Maximum Flow in Planar Graphs

Similar documents
The Residual Graph. 12 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow.

The Residual Graph. 11 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm

Randomized Perfect Bipartite Matching

Reminder: Flow Networks

Today: Max Flow Proofs

Algorithmic Discrete Mathematics 6. Exercise Sheet

Flow Networks. Ma/CS 6a. Class 14: Flow Exercises

Max Flow, Min Cut COS 521. Kevin Wayne Fall Soviet Rail Network, Cuts. Minimum Cut Problem. Flow network.

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t)

1 Motivation and Basic Definitions

CS4445/9544 Analysis of Algorithms II Solution for Assignment 1

Greedy. I Divide and Conquer. I Dynamic Programming. I Network Flows. Network Flow. I Previous topics: design techniques

18 Extensions of Maximum Flow

Soviet Rail Network, 1955

CSE 521: Design & Analysis of Algorithms I

5.2 GRAPHICAL VELOCITY ANALYSIS Polygon Method

4/12/12. Applications of the Maxflow Problem 7.5 Bipartite Matching. Bipartite Matching. Bipartite Matching. Bipartite matching: the flow network

Graphs III - Network Flow

CS 473G Lecture 15: Max-Flow Algorithms and Applications Fall 2005

MAXIMUM FLOW. introduction Ford-Fulkerson algorithm maxflow-mincut theorem

Geometric Path Problems with Violations

Main Reference: Sections in CLRS.

Please Complete Course Survey. CMPSCI 311: Introduction to Algorithms. Approximation Algorithms. Coping With NP-Completeness. Greedy Vertex Cover

Today s topics. CSE 421 Algorithms. Problem Reduction Examples. Problem Reduction. Undirected Network Flow. Bipartite Matching. Problem Reductions

Algorithm Design and Analysis

Maximum Flow and Minimum Cut

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov)

Algorithm Design and Analysis

CSC 364S Notes University of Toronto, Spring, The networks we will consider are directed graphs, where each edge has associated with it

Soviet Rail Network, 1955

They were originally developed for network problem [Dantzig, Ford, Fulkerson 1956]

6/3/2009. CS 244 Algorithm Design Instructor: t Artur Czumaj. Lecture 8 Network flows. Maximum Flow and Minimum Cut. Minimum Cut Problem.

! Abstraction for material flowing through the edges. ! G = (V, E) = directed graph, no parallel edges.

Network Flows UPCOPENCOURSEWARE number 34414

Basic Tools CMSC 641. Running Time. Problem. Problem. Algorithmic Design Paradigms. lg (n!) (lg n)! (lg n) lgn n.2

Flow networks, flow, maximum flow. Some definitions. Edmonton. Saskatoon Winnipeg. Vancouver Regina. Calgary. 12/12 a.

Matching. Slides designed by Kevin Wayne.

Flow networks. Flow Networks. A flow on a network. Flow networks. The maximum-flow problem. Introduction to Algorithms, Lecture 22 December 5, 2001

Stationary Distribution. Design and Analysis of Algorithms Andrei Bulatov

Maximum Flow. Contents. Max Flow Network. Maximum Flow and Minimum Cut

Wrap up: Weighted, directed graph shortest path Minimum Spanning Tree. Feb 25, 2019 CSCI211 - Sprenkle

16 Max-Flow Algorithms and Applications

arxiv: v1 [cs.cg] 21 Mar 2013

Introduction to Congestion Games

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V

1 Adjusted Parameters

Selfish Routing. Tim Roughgarden Cornell University. Includes joint work with Éva Tardos

Introduction to SLE Lecture Notes

CSE 421 Introduction to Algorithms Winter The Network Flow Problem

CHAPTER 12 DIRECT CURRENT CIRCUITS

Maximum Flow. How do we transport the maximum amount data from source to sink? Some of these slides are adapted from Lecture Notes of Kevin Wayne.

