IEOR 4004 Maximum flow problems. Connectivity t t s s Q1: Can Alice send a message to Bob ? Yes if every (s,t)-cut contains at least one forward edge.

Slides:



Advertisements
Similar presentations
Bus 480 – Lecture 2 Transportation and Assignment models
Advertisements

PSC 151 Laboratory Activity 9 Electric Circuits II Series and Parallel Connections.
COMP 482: Design and Analysis of Algorithms
Maximum Flow and Minimum Cut Problems In this handout: Duality theory Upper bounds for maximum flow value Minimum Cut Problem Relationship between Maximum.
Maximum flow problems IV. MA252 Combinatorial Optimization.
IEOR 4004 Final Review part I.
Transportation problem Factories Customers Requirement for goods Production capacity... Minimum cost of transportation satisfying the demand of customers.
IMIM v v v v v v v v v DEFINITION L v 11 v 2 1 v 31 v 12 v 2 2 v 32.
Outline LP formulation of minimal cost flow problem
Lecture 5: Network Flow Algorithms Max-Flow Min-Cut Single-Source Shortest-Path (SSSP) Job Sequencing.
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.
Chapter 6 Maximum Flow Problems Flows and Cuts Augmenting Path Algorithm.
MAXIMUM FLOW Max-Flow Min-Cut Theorem (Ford Fukerson’s Algorithm)
CSCI 256 Data Structures and Algorithm Analysis Lecture 18 Some slides by Kevin Wayne copyright 2005, Pearson Addison Wesley all rights reserved, and some.
1 Augmenting Path Algorithm s t G: Flow value = 0 0 flow capacity.
1 Maximum Flow w s v u t z 3/33/3 1/91/9 1/11/1 3/33/3 4/74/7 4/64/6 3/53/5 1/11/1 3/53/5 2/22/2 
1 Chapter 7 Network Flow Slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved.
1 Chapter 7 Network Flow Slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved.
1 COMMONWEALTH OF AUSTRALIA Copyright Regulations 1969 WARNING This material has been reproduced and communicated to you by or on behalf.
1 The Max Flow Problem. 2 Flow networks Flow networks are the problem instances of the max flow problem. A flow network is given by 1) a directed graph.
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.
The max flow problem
Maximum Flows Lecture 4: Jan 19. Network transmission Given a directed graph G A source node s A sink node t Goal: To send as much information from s.
Minimum Cost Flow Lecture 5: Jan 25. Problems Recap Bipartite matchings General matchings Maximum flows Stable matchings Shortest paths Minimum spanning.
Maximum Flow CSC 172 SPRING 2002 LECTURE 27. Flow Networks Digraph Weights, called capacities, on edges Two distinct veticies Source, “s” (no incoming.
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.
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.
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.
Flow Networks Formalization Basic Results Ford-Fulkerson Edmunds-Karp Bipartite Matching Min-cut.
Lecture 5: Network Flow Algorithms Single-Source Shortest-Path (SSSP) (Dijkstra's Algorithm) Max Flow - Min Cut (Ford-Fulkerson) Job Sequencing.
1 COMMONWEALTH OF AUSTRALIA Copyright Regulations 1969 WARNING This material has been reproduced and communicated to you by or on behalf.
Max Flow – Min Cut Problem. Directed Graph Applications Shortest Path Problem (Shortest path from one point to another) Max Flow problems (Maximum material.
Maximum Flow Problem (Thanks to Jim Orlin & MIT OCW)
& 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.
Chapter 7 April 28 Network Flow.
15.082J & 6.855J & ESD.78J October 7, 2010 Introduction to Maximum Flows.
15.082J and 6.855J March 4, 2003 Introduction to Maximum Flows.
Chapter 7 May 3 Ford-Fulkerson algorithm Step-by-step walk through of an example Worst-case number of augmentations Edmunds-Karp modification Time complexity.
1 EE5900 Advanced Embedded System For Smart Infrastructure Static Scheduling.
CSCI-256 Data Structures & Algorithm Analysis Lecture Note: Some slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved. 23.
1 Network Flow CSC401 – Analysis of Algorithms Chapter 8 Network Flow Objectives: Flow networks –Flow –Cut Maximum flow –Augmenting path –Maximum flow.
Fall 2003Maximum Flow1 w s v u t z 3/33/3 1/91/9 1/11/1 3/33/3 4/74/7 4/64/6 3/53/5 1/11/1 3/53/5 2/22/2 
Maximum Flow Problem Definitions and notations The Ford-Fulkerson method.
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)
Flow A flow f for a network N is is an assignment of an integer value f(e) to each edge e that satisfies the following properties: Capacity Rule: For each.
Maximum Flow c v 3/3 4/6 1/1 4/7 t s 3/3 w 1/9 3/5 1/1 3/5 u z 2/2
CS4234 Optimiz(s)ation Algorithms
Maximum Flow 9/13/2018 6:12 PM Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015.
CSCI 3160 Design and Analysis of Algorithms Tutorial 8
Maximum Flow Solutions
Network Flow 2016/04/12.
Instructor: Shengyu Zhang
Edmonds-Karp Algorithm
Maximum Flow c v 3/3 4/6 1/1 4/7 t s 3/3 w 1/9 3/5 1/1 3/5 u z 2/2
Maximum Flow c v 3/3 4/6 1/1 4/7 t s 3/3 w 1/9 3/5 1/1 3/5 u z 2/2
Chapter 7 Network Flow Slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved.
Network Flow and Connectivity in Wireless Sensor Networks
Introduction to Maximum Flows
Augmenting Path Algorithm
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.
Algorithms (2IL15) – Lecture 7
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.
Maximum Flow c v 3/3 4/6 1/1 4/7 t s 3/3 w 1/9 3/5 1/1 3/5 u z 2/2
Maximum flow problems II.
Augmenting Path Algorithm
The Residual Network Given a flow x, the residual capacity rij of arc (i, j) is the maximum additional flow that can be sent from i to j using arcs (i,
Presentation transcript:

