Solutions to Assignment 5

Size: px
Start display at page:

Download "Solutions to Assignment 5"

Transcription

1 Solutions to ssignment 5. We have captured several people whom we suspect are part of a spy ring. They are identified as,,,, E, F, and G. fter interrogation, admits to having met the other six. admits to having met five, to having met four, to having met three, E to having met two, F to having met two, and G to having met one. None of them would identify whom they knew, and no spy would claim to have met more people than he has actually met. ssume that F is telling the truth and there is only one liar. Who is the lying spy, if it is known in addition that the number of acquaintances he gives is 3 less than the true value? Why? Solution: First recall the following from class: In the acquaintanceship graph, with vertices being the spies and with an edge between two vertices if and only if the corresponding spies have met, if all of the spies were telling the truth, there would be an even number of vertices with odd degree, but the degree sequence is deg() = 6, deg() = 5, deg() = 4, deg() = 3, deg(e) = 2, deg(f ) = 2, deg(g) =. Therefore, not all of the spies are telling the truth. We know that F is telling the truth and there is only one liar. ny spy who lies will claim to know less than he actually does, so must be telling the truth, since there are only 6 other spies. lso, is telling the truth, since if not, then has met 6 or more spies, which implies that G has met and, thus G would also be lying, but there is only one liar. If both E and G are telling the truth, then one of or is lying, and since deg(g) =, then has met all of,,, E, and F, and the situation (so far) is as shown in the diagram. If is lying, then deg() 5, and this leads to a contradiction. deg() 4, and this also leads to a contradiction. Therefore, the liar is either E or G. G Similarly, if is lying, then F E This much we did in class.

2 To solve the problem, we now consider now the two possible cases. ase. E is lying and G is telling the truth. In this case, G would have met only, and since E is lying, then E has met = 5 other spies, and since he cannot have met G, he must have met,,, and F. ut, who is telling the truth, has met 5 others, namely,,, E, and F, and has met all 6 others. Thus,, and E have all met F, but this would make F a liar, which is a contradiction. Thus, this case cannot occur. ase 2. E is telling the truth and G is lying. If G is lying, he must have met + 3 = 4 other spies, and the graph shows that this case is possible. G F Thus, the lying spy is G. E 2. substantial collection of jewels is in a chest in one of two underground labyrinths, and they are in a room with an odd number of doors. Each door connects two different rooms. The first labyrinth has two entrance doors and the other has three, and only one of these labyrinths has any rooms with an odd number of doors. ssume that the labyrinth with two entrance doors does not contain any room with an odd number of doors. Prove that it is possible to enter this labyrinth by one entrance door and exit by the other. Solution: We know from the hypotheses that the labyrinth with two entrance doors has only rooms with an even number of doors. Suppose it is not possible to enter through one entrance door and exit by the other. Remove all of the labyrinth except for the part accessible from one of the entrance doors. In other words, look at the component that contains one of the entrance doors. onsider the graph that represents this component, where the area outside the labyrinth is represented by one vertex and the rooms are represented by other vertices. oors are represented by edges between the vertices. The graph is connected (all rooms belong to the same component) and the outside vertex has degree exactly one. y the parity theorem, there must be an even number of vertices with odd degree, which means that at least one other door has odd degree. ut this vertex would then represent a room with an odd number of doors. However, this room is part of the original labyrinth. Thus, we have a contradiction. 3. There are seven code words, denoted by,,,, E, F, and G. ll code words have different frequencies: occurs on the average of 0 times out of 00;, 20 out of 00;, 9 out of 00;, 3 out of 00; E, 7 out of 00; F, 4 out of 00; and G, 9 out of 00. ll our messages are to be sent in dots and dashes, but unlike Morse code, which has pauses between letters, we want the code words to go out without pauses. Trained people can send dots accurately at the rate of two per second, including the silence before the next dot or dash. ashes are slower, however, achieving rates of only one per second. Suppose that we are not allowed to use dot dot as a code word. esign an unambiguous code such that an average message of 00 code words takes no more than 200 seconds. 2

