Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these...

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Main Authors: Adrian Marius Deaconu, Delia Spridon
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/15/1716
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spelling doaj-2c1b132011bc46c0bfa691e35ddb7fe12021-08-06T15:28:11ZengMDPI AGMathematics2227-73902021-07-0191716171610.3390/math9151716Adaptation of Random Binomial Graphs for Testing Network Flow Problems AlgorithmsAdrian Marius Deaconu0Delia Spridon1Department of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, Transilvania University of Brasov, 50003 Brașov, RomaniaDepartment of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, Transilvania University of Brasov, 50003 Brașov, RomaniaAlgorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.https://www.mdpi.com/2227-7390/9/15/1716network flowrandom networksparallel programmingtime efficiency of algorithms
collection DOAJ
language English
format Article
sources DOAJ
author Adrian Marius Deaconu
Delia Spridon
spellingShingle Adrian Marius Deaconu
Delia Spridon
Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms
Mathematics
network flow
random networks
parallel programming
time efficiency of algorithms
author_facet Adrian Marius Deaconu
Delia Spridon
author_sort Adrian Marius Deaconu
title Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms
title_short Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms
title_full Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms
title_fullStr Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms
title_full_unstemmed Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms
title_sort adaptation of random binomial graphs for testing network flow problems algorithms
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2021-07-01
description Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.
topic network flow
random networks
parallel programming
time efficiency of algorithms
url https://www.mdpi.com/2227-7390/9/15/1716
work_keys_str_mv AT adrianmariusdeaconu adaptationofrandombinomialgraphsfortestingnetworkflowproblemsalgorithms
AT deliaspridon adaptationofrandombinomialgraphsfortestingnetworkflowproblemsalgorithms
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