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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Online Access: | https://www.mdpi.com/2227-7390/9/15/1716 |
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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 |
_version_ |
1721217896944762880 |