3 Solution: Each code word is represented by a dash followed by 0 to 6 dots. frequencies have fewer dots, so the codes would be assigned as follows: Those with higher = = = = E = F = G = Since each dash signifies the beginning of a code word, there is no ambiguity. This code will take seconds to send the 00 code words = 9.5 Other more efficient solutions are possible. The record for the past 6 years is 85.5 seconds, and was attained by a student in this year s class. 4. tiger of the fiercest species has escaped from a zoo and is hiding in an abandoned temple. The zoo has keepers who are trained to catch tigers. The tiger may be in any room. The tiger may run from one room to another while they are looking for it, although it won t run into a room that has a keeper in it. The temple has no windows and only one entrance. Of its rooms, all but one connect to one other room or to three others. The last room connects to two other rooms. There are no doors of any kind between rooms. The rooms are dark and the tiger may find many hiding places, even though none of the rooms is very large. There is only one way to walk from any room to any other in the temple. We do not want to lay traps or put barriers between rooms. The temple is small enough that moving from room to room takes almost no time. oing a thorough search of a room takes a keeper 20 minutes. If he finds the tiger he can use his stun gun. Time is of the essence, because the tiger can scatch its way through various parts of the temple wall in under three hours. It is imperative that at no time should the tiger have a free path to the entrance. esign the layout of the temple with the properties above as stated by hief Inspector Singh, so that an escaped tiger inside the temple can be trapped by two keepers in 2 hours and 20 minutes. What is the maximum number of rooms? Solution: In 2 hours and 20 minutes, each keeper can search up to a maximum of 7 rooms, so the number of rooms cannot be greater than 4. onsider the rooms as being the vertices of a graph and the connections between the rooms as being the edges (we are ignoring the entrance door). One vertex has degree 2 exactly, all the others have degree 3 or. There must be an even number of vertices with odd degree, so there can be a maximum of 2 vertices with odd degree. Thus there can be no more than 3 rooms in the temple. The diagram below shows that 3 rooms are possible. The two keepers can search the rooms in alphabetical order, and can find the tiger in 2 hours and 20 minutes. E G I K F H J L M 5. uring wartime, the queen and her prime minister live in fortified rooms. The queen has 5 rooms and the PM has 5, for a total of 20 rooms. Some of the rooms are connected by passageways. No two rooms are connected by more than one passageway. For each two of the queen s rooms, there is a unique path of passageways from one to the other. The combined total number of passageways for the queen s rooms and the PM s rooms is 25. Prove that there is a passageway between one of the queen s rooms and one of the prime minister s rooms. 3

4 Solution: Note first that we can represent the problem using a graph, with the vertices representing the rooms and the edges representing the passageways, since there is at most one edge between any two vertices. Since for each pair of vertices in the queen s complex there is a unique path from one to the other, the queen lives in a tree. The queen s tree has 5 vertices and hence 4 edges. Since there are a total of 25 edges for the queen s complex and the PM s complex, then there are edges connecting the( vertices ) in the PM s complex. However, the PM s complex has 5 vertices, and so 5 must have at most = 0 edges. The extra edge must therefore connect one of the PM s rooms 2 with one of the queen s rooms. 6. Let G = (V, E) be a graph with vertex set V and edge set E, and let p = V be the number of vertices in G, and q = E the number of edges in G. The average degree of the vertices in G is defined to be (G) = deg(v). p v V If G is a connected graph, what can you say about G if (a) (G) > 2? (b) (G) = 2? (c) (G) < 2? raw a few pictures before committing yourself!!! Solution: First note that (G) = p v V deg(v) = 2q p (a) (G) > 2 if and only if q > p. Suppose that G is a connected graph and q > p, then G is not a tree and hence contains a cycle. Remove an edge from the cycle, then the resulting graph H is still connected, has p vertices and q edges. Now, p < q = (q ) +, so that H is not a tree, so it also has a cycle. Therefore, G has at least two cycles. onversely, suppose that G is a connected graph which has at least two cycles, then we may remove an edge from two of the cycles and the resulting graph H is still connected, so that p (q 2) + = q < q, and (G) > 2. Thus, if G is a connected graph, then (G) > 2 if and only if G has at least two cycles. (b) (G) = 2 if and only if q = p. Suppose that G is a connected graph and q = p, then G is not a tree and hence contains a cycle. Remove an edge from the cycle, then the resulting graph H is still connected, has p vertices and q edges and p = (q ) +, so that H is a tree. Therefore, G has exactly one cycle. onversely, suppose that G is a connected graph with exactly one cycle, if we remove an edge from the cycle, the resulting graph H is still connected and has no cycles, hence is a tree. Therefore, since H has p vertices and q edges, we have p = (q ) + = q. Thus, if G is a connected graph, then (G) = 2 if and only if G has exactly one cycle. 4

