Graphs III - Network Flow

Size: px
Start display at page:

Download "Graphs III - Network Flow"

Transcription

1 Graph III - Nework Flow

2 Flow nework eup graph G=(V,E) edge capaciy w(u,v) 0 - if edge doe no exi, hen w(u,v)=0 pecial verice: ource verex ; ink verex - no edge ino and no edge ou of Aume every verex v i on a pah from ource o ink ( v ) - verice v ha are no on uch pah can be ignored (deleed) along wih all connecing edge

3 Flow nework flow i a funcion f:vxv->r uch ha - flow from u o v: f(u,v) w(u,v) - ymmery f(u,v) = -f(v,u) - flow i conerved on all node excep ource and ink X vv f(u, v) =0 oal flow (from he orce)

4 Maximum Flow Problem deermine he flow f ha realize he maximum oal flow

5 More on Flow f(u,u)=0 oal ne flow ino/ou-o a verex i 0 X vv f(v, u) =0 - excep for ource and ink if edge (u,v) i miing in G, here can be no ne flow from u o v

6 More on Flow poiive ne flow enering v X uv ;f(u,v)>0 f(u, v) poiive ne flow leaving v hee wo are equal X f(v, u) uv ;f(v,u)>0

7 Cancellaion poiive flow on (u,v) cancel poiive flow on (v,u) unil only one i poiive (he oher become 0) - boh flow decreae, o hey ill aify capaciy conrain - flow conervaion aified ince boh flow reduced by he ame amoun

8 Ford-Fulkeron wan he max flow for ource o ink - a cla of algorihm, no a ingle one iniialize flow wih O; repea find an augmening pah from o (ha admi more flow) end more flow on ha pah unil no augmening pah exi have o prove ha hi erminaion condiion implie he flow i max. - if an augmening pah exi, ending more flow o i increae he value of he exiing flow

9 Reidual nework afer ending ome flow on a pah from o, he graph eenially change - exiing flow edge will have a differen capaciy in he reidual nework (becaue he flow ue ome) - new edge can appear (in red) : he poibiliie of revering he exiing flow

10 Reidual nework reidual capaciy of edge (u,v) : r(u,v) = w(u,v)-f(u,v) reidual nework R induced by f i given by he e of edge alo called R wih poiive reidual capaciy - edge e R = { (u,v) : r(u,v)>0 } noe ha ome new edge appear! u 3 v - example (u,v) E; w(u,v)=3, f(u,v)= - hen r(u,v) = 3- = - edge (v,u) no in he original graph u v - bu r(v,u) = 0 - f(v,u) = 0- (-)=; herefore edge (v,u) i now par of he reidual nework. edge (v,u) can be par of he reidual newwork only if eiher (u,v) or (v,u) are edge in he original graph - hu R E

11 Augmening pah any pah p= -> in he reidual nework R he reidual capaciy of he pah p i he minimum ( boleneck ) edge reidual capaciy - r(p) = min { r(u,v) : (u,v) p } add he pah p a addiional flow f p of ize r(p) - o he exiing flow f ha creaed R - new flow f = f + f p - increae he flow oal by r(p). Proof in he book.

12 Cu in flow nework Cu C = (S,T) i a pariion of verice - S T=V ; S T= ; S=V T - S conain he ource and T conain he ink S; T Ne flow acro cu i f(s,t), he um of all flow on edge from S o T f(s,t)=3 ; w(s,t)=5 capaciy of a cu i he um of edge capaciy from S o T

13 Max Flow - Min Cu heorem (S,T) i a cu, f a flow wih oal value f. Then - f(s,t) = f (he oal flow value) - conequenly f w(s,t) : flow value i maller han he capaciy of any cu MAX-FLOW MIN-CUT heorem. The following are equivalen: - (a) f i a max flow - (b) reidual nework R=R f ha no augmening pah - (c) here i a cu (S,T) uch ha f = w(s,t)

14 Max Flow - Min Cu proof inuiion (a)=>(b) already dicued (b)=>(c): conider S = { v pah v in reidual R} - S - T =V S; T. If S, hen here would be a augmening pah in R - R can have an edge (v S, u T) becaue ha would mean u S - hu exiing flow aurae he cu (S,T) v v v impoible v u u u (c)=>(a): no flow can be bigger han capaciy of a cu, o f mu be a maximum flow (ince i aurae he cu decribed above)

15 Ford-Fulkeron for each edge (u,v) ini: f(u,v)=0; f(v,u)=0 R = G while exi pah p( )in reidual R c(p) = min { r(u,v); (u,v) p }//pah capaciy, ued a new flow for each (u,v) p f(u,v) = f(u,v) + c(p) ; f(v,u)= - f(u,v) recompue reidual nework R=Rf

16 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

17 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

18 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

19 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

20 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

21 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

22 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

23 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

24 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

25 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

26 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

27 Ford-Fulkeron Running ime wih ineger capaciie - fing a pah in R i O(E) (ay wih DFS) - f =oal flow value - a mo f ieraion; every ieraion increae he flow by or more - oal O(E* f ) problem: f can be very large, hu he algorihm very low - for real-value edge cacpaciie, Ford-Fulkeron can be arbirary low

