In graph theory, a flow network (also known as a transportation network) is a directed graph where each edge has a capacity and each edge receives a flow. The amount of flow on an edge cannot exceed the capacity of the edge. Often in operations research, a directed graph is called a network, the vertices are called nodes and the edges are called arcs. A flow must satisfy the restriction that the amount of flow into a node equals the amount of flow out of it, unless it is a source, which has only outgoing flow, or sink, which has only incoming flow. A network can be used to model traffic in a computer network, circulation with demands, fluids in pipes, currents in an electrical circuit, or anything similar in which something travels through a network of nodes. (Wikipedia).
OCR MEI MwA H: Network Flows: 01 Introduction
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From playlist OCR MEI MwA H: Network Flows
OCR MEI MwA H: Network Flows: 02 Feasible Flows
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From playlist OCR MEI MwA H: Network Flows
From playlist Week 9: Social Networks
Flowchart for the Factorial Program
Flowchart for the factorial program
From playlist Computer Science
Linear Algebra - Lecture 14 - Applications to Networks
In this lecture, we study how to apply linear algebra techniques to flow networks.
From playlist Linear Algebra Lectures
How to do flow test using flow meter in machines.
More details visit: http://www.HowmachineWorks.com/ Plz Join Our Face Book Page. http://www.facebook.com/pages/HowMachineWorks This video give you a clear idea about how to do a flow test using a flow meter on a hydraulic pump.
From playlist Hydraulics and related stuff.
Star Network - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
OCR MEI MwA H: Network Flows: 03 Feasible Flows Example
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From playlist OCR MEI MwA H: Network Flows
Max Flow Ford Fulkerson | Network Flow | Graph Theory
Explanation of how to find the maximum flow with the Ford-Fulkerson method Next video: https://youtu.be/Xu8jjJnwvxE Algorithms repository: https://github.com/williamfiset/algorithms#network-flow Video slides: https://github.com/williamfiset/Algorithms/tree/master/slides 0:00 Intro and
From playlist Network Flow playlist
離散数学入門#9: 最大流問題(2):増加道アルゴリズムと最大流最小カット定理
早稲田大学の全学部の3〜4年生を対象とする全学オープン科目「離散数学入門」(担当教員:早水 桃子)の授業動画です.文理を問わず,誰でもグラフ理論やグラフアルゴリズムの初歩を学ぶことができます.グラフ理論の定理やグラフに関するアルゴリズムを正しく理解して,現実の諸問題を解決するための応用力を身につけましょう. --------------------------------------------------------------------------------------- 前回の講義では,各アークの容量が規定されたネットワークの始点(ソース)から終点(シンク)に向け
From playlist 離散数学入門Ⅲ
離散数学入門#8: 最大流問題(1):フローネットワークの基礎知識
早稲田大学の全学部の3〜4年生を対象とする全学オープン科目「離散数学入門」(担当教員:早水 桃子)の授業動画です.文理を問わず,誰でもグラフ理論やグラフアルゴリズムの初歩を学ぶことができます.グラフ理論の定理やグラフに関するアルゴリズムを正しく理解して,現実の諸問題を解決するための応用力を身につけましょう. --------------------------------------------------------------------------------------- ネットワークの始点(ソース)から終点(シンク)に向けて流せる最大の流量を問う「最大流問題」は,
From playlist 離散数学入門Ⅲ
Stanford Seminar - Security and the Software Defined Network
"Security and the Software Defined Network" - Phil Porras of SRI International Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems a
From playlist Engineering
Physics of flow networks (Lecture 2, Remote talk) by Karen Alim
Program ICTP - ICTS Winter School on Quantitative Systems Biology ORGANIZERS: Buzz Baum, Guillaume Salbreux, Stefano Di Talia and Vijaykumar Krishnamurthy DATE: 03 December 2019 to 20 December 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore The development of an organism from a sin
From playlist Winter School on Quantitative Systems Biology: Quantitative Approaches in Ecosystem Ecology 2020
Nexus Trimester - Giacomo Como (Lund University)
Resilient control of dynamic flow networks Giacomo Como (Lund University) february 29, 2016 Abstract: This talk focuses on distributed control of dynamical flow networks. These are modeled as dynamical systems derived from mass conservation laws on directed capacitated networks. The flow
From playlist Nexus Trimester - 2016 - Central Workshop
Physics of flow networks (Lecture 1, Remote talk) by Karen Alim
Program ICTP - ICTS Winter School on Quantitative Systems Biology ORGANIZERS: Buzz Baum, Guillaume Salbreux, Stefano Di Talia and Vijaykumar Krishnamurthy DATE: 03 December 2019 to 20 December 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore The development of an organism from a sin
From playlist Winter School on Quantitative Systems Biology: Quantitative Approaches in Ecosystem Ecology 2020
Lecture 10 | Convex Optimization II (Stanford)
Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd introduces a new topic, Decomposition Applications. This course introduces topics such as subgradient, cutting-plane, and ellipsoid methods. Decentral
From playlist Lecture Collection | Convex Optimization
TUT1446 Software Defined Networking Deep Dive
This tutorial session was delivered at SUSECON in April 2019, in Nashville, TN. Abstract: Software Defined Networking is now very popular and the industry agrees that it is not going anywhere. We at SUSE have embraced this technology and integrated our products with multiple SDN technologi
From playlist SUSECON 2019
Streamlines | Lecture 6 | Flow Around a Cylinder
Definition of steamlines as curves of constant stream function. Join me on Coursera: https://www.coursera.org/learn/mathematics-engineers-capstone Lecture notes at https://www.math.hkust.edu.hk/~machas/flow-around-a-cylinder.pdf Subscribe to my channel: http://www.youtube.com/user/jchas
From playlist Flow Around a Cylinder
Lec 11 | MIT RES.6-008 Digital Signal Processing, 1975
Lecture 11: Representation of linear digital networks Instructor: Alan V. Oppenheim View the complete course: http://ocw.mit.edu/RES6-008S11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT RES.6-008 Digital Signal Processing, 1975