5 (c) (G) < 2 if and only if q < p. Suppose that G is a connected graph and q < p, then since p and q are integers, we must have q + p. We showed in class that any connected graph with p vertices and q edges has p q +, therefore p = q + and G is a tree. onversely, suppose that G is a tree, then G is connected and p = q +, so that (G) = 2q p = p (2p 2) = 2 2 p < 2. Thus, if G is a connected graph, then (G) < 2 if and only if G is a tree. 7. The Floridian parliament has to choose its next prime minister. There are five candidates, namely: xel, oris, laudia, ieter, Erica The house is split into 3 different voting blocks, each with 30 voters. The preferences are shown below. (,,, E, ) (, E,,, ) (,,, E, ) Here (U, V, W, X, Y ) means that a voting block prefers U to V to W to X to Y. The Floridian system uses a sequence of one-on-one elections with the winner of one election being a candidate in the next election. The sequence is set by the chief justice. (a) raw the tournament which is generated by these preferences charts. (b) The chief justice wants laudia to win. What sequence should he use. Solution: The tournament generated by the preference charts is shown below. E Since the tournament contains the directed Hamiltonian path E the chief justice can arrange for laudia to win the election by holding 4 one-on-one elections as follows: Election : Election 2: Election 3: Election 4: vs E winner vs winner vs winner vs 5

6 8. Let G be a graph whose vertices correspond to the bit-strings of length n, a = a a 2 a n where a i = 0 or, and whose edges are formed by joining those bit-strings which differ in exactly two places. (a) Show that G is regular, that is, every vertex has the same degree, and find the degree of each vertex. (b) Find a necessary and sufficient condition that there exist a path joining two vertices a = a a 2 a n and b = b b 2 b n in G. (c) Find the number of connected components of G. Solution: (a) Each vertex has degree ( n 2). (There is an edge between a a 2 a n and b b 2 b n if and only if then the Hamming distance between them is two, so the degree of a a 2 a n is the number of words at distance two from a a 2 a n, or equivalently, the number of words that differ in exactly two places which is ( n 2).) (b) necessary and sufficient condition is that a a 2 a n and b b 2 b n have the same parity (ie, that both have an even number of ones or both have an odd number of ones). The reason is as follows: If the Hamming distance between a a 2 a n and b b 2 b n is an even number, then we can transform a a 2 a n into b b 2 b n by changing two bits at a time, so there is a path from a a 2 a n to b b 2 b n. onversely, if there is a path from a a 2 a n and b b 2 b n, then we must be able to transform a a 2 a n into b b 2 b n by changing two bits at a time. ut changing two bits of a a 2 a n preserves the parity. (c) It follows from (b) that there are exactly two components, one containing all bit strings of length n with an even number of s, the other containing those with an odd number of s. 9. Prove that if a tournament is not strongly connected, then there exists an arc such that if its orientation is reversed, the resulting tournament would be strongly connected. You may use Redei s theorem or the amion-moon theorem in your proof. Solution: y Redei s theorem, any tournament T has a Hamiltonian directed path, that is, a directed path starting at a vertex a and ending at a vertex b, and passing through every vertex in the tournament exactly once. If the tournament is not strongly connected, then a b, and the arc goes from a to b (otherwise, we would have a Hamiltonian cycle and the tournament would be strongly connected). Therefore, if we reverse the direction or orientation of the arc (a, b), then we have a Hamiltonian cycle and the resulting tournament is strongly connected. 0. You are given the weighted graph shown. 8 5 E F H G 2 6

7 (a) Use ijkstra s algorithm to construct a shortest-path spanning tree from the vertex in the graph above. You may do it graphically or by using a table. (b) For the same weighted graph as in part (a), use Kruskal s algorithm to find a minimal spanning tree. You may do it graphically or by using a table. (c) For each of the spanning trees found above, compare the weights of the paths from vertex to vertex G. lso, compare the total weights of the two spanning trees. What can you conclude? Solution: (a) Using ijkstra s algorithm, starting at, we find the shortest-path spanning tree for the graph as follows: Step Tree So Far Fringe Vertices andidate Edge (istance).,, E (8), (0), E(5) 2., E,, F, H (8), (0), EF (6), EH(8) 3., E, F,, G, H F (7), (0), F G(), EH(8) 4., E, F,,, G, H (), (0), F G(), EH(8) 5., E, F,, H,, G (), H(9), F G() 6., E, F,, H,, G (0), F G() 7., E, F,, H,, G F G() 8., E, F,, H,,, G The shortest-path spanning tree from the vertex, as well as the table, is shown below. E F G, 0,,,,,, 5 E F,8,,0,5,, E E, 8,,0 E,6, E,8 EF F F,7,,0 F, E,8 F 3 5,2,0 F, E,8 EH H,2 H,9 F, H H G,0 F, F, FG G (b) Using Kruskal s algorithm, the minimal spanning tree for the graph, as well as the table, is shown below. E F G H E F H G 2 F EF H G EH GH E FG H, (c) s is easily seen from the spanning tree, using ijkstra a algorithm we have d(, G) = and Total Weight = 7, while using Kruskal s algorithm we have d(, G) = 2 and Total Weight = 4. 7