28 Edmond-Karp ame a FF, bu find he augmening pah wih BFS for each edge (u,v) ini: f(u,v)=0; f(v,u)=0 R = G while BFS find pah p( )in reidual R c(p) = min { r(u,v); (u,v) p }//pah capaciy, ued a new flow for each (u,v) p f(u,v) = f(u,v) + c(p) ; f(v,u)= - f(u,v) recompue reidual nework R=Rf

29 Analyi of Edmond-Karp BFS will find he augmening pah wih fewe number of edge noe ha previu oy bad example would find max flow afer wo ieraion

30 Analyi of Edmond-Karp BFS will find he augmening pah wih fewe number of edge noe ha previu oy bad example would find max flow afer wo ieraion

31 Analyi of Edmond-Karp BFS will find he augmening pah wih fewe number of edge noe ha previu oy bad example would find max flow afer wo ieraion

32 Analyi of Edmond-Karp BFS will find he augmening pah wih fewe number of edge noe ha previu oy bad example would find max flow afer wo ieraion

33 Analyi of Edmond-Karp BFS will find he augmening pah wih fewe number of edge noe ha previu oy bad example would find max flow afer wo ieraion

34 Analyi of Edmond-Karp How many augmening pah can EK find? - augmening pah p ha criical edge (u,v), if (u,v) i he minimum reidual capaciy edge on he pah - any edge can be criical a mo V ime during EK. Proof in he book - here are E edge, o a mo V * E criical edge for he enire execuion - hu a mo O(VE) augmening pah (each pah ha a lea one criical edge) BFS ake O(E) o find each augmening pah oal O(VE )

35 Puh-Relabel (Opional reading) Advanced maerial no covered - opional reading from book inuiion : flood he nework, uing verex heigh - node can accumulae flow - he more flow hey accumulae, he higher hey go - flow goe downhill pracical / fa implemenaion: O(V 3 ) running ime.

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

Flow networks. Flow Networks. A flow on a network. Flow networks. The maximum-flow problem. Introduction to Algorithms, Lecture 22 December 5, 2001 CS 545 Flow Nework lon Efra Slide courey of Charle Leieron wih mall change by Carola Wenk Flow nework Definiion. flow nework i a direced graph G = (V, E) wih wo diinguihed verice: a ource and a ink. Each

More information

Today: Max Flow Proofs

Today: Max Flow Proofs Today: Max Flow Proof COSC 58, Algorihm March 4, 04 Many of hee lide are adaped from everal online ource Reading Aignmen Today cla: Chaper 6 Reading aignmen for nex cla: Chaper 7 (Amorized analyi) In-Cla

More information

1 Motivation and Basic Definitions

1 Motivation and Basic Definitions CSCE : Deign and Analyi of Algorihm Noe on Max Flow Fall 20 (Baed on he preenaion in Chaper 26 of Inroducion o Algorihm, 3rd Ed. by Cormen, Leieron, Rive and Sein.) Moivaion and Baic Definiion Conider

More information

CS4445/9544 Analysis of Algorithms II Solution for Assignment 1

CS4445/9544 Analysis of Algorithms II Solution for Assignment 1 Conider he following flow nework CS444/944 Analyi of Algorihm II Soluion for Aignmen (0 mark) In he following nework a minimum cu ha capaciy 0 Eiher prove ha hi aemen i rue, or how ha i i fale Uing he

More information

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

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow. CSE 202: Deign and Analyi of Algorihm Winer 2013 Problem Se 3 Inrucor: Kamalika Chaudhuri Due on: Tue. Feb 26, 2013 Inrucion For your proof, you may ue any lower bound, algorihm or daa rucure from he ex

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorihm Deign and Analyi LECTURES 17 Nework Flow Dualiy of Max Flow and Min Cu Algorihm: Ford-Fulkeron Capaciy Scaling Sofya Rakhodnikova S. Rakhodnikova; baed on lide by E. Demaine, C. Leieron, A. Smih,

More information

Network Flow. Data Structures and Algorithms Andrei Bulatov

Network Flow. Data Structures and Algorithms Andrei Bulatov Nework Flow Daa Srucure and Algorihm Andrei Bulao Algorihm Nework Flow 24-2 Flow Nework Think of a graph a yem of pipe We ue hi yem o pump waer from he ource o ink Eery pipe/edge ha limied capaciy Flow

More information

Main Reference: Sections in CLRS.

Main Reference: Sections in CLRS. Maximum Flow Reied 09/09/200 Main Reference: Secion 26.-26. in CLRS. Inroducion Definiion Muli-Source Muli-Sink The Ford-Fulkeron Mehod Reidual Nework Augmening Pah The Max-Flow Min-Cu Theorem The Edmond-Karp

More information

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

Max Flow, Min Cut COS 521. Kevin Wayne Fall Soviet Rail Network, Cuts. Minimum Cut Problem. Flow network. Sovie Rail Nework, Max Flow, Min u OS Kevin Wayne Fall Reference: On he hiory of he ranporaion and maximum flow problem. lexander Schrijver in Mah Programming, :,. Minimum u Problem u Flow nework.! Digraph

More information

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

The Residual Graph. 12 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm Augmening Pah Algorihm Greedy-algorihm: ar wih f (e) = everywhere find an - pah wih f (e) < c(e) on every edge augmen flow along he pah repea a long a poible The Reidual Graph From he graph G = (V, E,

More information

Network Flows: Introduction & Maximum Flow

Network Flows: Introduction & Maximum Flow CSC 373 - lgorihm Deign, nalyi, and Complexiy Summer 2016 Lalla Mouaadid Nework Flow: Inroducion & Maximum Flow We now urn our aenion o anoher powerful algorihmic echnique: Local Search. In a local earch

More information

Maximum Flow and Minimum Cut

Maximum Flow and Minimum Cut // Sovie Rail Nework, Maximum Flow and Minimum Cu Max flow and min cu. Two very rich algorihmic problem. Cornerone problem in combinaorial opimizaion. Beauiful mahemaical dualiy. Nework Flow Flow nework.