Dynamic Programming 11/8/2009. Weighted Interval Scheduling. Weighted Interval Scheduling. Unweighted Interval Scheduling: Review

Network Flow. Data Structures and Algorithms Andrei Bulatov

Topics in Combinatorial Optimization May 11, Lecture 22

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation

Average Case Lower Bounds for Monotone Switching Networks

Network Flows: Introduction & Maximum Flow

Network Flow Applications

Mechtild Stoer * Frank Wagner** Abstract. fastest algorithm known. The runtime analysis is straightforward. In contrast to

Network flows. The problem. c : V V! R + 0 [ f+1g. flow network G = (V, E, c), a source s and a sink t uv not in E implies c(u, v) = 0

Name: Total Points: Multiple choice questions [120 points]

EECE 301 Signals & Systems Prof. Mark Fowler

MATH 31B: MIDTERM 2 REVIEW. x 2 e x2 2x dx = 1. ue u du 2. x 2 e x2 e x2] + C 2. dx = x ln(x) 2 2. ln x dx = x ln x x + C. 2, or dx = 2u du.

23 Maximum Flows and Minimum Cuts

Waveform Transmission Method, A New Waveform-relaxation Based Algorithm. to Solve Ordinary Differential Equations in Parallel

20/20 20/20 0/5 0/5 20/20 20/20 5/5 0/5 0/5 5/5 0/20 25/30 20/20 30/30 20/20 0/5 5/5 20/20 0/5 0/5 15/20 15/25 20/20 10/10

DETC2004/CIE ALGORITHMIC FOUNDATIONS FOR CONSISTENCY-CHECKING OF INTERACTION-STATES OF MECHATRONIC SYSTEMS

How to Solve System Dynamic s Problems

Price of Stability and Introduction to Mechanism Design

7. NETWORK FLOW I. Lecture slides by Kevin Wayne Copyright 2005 Pearson-Addison Wesley. Last updated on 11/22/17 6:11 AM

Linear Motion, Speed & Velocity

Computer-Aided Analysis of Electronic Circuits Course Notes 3

PHYSICS 151 Notes for Online Lecture #4

EE202 Circuit Theory II

CHAPTER 7: SECOND-ORDER CIRCUITS

Efficient Algorithms for Computing Disjoint QoS Paths

Suggested Practice Problems (set #2) for the Physics Placement Test

7. NETWORK FLOW I. Lecture slides by Kevin Wayne Copyright 2005 Pearson-Addison Wesley. Last updated on 11/22/17 6:11 AM

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max

Solutionbank Edexcel AS and A Level Modular Mathematics

Graph Theory: Network Flow

6. DYNAMIC PROGRAMMING II

Additional Methods for Solving DSGE Models

CMPS 6610/4610 Fall Flow Networks. Carola Wenk Slides adapted from slides by Charles Leiserson

Radical Expressions. Terminology: A radical will have the following; a radical sign, a radicand, and an index.

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff

Ma/CS 6a Class 15: Flows and Bipartite Graphs

Warm Up. Correct order: s,u,v,y,x,w,t

- If one knows that a magnetic field has a symmetry, one may calculate the magnitude of B by use of Ampere s law: The integral of scalar product

KINEMATICS IN ONE DIMENSION

y z P 3 P T P1 P 2. Werner Purgathofer. b a

Reading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4.