8 We conclude that the shortest-path spanning tree generated by ijkstra s algorithm does not have to be a minimal spanning tree, and that the minimal spanning tree generated by Kruskal s algorithm does not have to be a shortest-path spanning tree. 8

Math 222 (A1) Solutions to Assignment 4

Math 222 (A1) Solutions to Assignment 4 Math (1) Solutions to ssignment 1. We have captured several people whom we suspect are part of a spy ring. They are identified as,,,,,, and. fter interrogation, admits to having met the other six. admits

More information

γ(ɛ) (a, b) (a, d) (d, a) (a, b) (c, d) (d, d) (e, e) (e, a) (e, e) (a) Draw a picture of G.

γ(ɛ) (a, b) (a, d) (d, a) (a, b) (c, d) (d, d) (e, e) (e, a) (e, e) (a) Draw a picture of G. MAD 3105 Spring 2006 Solutions for Review for Test 2 1. Define a graph G with V (G) = {a, b, c, d, e}, E(G) = {r, s, t, u, v, w, x, y, z} and γ, the function defining the edges, is given by the table ɛ

More information

14 More Graphs: Euler Tours and Hamilton Cycles

14 More Graphs: Euler Tours and Hamilton Cycles 14 More Graphs: Euler Tours and Hamilton Cycles 14.1 Degrees The degree of a vertex is the number of edges coming out of it. The following is sometimes called the First Theorem of Graph Theory : Lemma

More information

Assignment 4 Solutions of graph problems

Assignment 4 Solutions of graph problems Assignment 4 Solutions of graph problems 1. Let us assume that G is not a cycle. Consider the maximal path in the graph. Let the end points of the path be denoted as v 1, v k respectively. If either of

More information

CHAPTER 2. Graphs. 1. Introduction to Graphs and Graph Isomorphism

CHAPTER 2. Graphs. 1. Introduction to Graphs and Graph Isomorphism CHAPTER 2 Graphs 1. Introduction to Graphs and Graph Isomorphism 1.1. The Graph Menagerie. Definition 1.1.1. A simple graph G = (V, E) consists of a set V of vertices and a set E of edges, represented

More information

Math 485, Graph Theory: Homework #3

Math 485, Graph Theory: Homework #3 Math 485, Graph Theory: Homework #3 Stephen G Simpson Due Monday, October 26, 2009 The assignment consists of Exercises 2129, 2135, 2137, 2218, 238, 2310, 2313, 2314, 2315 in the West textbook, plus the

More information

Chapter 3: Paths and Cycles

Chapter 3: Paths and Cycles Chapter 3: Paths and Cycles 5 Connectivity 1. Definitions: Walk: finite sequence of edges in which any two consecutive edges are adjacent or identical. (Initial vertex, Final vertex, length) Trail: walk

More information

Topic 10 Part 2 [474 marks]

Topic 10 Part 2 [474 marks] Topic Part 2 [474 marks] The complete graph H has the following cost adjacency matrix Consider the travelling salesman problem for H a By first finding a minimum spanning tree on the subgraph of H formed

More information

Adjacent: Two distinct vertices u, v are adjacent if there is an edge with ends u, v. In this case we let uv denote such an edge.

Adjacent: Two distinct vertices u, v are adjacent if there is an edge with ends u, v. In this case we let uv denote such an edge. 1 Graph Basics What is a graph? Graph: a graph G consists of a set of vertices, denoted V (G), a set of edges, denoted E(G), and a relation called incidence so that each edge is incident with either one

More information

1 Some Solution of Homework

1 Some Solution of Homework Math 3116 Dr. Franz Rothe May 30, 2012 08SUM\3116_2012h1.tex Name: Use the back pages for extra space 1 Some Solution of Homework Proposition 1 (Counting labeled trees). There are n n 2 different labeled

More information

a) Graph 2 and Graph 3 b) Graph 2 and Graph 4 c) Graph 1 and Graph 4 d) Graph 1 and Graph 3 e) Graph 3 and Graph 4 f) None of the above

a) Graph 2 and Graph 3 b) Graph 2 and Graph 4 c) Graph 1 and Graph 4 d) Graph 1 and Graph 3 e) Graph 3 and Graph 4 f) None of the above Mathematics 105: Math as a Liberal Art. Final Exam. Name Instructor: Ramin Naimi Spring 2008 Close book. Closed notes. No Calculators. NO CELL PHONES! Please turn off your cell phones and put them away.