IEOR 4004 Maximum flow problems

Connectivity t t s s Q1: Can Alice send a message to Bob ? Yes if every (s,t)-cut contains at least one forward edge forward backward

Connectivity t t s s Q2: How fast? Send data in parallel Q1: Can Alice send a message to Bob ?

Edge capacity t t s s Idea: Data packets can share edges (bandwidth) Two packets in parallel

t t s s

Conservation of flow t t s s v net flow (excess) value of flow value of flow incoming outgoing

t t s s

Forward paths do not suffice t t s s

Augmenting chain t t s s

Exponentially many steps t t s s

t t s s

t t s s Residual network t t s s Forward path Augmenting chain

Recall: Connectivity t t s s Q1: Is there a path from s to t? Yes if every (s,t)-cut contains at least one forward edge Else No forward backward

Flows and cuts t t s s flow across a cut (forward flow – backward flow) value of a flow capacity of a cut (forward edges) flow across a cut ≤ capacity of the cut Weak duality

Maximum flow = Minimum cut t t s s optimal solution Strong duality

Transportation problem Factories Retail stores Requirement for goods Production capacity... Can factories satisfy the demand of retail stores ? aiai aiai bjbj bjbj edge if i-th factory can deliver to j-th store t Maximum flow  capacity production (capacity) demand (capacity) source target a1a1 a2a2 anan bmbm b1b1 b2b2 s

Transportation problem Factories Retail stores Requirement for goods Production capacity... Can factories satisfy the demand of retail stores ? t Maximum flow  capacity limited production (capacity) limited demand (capacity) Units of flow 1 23 source target bmbm b1b1 b2b2 Example 1: n=m=3 a 1 =a 2 =a 3 =1 b 1 =b 2 =b 3 =1 Answer: Yes! a1a1 a2a2 anan s

Transportation problem Factories Retail stores Requirement for goods Production capacity Can factories satisfy the demand of retail stores ? t Maximum flow  capacity production (capacity) demand (capacity) source target Example 2: n=m=3 a 1 =a 2 =1 a 3 =3 b 1 =3 b 2 =b 3 =1 Answer: No! Maximum flow = 4 < 5 3 rd factory does not deliver to 1 st retail store s Example 1: n=m=3 a 1 =a 2 =a 3 =1 b 1 =b 2 =b 3 =1