More information

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

Flow networks, flow, maximum flow. Some definitions. Edmonton. Saskatoon Winnipeg. Vancouver Regina. Calgary. 12/12 a. Flow nework, flow, maximum flow Can inerpre direced graph a flow nework. Maerial coure hrough ome yem from ome ource o ome ink. Source produce maerial a ome eady rae, ink conume a ame rae. Example: waer

More information

Algorithmic Discrete Mathematics 6. Exercise Sheet

Algorithmic Discrete Mathematics 6. Exercise Sheet Algorihmic Dicree Mahemaic. Exercie Shee Deparmen of Mahemaic SS 0 PD Dr. Ulf Lorenz 7. and 8. Juni 0 Dipl.-Mah. David Meffer Verion of June, 0 Groupwork Exercie G (Heap-Sor) Ue Heap-Sor wih a min-heap

More information

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

The Residual Graph. 11 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm Augmening Pah Algorihm Greedy-algorihm: ar wih f (e) = everywhere find an - pah wih f (e) < c(e) on every edge augmen flow along he pah repea a long a poible The Reidual Graph From he graph G = (V, E,

More information

Soviet Rail Network, 1955

Soviet Rail Network, 1955 Sovie Rail Nework, 1 Reference: On he hiory of he ranporaion and maximum flow problem. Alexander Schrijver in Mah Programming, 1: 3,. Maximum Flow and Minimum Cu Max flow and min cu. Two very rich algorihmic

More information

CSE 521: Design & Analysis of Algorithms I

CSE 521: Design & Analysis of Algorithms I CSE 52: Deign & Analyi of Algorihm I Nework Flow Paul Beame Biparie Maching Given: A biparie graph G=(V,E) M E i a maching in G iff no wo edge in M hare a verex Goal: Find a maching M in G of maximum poible

More information

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

CSC 364S Notes University of Toronto, Spring, The networks we will consider are directed graphs, where each edge has associated with it CSC 36S Noe Univeriy of Torono, Spring, 2003 Flow Algorihm The nework we will conider are direced graph, where each edge ha aociaed wih i a nonnegaive capaciy. The inuiion i ha if edge (u; v) ha capaciy

More information

Soviet Rail Network, 1955

Soviet Rail Network, 1955 7.1 Nework Flow Sovie Rail Nework, 19 Reerence: On he hiory o he ranporaion and maximum low problem. lexander Schrijver in Mah Programming, 91: 3, 00. (See Exernal Link ) Maximum Flow and Minimum Cu Max

More information

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

Flow Networks. Ma/CS 6a. Class 14: Flow Exercises 0/0/206 Ma/CS 6a Cla 4: Flow Exercie Flow Nework A flow nework i a digraph G = V, E, ogeher wih a ource verex V, a ink verex V, and a capaciy funcion c: E N. Capaciy Source 7 a b c d e Sink 0/0/206 Flow

More information

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

! Abstraction for material flowing through the edges. ! G = (V, E) = directed graph, no parallel edges. Sovie Rail Nework, haper Nework Flow Slide by Kevin Wayne. opyrigh Pearon-ddion Weley. ll righ reerved. Reference: On he hiory of he ranporaion and maximum flow problem. lexander Schrijver in Mah Programming,

More information

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

Greedy. I Divide and Conquer. I Dynamic Programming. I Network Flows. Network Flow. I Previous topics: design techniques Algorihm Deign Technique CS : Nework Flow Dan Sheldon April, reedy Divide and Conquer Dynamic Programming Nework Flow Comparion Nework Flow Previou opic: deign echnique reedy Divide and Conquer Dynamic

More information

Matching. Slides designed by Kevin Wayne.

Matching. Slides designed by Kevin Wayne. Maching Maching. Inpu: undireced graph G = (V, E). M E i a maching if each node appear in a mo edge in M. Max maching: find a max cardinaliy maching. Slide deigned by Kevin Wayne. Biparie Maching Biparie

More information

Reminder: Flow Networks

Reminder: Flow Networks 0/0/204 Ma/CS 6a Cla 4: Variou (Flow) Execie Reminder: Flow Nework A flow nework i a digraph G = V, E, ogeher wih a ource verex V, a ink verex V, and a capaciy funcion c: E N. Capaciy Source 7 a b c d

More information

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)

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) /0/ dmin lunch oday rading MX LOW PPLIION 0, pring avid Kauchak low graph/nework low nework direced, weighed graph (V, ) poiive edge weigh indicaing he capaciy (generally, aume ineger) conain a ingle ource

More information

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

6/3/2009. CS 244 Algorithm Design Instructor: t Artur Czumaj. Lecture 8 Network flows. Maximum Flow and Minimum Cut. Minimum Cut Problem. Maximum Flow and Minimum Cu CS lgorihm Deign Inrucor: rur Czumaj Lecure Nework Max and min cu. Two very rich algorihmic problem. Cornerone problem in combinaorial opimizaion. Beauiful mahemaical dualiy.