More information

The angle measure at for example the vertex A is denoted by m A, or m BAC.

The angle measure at for example the vertex A is denoted by m A, or m BAC. MT 200 ourse notes on Geometry 5 2. Triangles and congruence of triangles 2.1. asic measurements. Three distinct lines, a, b and c, no two of which are parallel, form a triangle. That is, they divide the

More information

Fundamental Properties of Graphs

Fundamental Properties of Graphs Chapter three In many real-life situations we need to know how robust a graph that represents a certain network is, how edges or vertices can be removed without completely destroying the overall connectivity,

More information

Ma/CS 6b Class 5: Graph Connectivity

Ma/CS 6b Class 5: Graph Connectivity Ma/CS 6b Class 5: Graph Connectivity By Adam Sheffer Communications Network We are given a set of routers and wish to connect pairs of them to obtain a connected communications network. The network should

More information

Chapter 11: Graphs and Trees. March 23, 2008

Chapter 11: Graphs and Trees. March 23, 2008 Chapter 11: Graphs and Trees March 23, 2008 Outline 1 11.1 Graphs: An Introduction 2 11.2 Paths and Circuits 3 11.3 Matrix Representations of Graphs 4 11.5 Trees Graphs: Basic Definitions Informally, a

More information

Assignment # 4 Selected Solutions

Assignment # 4 Selected Solutions Assignment # 4 Selected Solutions Problem 2.3.3 Let G be a connected graph which is not a tree (did you notice this is redundant?) and let C be a cycle in G. Prove that the complement of any spanning tree

More information

1. Chapter 1, # 1: Prove that for all sets A, B, C, the formula

1. Chapter 1, # 1: Prove that for all sets A, B, C, the formula Homework 1 MTH 4590 Spring 2018 1. Chapter 1, # 1: Prove that for all sets,, C, the formula ( C) = ( ) ( C) is true. Proof : It suffices to show that ( C) ( ) ( C) and ( ) ( C) ( C). ssume that x ( C),

More information

Math236 Discrete Maths with Applications

Math236 Discrete Maths with Applications Math236 Discrete Maths with Applications P. Ittmann UKZN, Pietermaritzburg Semester 1, 2012 Ittmann (UKZN PMB) Math236 2012 1 / 19 Degree Sequences Let G be a graph with vertex set V (G) = {v 1, v 2, v

More information

Theorem 3.1 (Berge) A matching M in G is maximum if and only if there is no M- augmenting path.

Theorem 3.1 (Berge) A matching M in G is maximum if and only if there is no M- augmenting path. 3 Matchings Hall s Theorem Matching: A matching in G is a subset M E(G) so that no edge in M is a loop, and no two edges in M are incident with a common vertex. A matching M is maximal if there is no matching

More information

Definition For vertices u, v V (G), the distance from u to v, denoted d(u, v), in G is the length of a shortest u, v-path. 1

Definition For vertices u, v V (G), the distance from u to v, denoted d(u, v), in G is the length of a shortest u, v-path. 1 Graph fundamentals Bipartite graph characterization Lemma. If a graph contains an odd closed walk, then it contains an odd cycle. Proof strategy: Consider a shortest closed odd walk W. If W is not a cycle,

More information

Discrete mathematics

Discrete mathematics Discrete mathematics Petr Kovář petr.kovar@vsb.cz VŠB Technical University of Ostrava DiM 470-2301/02, Winter term 2018/2019 About this file This file is meant to be a guideline for the lecturer. Many

More information

Computer Algorithms-2 Prof. Dr. Shashank K. Mehta Department of Computer Science and Engineering Indian Institute of Technology, Kanpur

Computer Algorithms-2 Prof. Dr. Shashank K. Mehta Department of Computer Science and Engineering Indian Institute of Technology, Kanpur Computer Algorithms-2 Prof. Dr. Shashank K. Mehta Department of Computer Science and Engineering Indian Institute of Technology, Kanpur Lecture - 6 Minimum Spanning Tree Hello. Today, we will discuss an