I. Introduction to place/transition nets. Place/Transition Nets I. Example: a vending machine. Example: a vending machine

Math 10B: Mock Mid II. April 13, 2016

3/3/2015. Chapter 7. Network Flow. Maximum Flow and Minimum Cut. Minimum Cut Problem

Electrical and current self-induction

INDEX. Transient analysis 1 Initial Conditions 1

COMPETITIVE LOCAL ROUTING WITH CONSTRAINTS

A finitely presented group with unbounded dead-end depth

Transcription:

Maximum Flow in Planar Graph

Planar Graph and i Dual

Dualiy i defined for direced planar graph a well

Minimum - cu in undireced planar graph

An - cu (undireced graph)

An - cu

The dual o he cu

Cu/Cycle A cu ha eparae he graph ino wo conneced componen one conaining and one conaining (we can aume he min-cu i like hi) A imple cycle wih inide and ouide Look for a hore uch cycle in he dual (lengh in he dual are capaciie in he primal)

The face conaining

and he face conaining

Le P be he hore pah beween hem

Any cycle ep. and croe P

The hore cycle will cro P once

The hore cycle will cro i once We are afer he hore cycle ep. and and croe P once.

Finding uch hore cycle

Finding uch hore cycle Claify edge inciden o he pah a lef or righ

Finding uch hore cycle Cu he pah open

Finding uch hore cycle Direc he edge inciden o he pah

Finding uch hore cycle v 1 v 2 Find hore pah beween every pair v 1, v 2

Finding uch hore cycle v 1 v 2 Find hore pah beween every pair v 1, v 2

Finding uch hore cycle v 1 v 2 Find hore pah beween every pair v 1, v 2

Finding uch hore cycle v 1 v 2 Take he hore among hee hore pah

Speeding up by divide and conquer v 1 v 2

Speeding up by divide and conquer Shore cycle do no cro

Speeding up by divide and conquer Shore cycle do no cro

Take v 1 and v 2 o be he middle pair v 1 v 2

Take v 1 and v 2 o be he middle pair v

Spli he problem v

Spli he problem

Add new ource/ink v

In fac Thi ae i ymmeric o our aring poiion v

Analyi Ob1: Pah are horer by a facor of 2 deph of recurion log n v

Analyi Ob2: Each red verex i in one ubproblem (+ and ) Toal ize of ubproblem a level k log(n) i O(n + 2 k ) = O(n) v

Summary Toal ime O(nlog 2 n) uing Dijkra or O(nlog(n)) uing he O(n) SSSP algorihm for planar graph v

Circulaion and price

Circulaion and price β β β β β β

Circulaion and price Decompoe he flow ino CCW cycle Sar wih poenial of 0 For each CCW cycle of value β, add β o he poenial of he face inide he cycle The flow along an edge i he difference in he poenial of i inciden face β 1 β 2 -β 1 β 2

Circulaion and price Any face price define a circulaion he ame way β 1 -β 5 β 1 β 5 β 2 -β 1 β 4 β 5 -β 4 β 2 β 3 β 3 -β 2 β 4 -β 3 β The flow i feaible iff β β u(e) Iff e u(e) + β β 0 (nonnegaive reduced co) β

Circulaion and price Flow i feaible iff e u(e) + β β β β We can ge poenial from any hore pah ree in he dual The reduced co equal he reidual capaciie of he correponding flow.

2 applicaion for hi connecion

Feaible circulaion Negaive capaciy i a lower bound on he flow on he revere arc u(e) < 0 β β A circulaion exi iff here are feaible poenial iff no negaive cycle in he dual Can decide via a hore pah algorihm ha can handle negaive weigh O(mn)

Max - flow when and are on he ame face Find max flow from o when and are on he ame face

Max - flow when and are on he ame face Add an edge from o wih capaciy

Max - flow when and are on he ame face f 2 f 1 Infinie face pli Compue hore pah from f 1 and define a flow according o hee poenial

Max - flow when and are on he ame face f 2 f 1 Infinie face pli Compue hore pah from f 1 and define a flow according o hee poenial

Max - flow when and are on he ame face f 2 Delee he new edge and you ge a maximum flow from o f 1 Proof. I feaible (corre. o p. in he dual), i maximum becaue i equal he minimum -cu (=hore pah from f1 o f2)

Max - flow when and are on he ame face f 2 f 1 Proof. I feaible (corre. o p. in he dual), i maximum becaue i equal he minimum -cu (=hore pah from f 1 o f 2 )