More information

CSE 421 Introduction to Algorithms Winter The Network Flow Problem

CSE 421 Introduction to Algorithms Winter The Network Flow Problem CSE 42 Inroducion o Algorihm Winer 202 The Nework Flow Problem 2 The Nework Flow Problem 5 a 4 3 x 3 7 6 b 4 y 4 7 6 c 5 z How much uff can flow from o? 3 Sovie Rail Nework, 955 Reference: On he hiory

More information

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

CS 473G Lecture 15: Max-Flow Algorithms and Applications Fall 2005 CS 473G Lecure 1: Max-Flow Algorihm and Applicaion Fall 200 1 Max-Flow Algorihm and Applicaion (November 1) 1.1 Recap Fix a direced graph G = (V, E) ha doe no conain boh an edge u v and i reveral v u,

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorihm Deign and Analyi LECTURE 0 Nework Flow Applicaion Biparie maching Edge-dijoin pah Adam Smih 0//0 A. Smih; baed on lide by E. Demaine, C. Leieron, S. Rakhodnikova, K. Wayne La ime: Ford-Fulkeron

More information

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

MAXIMUM FLOW. introduction Ford-Fulkerson algorithm maxflow-mincut theorem MAXIMUM FLOW inroducion Ford-Fulkeron algorihm maxflow-mincu heorem Mincu problem Inpu. An edge-weighed digraph, ource verex, and arge verex. each edge ha a poiive capaciy capaciy 9 10 4 15 15 10 5 8 10

More information

18 Extensions of Maximum Flow

18 Extensions of Maximum Flow Who are you?" aid Lunkwill, riing angrily from hi ea. Wha do you wan?" I am Majikhie!" announced he older one. And I demand ha I am Vroomfondel!" houed he younger one. Majikhie urned on Vroomfondel. I

More information

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

4/12/12. Applications of the Maxflow Problem 7.5 Bipartite Matching. Bipartite Matching. Bipartite Matching. Bipartite matching: the flow network // Applicaion of he Maxflow Problem. Biparie Maching Biparie Maching Biparie maching. Inpu: undireced, biparie graph = (, E). M E i a maching if each node appear in a mo one edge in M. Max maching: find

More information

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

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov) Algorihm and Daa Srucure 2011/ Week Soluion (Tue 15h - Fri 18h No) 1. Queion: e are gien 11/16 / 15/20 8/13 0/ 1/ / 11/1 / / To queion: (a) Find a pair of ube X, Y V uch ha f(x, Y) = f(v X, Y). (b) Find

More information

26.1 Flow networks. f (u,v) = 0.

26.1 Flow networks. f (u,v) = 0. 26 Maimum Flow Ju a we can model a road map a a direced graph in order o find he hore pah from one poin o anoher, we can alo inerpre a direced graph a a flow nework and ue i o anwer queion abou maerial

More information

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.

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. Conen Conen. Maximum flow problem. Minimum cu problem. Max-flow min-cu heorem. Augmening pah algorihm. Capaciy-caling. Shore augmening pah. Chaper Maximum How do we ranpor he maximum amoun daa from ource

More information

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

Maximum Flow. Contents. Max Flow Network. Maximum Flow and Minimum Cut Conen Maximum Flow Conen. Maximum low problem. Minimum cu problem. Max-low min-cu heorem. Augmening pah algorihm. Capaciy-caling. Shore augmening pah. Princeon Univeriy COS Theory o Algorihm Spring Kevin

More information

Network Flows UPCOPENCOURSEWARE number 34414

Network Flows UPCOPENCOURSEWARE number 34414 Nework Flow UPCOPENCOURSEWARE number Topic : F.-Javier Heredia Thi work i licened under he Creaive Common Aribuion- NonCommercial-NoDeriv. Unpored Licene. To view a copy of hi licene, vii hp://creaivecommon.org/licene/by-nc-nd/./

More information

16 Max-Flow Algorithms and Applications

16 Max-Flow Algorithms and Applications Algorihm A proce canno be underood by opping i. Underanding mu move wih he flow of he proce, mu join i and flow wih i. The Fir Law of Mena, in Frank Herber Dune (196) There a difference beween knowing

More information

Introduction to Congestion Games

Introduction to Congestion Games Algorihmic Game Theory, Summer 2017 Inroducion o Congeion Game Lecure 1 (5 page) Inrucor: Thoma Keelheim In hi lecure, we ge o know congeion game, which will be our running example for many concep in game

More information

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

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 Nework flow The problem Seing flow nework G = (V, E, c), a orce and a ink no in E implie c(, ) = 0 Flow from o capaciy conrain kew-ymmery flow-coneraion ale of he flow jfj = P 2V Find a maximm flow from

More information

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

CMPS 6610/4610 Fall Flow Networks. Carola Wenk Slides adapted from slides by Charles Leiserson CMP 6610/4610 Fall 2016 Flow Nework Carola Wenk lide adaped rom lide by Charle Leieron Max low and min c Fndamenal problem in combinaorial opimizaion Daliy beween max low and min c Many applicaion: Biparie