More information

1. a graph G = (V (G), E(G)) consists of a set V (G) of vertices, and a set E(G) of edges (edges are pairs of elements of V (G))

1. a graph G = (V (G), E(G)) consists of a set V (G) of vertices, and a set E(G) of edges (edges are pairs of elements of V (G)) 10 Graphs 10.1 Graphs and Graph Models 1. a graph G = (V (G), E(G)) consists of a set V (G) of vertices, and a set E(G) of edges (edges are pairs of elements of V (G)) 2. an edge is present, say e = {u,

More information

Intermediate Math Circles Wednesday, February 8, 2017 Graph Theory I

Intermediate Math Circles Wednesday, February 8, 2017 Graph Theory I Intermediate Math Circles Wednesday, February 8, 2017 Graph Theory I Many of you are probably familiar with the term graph. To you a graph may mean a line or curve defined by a function y = f(x). It may

More information

A Reduction of Conway s Thrackle Conjecture

A Reduction of Conway s Thrackle Conjecture A Reduction of Conway s Thrackle Conjecture Wei Li, Karen Daniels, and Konstantin Rybnikov Department of Computer Science and Department of Mathematical Sciences University of Massachusetts, Lowell 01854

More information

11-5 Networks. Königsberg Bridge Problem

11-5 Networks. Königsberg Bridge Problem Section 11-5 Networks 1 11-5 Networks In the 1700s, the people of Königsberg, Germany (now Kaliningrad in Russia), used to enjoy walking over the bridges of the Pregel River. There were three landmasses

More information

CPS 102: Discrete Mathematics. Quiz 3 Date: Wednesday November 30, Instructor: Bruce Maggs NAME: Prob # Score. Total 60

CPS 102: Discrete Mathematics. Quiz 3 Date: Wednesday November 30, Instructor: Bruce Maggs NAME: Prob # Score. Total 60 CPS 102: Discrete Mathematics Instructor: Bruce Maggs Quiz 3 Date: Wednesday November 30, 2011 NAME: Prob # Score Max Score 1 10 2 10 3 10 4 10 5 10 6 10 Total 60 1 Problem 1 [10 points] Find a minimum-cost

More information

An Introduction to Graph Theory

An Introduction to Graph Theory An Introduction to Graph Theory Evelyne Smith-Roberge University of Waterloo March 22, 2017 What is a graph? Definition A graph G is: a set V (G) of objects called vertices together with: a set E(G), of

More information

Module 11. Directed Graphs. Contents

Module 11. Directed Graphs. Contents Module 11 Directed Graphs Contents 11.1 Basic concepts......................... 256 Underlying graph of a digraph................ 257 Out-degrees and in-degrees.................. 258 Isomorphism..........................

More information

Not Conway s 99-Graph Problem

Not Conway s 99-Graph Problem Not onway s 99-Graph Problem Sa ar Zehavi Ivo agundes avid de Oliveira September 15, 2017 1 bstract onway s 99-graph problem is the second problem amongst the five 1000$ 2017 open problems set [1]. our

More information

Ma/CS 6b Class 4: Matchings in General Graphs

Ma/CS 6b Class 4: Matchings in General Graphs Ma/CS 6b Class 4: Matchings in General Graphs By Adam Sheffer Reminder: Hall's Marriage Theorem Theorem. Let G = V 1 V 2, E be a bipartite graph. There exists a matching of size V 1 in G if and only if

More information

GRAPH DECOMPOSITION BASED ON DEGREE CONSTRAINTS. March 3, 2016

GRAPH DECOMPOSITION BASED ON DEGREE CONSTRAINTS. March 3, 2016 GRAPH DECOMPOSITION BASED ON DEGREE CONSTRAINTS ZOÉ HAMEL March 3, 2016 1. Introduction Let G = (V (G), E(G)) be a graph G (loops and multiple edges not allowed) on the set of vertices V (G) and the set

More information

Basic Graph Theory with Applications to Economics

Basic Graph Theory with Applications to Economics Basic Graph Theory with Applications to Economics Debasis Mishra February, 0 What is a Graph? Let N = {,..., n} be a finite set. Let E be a collection of ordered or unordered pairs of distinct elements

More information

The University of Sydney MATH 2009

The University of Sydney MATH 2009 The University of Sydney MTH 009 GRPH THORY Tutorial 10 Solutions 004 1. In a tournament, the score of a vertex is its out-degree, and the score sequence is a list of all the scores in non-decreasing order.

