分支网络中的启发式优化传输

IF 1.5 4区物理与天体物理 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

International Journal of Modern Physics C Pub Date : 2024-03-12 DOI:10.1142/s0129183124501201

M. Andrecut

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引用次数: 0

摘要

最佳传输的目的是通过最小化成本（通常定义为距离的函数）来学习源到目标的映射。这一问题的解决方案由直线段组成，以最佳方式连接信号源和目标，而且不会出现分支。这些最优解与自然和人造交通网络形成了鲜明对比，在自然和人造交通网络中，分支结构非常普遍。在这里，我们讨论了一种用于优化网络运输的快速启发式分支方法。我们还提供了几个合成实例的数值应用、一个简化的心血管网络和 "圣诞老人 "配送网络（包括全球 141182 个已知位置和人口的城市）。

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Heuristic optimal transport in branching networks

Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance. The solution to this problem consists of straight line segments optimally connecting sources to targets, and it does not exhibit branching. These optimal solutions are in stark contrast with both natural, and man-made transportation networks, where branching structures are prevalent. Here, we discuss a fast heuristic branching method for optimal transport in networks. We also provide several numerical applications to synthetic examples, a simplified cardiovascular network, and the “Santa Claus” distribution network which includes 141182 cities around the world, with known location and population.

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来源期刊

International Journal of Modern Physics C 物理-计算机：跨学科应用

CiteScore

3.00

自引率

15.80%

发文量

158

审稿时长

4 months

期刊介绍： International Journal of Modern Physics C (IJMPC) is a journal dedicated to Computational Physics and aims at publishing both review and research articles on the use of computers to advance knowledge in physical sciences and the use of physical analogies in computation. Topics covered include: algorithms; computational biophysics; computational fluid dynamics; statistical physics; complex systems; computer and information science; condensed matter physics, materials science; socio- and econophysics; data analysis and computation in experimental physics; environmental physics; traffic modelling; physical computation including neural nets, cellular automata and genetic algorithms.