ENGM 631 Maximum Flow Solutions. Maximum Flow Models (Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) 1 2 3 4 5 6 (6,8) (3,3) (4,4) (4,10)

Slides:



Advertisements
Similar presentations
Maximum Flow and Minimum Cut Problems In this handout: Duality theory Upper bounds for maximum flow value Minimum Cut Problem Relationship between Maximum.
Advertisements

Outline LP formulation of minimal cost flow problem
Max Flow Problem Given network N=(V,A), two nodes s,t of V, and capacities on the arcs: uij is the capacity on arc (i,j). Find non-negative flow fij for.
1 Maximum flow sender receiver Capacity constraint Lecture 6: Jan 25.
Chapter 6 Maximum Flow Problems Flows and Cuts Augmenting Path Algorithm.
Network Optimization Models: Maximum Flow Problems
MAXIMUM FLOW Max-Flow Min-Cut Theorem (Ford Fukerson’s Algorithm)
1 Augmenting Path Algorithm s t G: Flow value = 0 0 flow capacity.
1 COMMONWEALTH OF AUSTRALIA Copyright Regulations 1969 WARNING This material has been reproduced and communicated to you by or on behalf.
Nick McKeown Spring 2012 Maximum Matching Algorithms EE384x Packet Switch Architectures.
Applications of Maximum Flow and Minimum Cut Problems In this handout Transshipment problem Assignment Problem.
CSC 2300 Data Structures & Algorithms April 17, 2007 Chapter 9. Graph Algorithms.
CSE 421 Algorithms Richard Anderson Lecture 22 Network Flow.
Section 4.2 Network Flows By Christina Touhey. The flow out of a equals the flow into z. Algorithm 1.Make vertex a: (0, ). 2.Scan the first vertex and.
1 Augmenting Path Algorithm s t G: Flow value = 0 0 flow capacity.
Network Optimization Models: Maximum Flow Problems In this handout: The problem statement Solving by linear programming Augmenting path algorithm.
Chapter 7 Network Flow Models.
A network is shown, with a flow f. v u 6,2 2,2 4,1 5,3 2,1 3,2 5,1 4,1 3,3 Is f a maximum flow? (a) Yes (b) No (c) I have absolutely no idea a b c d.
Network Flow & Linear Programming Jeff Edmonds York University Adapted from NetworkFlow.ppt.
A B C D The diagram below shows water flowing through a pipework system. The values on the edges are the capacities of water that they.
Here is an example that involves what is called ‘back flow’ 0 A C B D S T Arrows have already been drawn initially.
1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.
NetworkModel-1 Network Optimization Models. NetworkModel-2 Network Terminology A network consists of a set of nodes and arcs. The arcs may have some flow.
Computational Methods for Management and Economics Carla Gomes Module 9d Network Models Maximum Flow Problem (Slides adapted from J.Orlin’s and Hillier’s)
Shortest Route, Minimal Spanning Tree and Maximal Flow Models
ENGM 732 Network Flow Programming Network Flow Models.
Section 2.9 Maximum Flow Problems Minimum Cost Network Flows Shortest Path Problems.
Lecture 16 Maximum Matching. Incremental Method Transform from a feasible solution to another feasible solution to increase (or decrease) the value of.
Maximization of Network Survivability against Intelligent and Malicious Attacks (Cont’d) Presented by Erion Lin.
Network Flow How to solve maximal flow and minimal cut problems.
CS 4407, Algorithms University College Cork, Gregory M. Provan Network Optimization Models: Maximum Flow Problems In this handout: The problem statement.
The diagram below shows water flowing through a pipework system. The values on the edges are the capacities of water that they can carry.
& 6.855J & ESD.78J Algorithm Visualization The Ford-Fulkerson Augmenting Path Algorithm for the Maximum Flow Problem.
The Ford-Fulkerson Augmenting Path Algorithm for the Maximum Flow Problem Thanks to Jim Orlin & MIT OCW.
CS223 Advanced Data Structures and Algorithms 1 Maximum Flow Neil Tang 3/30/2010.
1 EE5900 Advanced Embedded System For Smart Infrastructure Static Scheduling.
Welcome Unit 6 Seminar MM305 Wednesday 8:00 PM ET Quantitative Analysis for Management Delfina Isaac.
A directed graph G consists of a set V of vertices and a set E of arcs where each arc in E is associated with an ordered pair of vertices from V. V={0,
Network Problems A D O B T E C
Max Flow Application: Precedence Relations
CSCI 3160 Design and Analysis of Algorithms Tutorial 8
ENGM 535 Optimization Networks
Max Flow min Cut.
Chapter 12 Network Models 12-1
Maximum Flow Solutions
CMSC 341 Lecture 24 Max Flow Prof. Neary
Network Flows – Labelling procedure
Network Flows – Back flow
The Shortest Augmenting Path Algorithm for the Maximum Flow Problem
Introduction Basic formulations Applications
Lecture 19-Problem Solving 4 Incremental Method
Network Flows – Multiple sources and sinks
Richard Anderson Lecture 21 Network Flow
Problem Solving 4.
Augmenting Path Algorithm
Network Flow CSE 373 Data Structures.
EE5900 Advanced Embedded System For Smart Infrastructure
X y y = x2 - 3x Solutions of y = x2 - 3x y x –1 5 –2 –3 6 y = x2-3x.
Network Flows – Restricted vertices
Lecture 21 Network Flow, Part 1
Network Flows – Minimum capacities
The Shortest Augmenting Path Algorithm for the Maximum Flow Problem
The Shortest Augmenting Path Algorithm for the Maximum Flow Problem
Augmenting Path Algorithm
7. Edmonds-Karp Algorithm
Maximum Flow Neil Tang 4/8/2008
Shortest Path Solutions
Richard Anderson Lecture 22 Network Flow
Maximum Flow Problems in 2005.
EMIS The Maximum Flow Problem: Flows and Cuts Updated 6 March 2008
Presentation transcript:

ENGM 631 Maximum Flow Solutions

Maximum Flow Models (Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10)

Maximum Flow Models (Flow, Capacity) [External Flow] (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) Maximal Flow 1.Capacity is only relevant parameter. 2.Find maximal flow from source to sink. S S [M] [-M]

Maximum Flow 1.Find a flow augmenting path defined by a sequence of arcs P =(k1, k2,.v.v.vkp) 2.Determine the maximum flow increase along the path 3.Change the flow in the arcs on the path 4.Repeat until no flow augmenting paths can be found

Maximum Flow 1.Find an augmenting path 2.Determine the maximum flow augmentation possible 3.Augment flow by that amount

Maximum Flow Models (Flow, Capacity) (0,3) (0,2) (0,7) (0,8) (0,6) (0,8) (0,3) (0,4) (0,10) Find a path top to bottom that has Additional capacity. Increase flow to Available capacity

Augmented Path (Flow, Capacity) (0,3) (0,2) (0,7) (0,8) (0,6) (4,8) (0,3) (4,4) (4,10) (4)

Augmented Path (Flow, Capacity) (0,3) (0,2) (0,7) (0,8) (0,6) (4,8) (0,3) (4,4) (4,10) (4)

Augmented Path (Flow, Capacity) (0,3) (0,2) (0,7) (0,8) (0,6) (4,8) (0,3) (4,4) (4,10) (4) (6)

Augmented Path (Flow, Capacity) (0,3) (2,2) (0,7) (0,8) (0,6) (6,8) (0,3) (4,4) (4,10) (6) (4)

Augmented Path (Flow, Capacity) (0,3) (2,2) (0,7) (0,8) (0,6) (6,8) (0,3) (4,4) (4,10) (6) (4)

Augmented Path (Flow, Capacity) (0,3) (2,2) (0,7) (0,8) (0,6) (6,8) (0,3) (4,4) (4,10) (6) (4)

Augmented Path (Flow, Capacity) (0,3) (2,2) (0,7) (0,8) (0,6) (6,8) (0,3) (4,4) (4,10) (6) (4)

Augmented Path (Flow, Capacity) (0,3) (2,2) (3,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (9)

Augmented Path (Flow, Capacity) (0,3) (2,2) (3,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) Arc 2-4 at capacity (9)

Augmented Path (Flow, Capacity) (0,3) (2,2) (3,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) Arc 2-4 at capacity Arc 2-5 at capacity (9)

Augmented Path (Flow, Capacity) (0,3) (2,2) (3,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) Arc 2-4 at capacity Arc 2-5 at capacity Arc 3-5 at capacity (9)

Augmented Path (Flow, Capacity) (0,3) (2,2) (3,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (9) No other path exists start to end that has additional capacity

Augmented Path (Flow, Capacity) (0,3) (2,2) (3,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (9)

Minimum Cut Algorithm 1.Find all possible cuts source to sink 2.Find the cut that has minimal capacity 3.Minimal capacity cut = maximum flow

(Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 14) Minimum Cut Algorithm

(Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 11) Minimum Cut Algorithm

(Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 11) Minimum Cut Algorithm

(Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 11) Minimum Cut Algorithm

(Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 17) Minimum Cut Algorithm

Maximum Flow Models (Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 9)

Maximum Flow Models (Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10) (Capacity = 9)