More information

MC 302 GRAPH THEORY 10/1/13 Solutions to HW #2 50 points + 6 XC points

MC 302 GRAPH THEORY 10/1/13 Solutions to HW #2 50 points + 6 XC points MC 0 GRAPH THEORY 0// Solutions to HW # 0 points + XC points ) [CH] p.,..7. This problem introduces an important class of graphs called the hypercubes or k-cubes, Q, Q, Q, etc. I suggest that before you

More information

Solution to Graded Problem Set 4

Solution to Graded Problem Set 4 Graph Theory Applications EPFL, Spring 2014 Solution to Graded Problem Set 4 Date: 13.03.2014 Due by 18:00 20.03.2014 Problem 1. Let V be the set of vertices, x be the number of leaves in the tree and

More information

We have already seen the transportation problem and the assignment problem. Let us take the transportation problem, first.

We have already seen the transportation problem and the assignment problem. Let us take the transportation problem, first. Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture 19 Network Models In this lecture, we will discuss network models. (Refer

More information

PROBLEM SET 1 SOLUTIONS MAS341: GRAPH THEORY 1. QUESTION 1

PROBLEM SET 1 SOLUTIONS MAS341: GRAPH THEORY 1. QUESTION 1 PROBLEM SET 1 SOLUTIONS MAS341: GRAPH THEORY 1. QUESTION 1 Find a Hamiltonian cycle in the following graph: Proof. Can be done by trial an error. Here we find the path using some helpful observations.

More information

EDGE MAXIMAL GRAPHS CONTAINING NO SPECIFIC WHEELS. Jordan Journal of Mathematics and Statistics (JJMS) 8(2), 2015, pp I.

EDGE MAXIMAL GRAPHS CONTAINING NO SPECIFIC WHEELS. Jordan Journal of Mathematics and Statistics (JJMS) 8(2), 2015, pp I. EDGE MAXIMAL GRAPHS CONTAINING NO SPECIFIC WHEELS M.S.A. BATAINEH (1), M.M.M. JARADAT (2) AND A.M.M. JARADAT (3) A. Let k 4 be a positive integer. Let G(n; W k ) denote the class of graphs on n vertices

More information

Answers to specimen paper questions. Most of the answers below go into rather more detail than is really needed. Please let me know of any mistakes.

Answers to specimen paper questions. Most of the answers below go into rather more detail than is really needed. Please let me know of any mistakes. Answers to specimen paper questions Most of the answers below go into rather more detail than is really needed. Please let me know of any mistakes. Question 1. (a) The degree of a vertex x is the number

More information

HANDSHAKING AND CHASING KIDS

HANDSHAKING AND CHASING KIDS HANDSHAKING AND CHASING KIDS BEGINNER CIRCLE 11/4/2012 1. KIDS RUNNING AROUND MATH SCIENCE At the end of every day, Isaac, Derek, Jonathan, Jeff and Morgan have to check the hallways of Math Science to

More information

Graph Theory Part Two

Graph Theory Part Two Graph Theory Part Two Recap from Last Time A graph is a mathematical structure for representing relationships. Nodes A graph consists of a set of nodes (or vertices) connected by edges (or arcs) A graph

More information

Basic Combinatorics. Math 40210, Section 01 Fall Homework 4 Solutions

Basic Combinatorics. Math 40210, Section 01 Fall Homework 4 Solutions Basic Combinatorics Math 40210, Section 01 Fall 2012 Homework 4 Solutions 1.4.2 2: One possible implementation: Start with abcgfjiea From edge cd build, using previously unmarked edges: cdhlponminjkghc

More information

Math 100 Homework 4 B A C E

Math 100 Homework 4 B A C E Math 100 Homework 4 Part 1 1. nswer the following questions for this graph. (a) Write the vertex set. (b) Write the edge set. (c) Is this graph connected? (d) List the degree of each vertex. (e) oes the

More information

Eulerian Cycle (2A) Young Won Lim 5/25/18

Eulerian Cycle (2A) Young Won Lim 5/25/18 ulerian ycle (2) opyright (c) 2015 2018 Young W. Lim. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU ree ocumentation License, Version 1.2 or any later

More information

Characterization of Graphs with Eulerian Circuits

Characterization of Graphs with Eulerian Circuits Eulerian Circuits 3. 73 Characterization of Graphs with Eulerian Circuits There is a simple way to determine if a graph has an Eulerian circuit. Theorems 3.. and 3..2: Let G be a pseudograph that is connected