More information

23 Maximum Flows and Minimum Cuts

23 Maximum Flows and Minimum Cuts A proce canno be underood by opping i. Underanding mu move wih he flow of he proce, mu join i and flow wih i. The Fir Law of Mena, in Frank Herber Dune (196) Conrary o expecaion, flow uually happen no

More information

Algorithms. Algorithms 6.4 MAXIMUM FLOW

Algorithms. Algorithms 6.4 MAXIMUM FLOW Algorihm ROBERT SEDGEWICK KEVIN WAYNE 6.4 MAXIMUM FLOW Algorihm F O U R T H E D I T I O N ROBERT SEDGEWICK KEVIN WAYNE hp://alg4.c.princeon.edu inroducion Ford Fulkeron algorihm maxflow mincu heorem analyi

More information

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

Basic Tools CMSC 641. Running Time. Problem. Problem. Algorithmic Design Paradigms. lg (n!) (lg n)! (lg n) lgn n.2 Baic Tool CMSC April, Review Aympoic Noaion Order of Growh Recurrence relaion Daa Srucure Li, Heap, Graph, Tree, Balanced Tree, Hah Table Advanced daa rucure: Binomial Heap, Fibonacci Heap Soring Mehod

More information

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

Today s topics. CSE 421 Algorithms. Problem Reduction Examples. Problem Reduction. Undirected Network Flow. Bipartite Matching. Problem Reductions Today opic CSE Algorihm Richard Anderon Lecure Nework Flow Applicaion Prolem Reducion Undireced Flow o Flow Biparie Maching Dijoin Pah Prolem Circulaion Loweround conrain on flow Survey deign Prolem Reducion

More information

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

They were originally developed for network problem [Dantzig, Ford, Fulkerson 1956] 6. Inroducion... 6. The primal-dual algorihmn... 6 6. Remark on he primal-dual algorihmn... 7 6. A primal-dual algorihmn for he hore pah problem... 8... 9 6.6 A primal-dual algorihmn for he weighed maching

More information

Introduction to SLE Lecture Notes

Introduction to SLE Lecture Notes Inroducion o SLE Lecure Noe May 13, 16 - The goal of hi ecion i o find a ufficien condiion of λ for he hull K o be generaed by a imple cure. I urn ou if λ 1 < 4 hen K i generaed by a imple curve. We will

More information

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

7. NETWORK FLOW I. Lecture slides by Kevin Wayne Copyright 2005 Pearson-Addison Wesley. Last updated on 11/22/17 6:11 AM 7. NETWORK FLOW I max-flow and min-cu problem Ford Fulkeron algorihm max-flow min-cu heorem capaciy-caling algorihm hore augmening pah blocking-flow algorihm imple uni-capaciy nework Lecure lide by Kevin

More information

Maximum Flow 5/6/17 21:08. Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015

Maximum Flow 5/6/17 21:08. Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015 Maximm Flo 5/6/17 21:08 Preenaion for e ih he exbook, Algorihm Deign and Applicaion, by M. T. Goodrich and R. Tamaia, Wiley, 2015 Maximm Flo χ 4/6 4/7 1/9 2015 Goodrich and Tamaia Maximm Flo 1 Flo Neork

More information

Maximum Flow in Planar Graphs

Maximum Flow in Planar Graphs 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

More information

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

Please Complete Course Survey. CMPSCI 311: Introduction to Algorithms. Approximation Algorithms. Coping With NP-Completeness. Greedy Vertex Cover Pleae Complee Coure Survey CMPSCI : Inroducion o Algorihm Dealing wih NP-Compleene Dan Sheldon hp: //owl.oi.uma.edu/parner/coureevalsurvey/uma/ Univeriy of Maachue Slide Adaped from Kevin Wayne La Compiled:

More information

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

7. NETWORK FLOW I. Lecture slides by Kevin Wayne Copyright 2005 Pearson-Addison Wesley. Last updated on 11/22/17 6:11 AM 7. NETWORK FLOW I max-flow and min-cu problem Ford Fulkeron algorihm max-flow min-cu heorem capaciy-caling algorihm hore augmening pah blocking-flow algorihm imple uni-capaciy nework Lecure lide by Kevin

More information

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

3/3/2015. Chapter 7. Network Flow. Maximum Flow and Minimum Cut. Minimum Cut Problem // Chaper Nework Flow Maximum Flow and Minimum Cu Max flow and min cu. Two very rich algorihmic problem. Cornerone problem in combinaorial opimizaion. Beauiful mahemaical dualiy. Nonrivial applicaion /

More information

How to Solve System Dynamic s Problems

How to Solve System Dynamic s Problems How o Solve Sye Dynaic Proble A ye dynaic proble involve wo or ore bodie (objec) under he influence of everal exernal force. The objec ay uliaely re, ove wih conan velociy, conan acceleraion or oe cobinaion

More information

introduction Ford-Fulkerson algorithm

introduction Ford-Fulkerson algorithm Algorihm ROBERT SEDGEWICK KEVIN WAYNE. MAXIMUM FLOW. MAXIMUM FLOW inroducion inroducion Ford-Fulkeron algorihm Ford-Fulkeron algorihm Algorihm F O U R T H E D I T I O N maxflow-mincu heorem analyi of running

More information

April 3, The maximum flow problem. See class notes on website.

