An augmenting‐flow algorithm for a class of node‐capacitated maximum flow problems

Networks ◽  
2021 ◽  
Author(s):  
Robert M. Curry ◽  
J. Cole Smith
Keyword(s):  
Maximum Flow ◽  
Flow Problems ◽  
Flow Algorithm

Tidal Flow: A Fast and Teachable Maximum Flow Algorithm

2018 ◽  
Vol 12 ◽  
pp. 25-41
Author(s):  
Matthew C. FONTAINE
Keyword(s):  
Computer Science ◽  
Tidal Flow ◽  
Maximum Flow ◽  
Efficient Algorithms ◽  
Science Students ◽  
Flow Algorithm ◽  
Maximum Flows

Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

scholarly journals Mapping the MPM maximum flow algorithm on GPUs

2010 ◽  
Vol 256 ◽  
pp. 012006 ◽  
Author(s):  
Steven Solomon ◽  
Parimala Thulasiraman
Keyword(s):  
Maximum Flow ◽  
Flow Algorithm

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

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.

Empirical Study on the Relationship between Financial Development and Economic Growth in China Based on Maximum Flow Algorithm

2021 ◽  
Vol 7 (5) ◽  
pp. 4463-4473
Author(s):  
Qiao Wang
Keyword(s):  
Economic Growth ◽  
Financial Development ◽  
Influencing Factor ◽  
Maximum Flow ◽  
Theoretical Research ◽  
Particle Swarm Algorithm ◽  
Model Parameters ◽  
Factors Affecting ◽  
Flow Algorithm ◽  
The Relationship

Objectives: The relationship between finance and economic growth has always been one of the hot issues in theoretical research and empirical analysis. As one of the important factors affecting economic growth, finance has long been recognized by the majority of scholars. Methods: In the context of the development of Internet e-commerce, empirical research on the relationship between China’s financial development and economic growth is conducted based on the maximum traffic algorithm. Results: Based on this, this paper constructs the Probit and Logistic binary discrete selection model for economic growth, and the discrete particle swarm algorithm is used to solve the sequence of influencing factors, estimating the model parameters, and the degree of influence of each influencing factor is calculated. Conclusion: The degree of concurrent employment is a decisive factor in economic growth.

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