145/3565019
James B. Orlin, László Végh
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Abstract

We present an O(nm) algorithm for all-pairs shortest paths computations in a directed graph with n nodes, m arcs, and nonnegative integer arc costs. This matches the complexity bound attained by Thorup [31] for the all-pairs problems in undirected graphs. The main insight is that shortest paths problems with approximately balanced directed cost functions can be solved similarly to the undirected case. The algorithm finds an approximately balanced reduced cost function in an O(mn log n) preprocessing step. Using these reduced costs, every shortest path query can be solved in O(m) time using an adaptation of Thorup’s component hierarchy method. The balancing result can also be applied to the ℓ-matrix balancing problem.

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基于近似成本平衡的有向最短路径
我们提出了一种O(nm)算法,用于计算具有n个节点,m条弧和非负整数弧代价的有向图中的全对最短路径。这与Thorup[31]对无向图中全对问题的复杂度界相匹配。主要观点是,具有近似平衡有向成本函数的最短路径问题可以与无向情况类似地解决。该算法在O(m√n log n)预处理步骤中找到一个近似平衡的降代价函数。利用这些降低的成本,每个最短路径查询都可以在O(m)时间内使用Thorup的组件层次方法进行求解。该平衡结果也可应用于求解矩阵的平衡问题。
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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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