April 3, The maximum flow problem. See class notes on website. 5.05 April, 007 The maximum flow problem See cla noe on webie. Quoe of he day You ge he maxx for he minimum a TJ Maxx. -- ad for a clohing ore Thi wa he mo unkinde cu of all -- Shakepeare in Juliu Caear

More information

CHAPTER 7: SECOND-ORDER CIRCUITS

CHAPTER 7: SECOND-ORDER CIRCUITS EEE5: CI RCUI T THEORY CHAPTER 7: SECOND-ORDER CIRCUITS 7. Inroducion Thi chaper conider circui wih wo orage elemen. Known a econd-order circui becaue heir repone are decribed by differenial equaion ha

More information

16 Max-Flow Algorithms

16 Max-Flow Algorithms A process canno be undersood by sopping i. Undersanding mus move wih he flow of he process, mus join i and flow wih i. The Firs Law of Mena, in Frank Herber s Dune (196) There s a difference beween knowing

More information

Ford-Fulkerson Algorithm for Maximum Flow

Ford-Fulkerson Algorithm for Maximum Flow Ford-Fulkerson Algorihm for Maximum Flow 1. Assign an iniial flow f ij (for insance, f ij =0) for all edges.label s by Ø. Mark he oher verices "unlabeled.". Find a labeled verex i ha has no ye been scanned.

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

introduction Ford-Fulkerson algorithm

introduction Ford-Fulkerson algorithm Algorihm ROBERT SEDGEWICK KEVIN WAYNE. MAXIMUM FLOW. MAXIMUM FLOW inroducion inroducion Ford-Fulkeron algorihm Ford-Fulkeron algorihm Algorihm F O U R T H E D I T I O N maxflow-mincu heorem analyi of running

More information

Maximum Flow. Flow Graph

Maximum Flow. Flow Graph Mximum Flow Chper 26 Flow Grph A ommon enrio i o ue grph o repreen flow nework nd ue i o nwer queion ou meril flow Flow i he re h meril move hrough he nework Eh direed edge i ondui for he meril wih ome

More information

Notes for Lecture 17-18

Notes for Lecture 17-18 U.C. Berkeley CS278: Compuaional Complexiy Handou N7-8 Professor Luca Trevisan April 3-8, 2008 Noes for Lecure 7-8 In hese wo lecures we prove he firs half of he PCP Theorem, he Amplificaion Lemma, up

More information

CHAPTER 12 DIRECT CURRENT CIRCUITS

CHAPTER 12 DIRECT CURRENT CIRCUITS CHAPTER 12 DIRECT CURRENT CIUITS DIRECT CURRENT CIUITS 257 12.1 RESISTORS IN SERIES AND IN PARALLEL When wo resisors are conneced ogeher as shown in Figure 12.1 we said ha hey are conneced in series. As

More information

CS Lunch This Week. Special Talk This Week. Soviet Rail Network, Flow Networks. Slides20 - Network Flow Intro.key - December 5, 2016

CS Lunch This Week. Special Talk This Week. Soviet Rail Network, Flow Networks. Slides20 - Network Flow Intro.key - December 5, 2016 CS Lunch This Week Panel on Sudying Engineering a MHC Wednesday, December, : Kendade Special Talk This Week Learning o Exrac Local Evens from he Web John Foley, UMass Thursday, December, :, Carr Sovie

More information

8. Basic RL and RC Circuits

8. Basic RL and RC Circuits 8. Basic L and C Circuis This chaper deals wih he soluions of he responses of L and C circuis The analysis of C and L circuis leads o a linear differenial equaion This chaper covers he following opics

More information

u(t) Figure 1. Open loop control system

u(t) Figure 1. Open loop control system Open loop conrol v cloed loop feedbac conrol The nex wo figure preen he rucure of open loop and feedbac conrol yem Figure how an open loop conrol yem whoe funcion i o caue he oupu y o follow he reference

More information

Ford Fulkerson algorithm max-flow min-cut theorem. max-flow min-cut theorem capacity-scaling algorithm

Ford Fulkerson algorithm max-flow min-cut theorem. max-flow min-cut theorem capacity-scaling algorithm 7. NETWORK FLOW I 7. NETWORK FLOW I max-flow and min-cu problem max-flow and min-cu problem Ford Fulkeron algorihm Ford Fulkeron algorihm max-flow min-cu heorem max-flow min-cu heorem capaciy-caling algorihm

More information

Graduate Algorithms CS F-18 Flow Networks

Graduate Algorithms CS F-18 Flow Networks Grue Algorihm CS673-2016F-18 Flow Nework Dvi Glle Deprmen of Compuer Siene Univeriy of Sn Frnio 18-0: Flow Nework Diree Grph G Eh ege weigh i piy Amoun of wer/eon h n flow hrough pipe, for inne Single

More information

Topics in Combinatorial Optimization May 11, Lecture 22

Topics in Combinatorial Optimization May 11, Lecture 22 8.997 Topics in Combinaorial Opimizaion May, 004 Lecure Lecurer: Michel X. Goemans Scribe: Alanha Newman Muliflows an Disjoin Pahs Le G = (V,E) be a graph an le s,,s,,...s, V be erminals. Our goal is o

More information

Today. Flow review. Augmenting paths. Ford-Fulkerson Algorithm. Intro to cuts (reason: prove correctness)

Today. Flow review. Augmenting paths. Ford-Fulkerson Algorithm. Intro to cuts (reason: prove correctness) Today Flow review Augmenting path Ford-Fulkeron Algorithm Intro to cut (reaon: prove correctne) Flow Network = ource, t = ink. c(e) = capacity of edge e Capacity condition: f(e) c(e) Conervation condition:

More information

Graph Theory: Network Flow

Graph Theory: Network Flow Univeriy of Wahingon Mah 336 Term Paper Graph Theory: Nework Flow Auhor: Ellio Broard Advier: Dr. Jame Morrow 3 June 200 Conen Inroducion...................................... 2 2 Terminology......................................

More information

Phys1112: DC and RC circuits

Phys1112: DC and RC circuits Name: Group Members: Dae: TA s Name: Phys1112: DC and RC circuis Objecives: 1. To undersand curren and volage characerisics of a DC RC discharging circui. 2. To undersand he effec of he RC ime consan.

More information

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

Ma/CS 6a Class 15: Flows and Bipartite Graphs //206 Ma/CS 6a Cla : Flow and Bipari Graph By Adam Shffr Rmindr: Flow Nwork A flow nwork i a digraph G = V, E, oghr wih a ourc vrx V, a ink vrx V, and a capaciy funcion c: E N. Capaciy Sourc 7 a b c d

More information

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

Selfish Routing. Tim Roughgarden Cornell University. Includes joint work with Éva Tardos Selfih Rouing Tim Roughgarden Cornell Univeriy Include join work wih Éva Tardo 1 Which roue would you chooe? Example: one uni of raffic (e.g., car) wan o go from o delay = 1 hour (no congeion effec) long

More information

Maximum Flow 3/3 4/6 1/1 4/7 3/3. s 3/5 1/9 1/1 3/5 2/2. 1/18/2005 4:03 AM Maximum Flow 1

Maximum Flow 3/3 4/6 1/1 4/7 3/3. s 3/5 1/9 1/1 3/5 2/2. 1/18/2005 4:03 AM Maximum Flow 1 Maximm Flo χ 4/6 4/7 1/9 8/2005 4:03 AM Maximm Flo 1 Oline and Reading Flo neork Flo ( 8.1.1) C ( 8.1.2) Maximm flo Agmening pah ( 8.2.1) Maximm flo and minimm c ( 8.2.1) Ford-Flkeron algorihm ( 8.2.2-8.2.3)

More information

, 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

, 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 ecure 8 7. Sabiliy Analyi For an n dimenional vecor R n, he and he vecor norm are defined a: = T = i n i (7.) I i eay o how ha hee wo norm aify he following relaion: n (7.) If a vecor i ime-dependen, hen

More information

Longest Common Prefixes

Longest Common Prefixes Longes Common Prefixes The sandard ordering for srings is he lexicographical order. I is induced by an order over he alphabe. We will use he same symbols (,

More information

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

Dynamic Programming 11/8/2009. Weighted Interval Scheduling. Weighted Interval Scheduling. Unweighted Interval Scheduling: Review //9 Algorihms Dynamic Programming - Weighed Ineral Scheduling Dynamic Programming Weighed ineral scheduling problem. Insance A se of n jobs. Job j sars a s j, finishes a f j, and has weigh or alue j. Two

More information

Timed Circuits. Asynchronous Circuit Design. Timing Relationships. A Simple Example. Timed States. Timing Sequences. ({r 6 },t6 = 1.

Timed Circuits. Asynchronous Circuit Design. Timing Relationships. A Simple Example. Timed States. Timing Sequences. ({r 6 },t6 = 1. Timed Circuis Asynchronous Circui Design Chris J. Myers Lecure 7: Timed Circuis Chaper 7 Previous mehods only use limied knowledge of delays. Very robus sysems, bu exremely conservaive. Large funcional

More information

Solutions to Assignment 1

Solutions to Assignment 1 MA 2326 Differenial Equaions Insrucor: Peronela Radu Friday, February 8, 203 Soluions o Assignmen. Find he general soluions of he following ODEs: (a) 2 x = an x Soluion: I is a separable equaion as we

More information

EXERCISES FOR SECTION 1.5

EXERCISES FOR SECTION 1.5 1.5 Exisence and Uniqueness of Soluions 43 20. 1 v c 21. 1 v c 1 2 4 6 8 10 1 2 2 4 6 8 10 Graph of approximae soluion obained using Euler s mehod wih = 0.1. Graph of approximae soluion obained using Euler

More information

CS4800: Algorithms & Data Jonathan Ullman

CS4800: Algorithms & Data Jonathan Ullman CS800: Algorithm & Data Jonathan Ullman Lecture 17: Network Flow Chooing Good Augmenting Path Mar 0, 018 Recap Directed graph! = #, % Two pecial node: ource & and ink = ' Edge capacitie ( ) 9 5 15 15 ource

More information

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

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 Annoncemen CSEP Applied Algorihm Richard Anderon Lecre 9 Nework Flow Applicaion Reading for hi week 7.-7.. Nework flow applicaion Nex week: Chaper 8. NP-Compleene Final exam, March 8, 6:0 pm. A UW. hor

More information

Average Case Lower Bounds for Monotone Switching Networks

Average Case Lower Bounds for Monotone Switching Networks Average Cae Lower Bound for Monoone Swiching Nework Yuval Filmu, Toniann Piai, Rober Robere, Sephen Cook Deparmen of Compuer Science Univeriy of Torono Monoone Compuaion (Refreher) Monoone circui were

More information

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2)

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2) Laplace Tranform Maoud Malek The Laplace ranform i an inegral ranform named in honor of mahemaician and aronomer Pierre-Simon Laplace, who ued he ranform in hi work on probabiliy heory. I i a powerful

More information

Electrical and current self-induction

Electrical and current self-induction Elecrical and curren self-inducion F. F. Mende hp://fmnauka.narod.ru/works.hml mende_fedor@mail.ru Absrac The aricle considers he self-inducance of reacive elemens. Elecrical self-inducion To he laws of

More information

Exponential Sawtooth

Exponential Sawtooth ECPE 36 HOMEWORK 3: PROPERTIES OF THE FOURIER TRANSFORM SOLUTION. Exponenial Sawooh: The eaie way o do hi problem i o look a he Fourier ranform of a ingle exponenial funcion, () = exp( )u(). From he able

More information

UCLA: Math 3B Problem set 3 (solutions) Fall, 2018

UCLA: Math 3B Problem set 3 (solutions) Fall, 2018 UCLA: Mah 3B Problem se 3 (soluions) Fall, 28 This problem se concenraes on pracice wih aniderivaives. You will ge los of pracice finding simple aniderivaives as well as finding aniderivaives graphically

More information

18.03SC Unit 3 Practice Exam and Solutions

18.03SC Unit 3 Practice Exam and Solutions Sudy Guide on Sep, Dela, Convoluion, Laplace You can hink of he ep funcion u() a any nice mooh funcion which i for < a and for > a, where a i a poiive number which i much maller han any ime cale we care

More information

!!"#"$%&#'()!"#&'(*%)+,&',-)./0)1-*23)

!!#$%&#'()!#&'(*%)+,&',-)./0)1-*23) "#"$%&#'()"#&'(*%)+,&',-)./)1-*) #$%&'()*+,&',-.%,/)*+,-&1*#$)()5*6$+$%*,7&*-'-&1*(,-&*6&,7.$%$+*&%'(*8$&',-,%'-&1*(,-&*6&,79*(&,%: ;..,*&1$&$.$%&'()*1$$.,'&',-9*(&,%)?%*,('&5

More information

Discussion Session 2 Constant Acceleration/Relative Motion Week 03

Discussion Session 2 Constant Acceleration/Relative Motion Week 03 PHYS 100 Dicuion Seion Conan Acceleraion/Relaive Moion Week 03 The Plan Today you will work wih your group explore he idea of reference frame (i.e. relaive moion) and moion wih conan acceleraion. You ll

More information

Efficient Algorithms for Computing Disjoint QoS Paths

Efficient Algorithms for Computing Disjoint QoS Paths Efficien Algorihm for Compuing Dijoin QoS Pah Ariel Orda and Alexander Sprinon 1 Deparmen of Elecrical Engineering, Technion Irael Iniue of Technology, Haifa, Irael 32000 Email: ariel@eeechnionacil Parallel

More information

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship Laplace Tranform (Lin & DeCarlo: Ch 3) ENSC30 Elecric Circui II The Laplace ranform i an inegral ranformaion. I ranform: f ( ) F( ) ime variable complex variable From Euler > Lagrange > Laplace. Hence,

More information

Network Flow Applications

Network Flow Applications Hopial problem Neork Flo Applicaion Injured people: n Hopial: k Each peron need o be brough o a hopial no more han 30 minue aay Each hopial rea no more han n/k" people Gien n, k, and informaion abou people

More information

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.

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. PHY1 Elecriciy Topic 7 (Lecures 1 & 11) Elecric Circuis n his opic, we will cover: 1) Elecromoive Force (EMF) ) Series and parallel resisor combinaions 3) Kirchhoff s rules for circuis 4) Time dependence

More information

Logistic growth rate. Fencing a pen. Notes. Notes. Notes. Optimization: finding the biggest/smallest/highest/lowest, etc.

Logistic growth rate. Fencing a pen. Notes. Notes. Notes. Optimization: finding the biggest/smallest/highest/lowest, etc. Opimizaion: finding he bigges/smalles/highes/lowes, ec. Los of non-sandard problems! Logisic growh rae 7.1 Simple biological opimizaion problems Small populaions AND large populaions grow slowly N: densiy

More information

Stationary Distribution. Design and Analysis of Algorithms Andrei Bulatov

Stationary Distribution. Design and Analysis of Algorithms Andrei Bulatov Saionary Disribuion Design and Analysis of Algorihms Andrei Bulaov Algorihms Markov Chains 34-2 Classificaion of Saes k By P we denoe he (i,j)-enry of i, j Sae is accessible from sae if 0 for some k 0

More information

Stat13 Homework 7. Suggested Solutions

Stat13 Homework 7. Suggested Solutions Sa3 Homework 7 hp://www.a.ucla.edu/~dinov/coure_uden.hml Suggeed Soluion Queion 7.50 Le denoe infeced and denoe noninfeced. H 0 : Malaria doe no affec red cell coun (µ µ ) H A : Malaria reduce red cell

More information