More information

5 Matchings in Bipartite Graphs and Their Applications

5 Matchings in Bipartite Graphs and Their Applications 5 Matchings in Bipartite Graphs and Their Applications 5.1 Matchings Definition 5.1 A matching M in a graph G is a set of edges of G, none of which is a loop, such that no two edges in M have a common

More information

Graph Theory. 1 Introduction to Graphs. Martin Stynes Department of Mathematics, UCC January 26, 2011

Graph Theory. 1 Introduction to Graphs. Martin Stynes Department of Mathematics, UCC   January 26, 2011 Graph Theory Martin Stynes Department of Mathematics, UCC email: m.stynes@ucc.ie January 26, 2011 1 Introduction to Graphs 1 A graph G = (V, E) is a non-empty set of nodes or vertices V and a (possibly

More information

HW Graph Theory SOLUTIONS (hbovik) - Q

HW Graph Theory SOLUTIONS (hbovik) - Q 1, Diestel 9.3: An arithmetic progression is an increasing sequence of numbers of the form a, a+d, a+ d, a + 3d.... Van der Waerden s theorem says that no matter how we partition the natural numbers into

More information

2.2 Set Operations. Introduction DEFINITION 1. EXAMPLE 1 The union of the sets {1, 3, 5} and {1, 2, 3} is the set {1, 2, 3, 5}; that is, EXAMPLE 2

2.2 Set Operations. Introduction DEFINITION 1. EXAMPLE 1 The union of the sets {1, 3, 5} and {1, 2, 3} is the set {1, 2, 3, 5}; that is, EXAMPLE 2 2.2 Set Operations 127 2.2 Set Operations Introduction Two, or more, sets can be combined in many different ways. For instance, starting with the set of mathematics majors at your school and the set of

More information

Instructor: Paul Zeitz, University of San Francisco

Instructor: Paul Zeitz, University of San Francisco Berkeley Math Circle Graph Theory and Ramsey Theory Instructor: Paul Zeitz, University of San Francisco (zeitz@usfca.edu) Definitions 1 A graph is a pair (V,E), where V is a finite set and E is a set of

More information

Steiner Trees and Forests

Steiner Trees and Forests Massachusetts Institute of Technology Lecturer: Adriana Lopez 18.434: Seminar in Theoretical Computer Science March 7, 2006 Steiner Trees and Forests 1 Steiner Tree Problem Given an undirected graph G

More information

Definition: A graph G = (V, E) is called a tree if G is connected and acyclic. The following theorem captures many important facts about trees.

Definition: A graph G = (V, E) is called a tree if G is connected and acyclic. The following theorem captures many important facts about trees. Tree 1. Trees and their Properties. Spanning trees 3. Minimum Spanning Trees 4. Applications of Minimum Spanning Trees 5. Minimum Spanning Tree Algorithms 1.1 Properties of Trees: Definition: A graph G

More information

6.2. Paths and Cycles

6.2. Paths and Cycles 6.2. PATHS AND CYCLES 85 6.2. Paths and Cycles 6.2.1. Paths. A path from v 0 to v n of length n is a sequence of n+1 vertices (v k ) and n edges (e k ) of the form v 0, e 1, v 1, e 2, v 2,..., e n, v n,

More information

IMO Training 2008: Graph Theory

IMO Training 2008: Graph Theory IMO Training 2008: Graph Theory by: Adrian Tang Email: tang @ math.ucalgary.ca This is a compilation of math problems (with motivation towards the training for the International Mathematical Olympiad)

More information

CSE 21 Spring 2016 Homework 5. Instructions

CSE 21 Spring 2016 Homework 5. Instructions CSE 21 Spring 2016 Homework 5 Instructions Homework should be done in groups of one to three people. You are free to change group members at any time throughout the quarter. Problems should be solved together,

More information

Logic: The Big Picture. Axiomatizing Arithmetic. Tautologies and Valid Arguments. Graphs and Trees

Logic: The Big Picture. Axiomatizing Arithmetic. Tautologies and Valid Arguments. Graphs and Trees Axiomatizing Arithmetic Logic: The Big Picture Suppose we restrict the domain to the natural numbers, and allow only the standard symbols of arithmetic (+,, =, >, 0, 1). Typical true formulas include:

More information

Kuratowski Notes , Fall 2005, Prof. Peter Shor Revised Fall 2007

Kuratowski Notes , Fall 2005, Prof. Peter Shor Revised Fall 2007 Kuratowski Notes 8.30, Fall 005, Prof. Peter Shor Revised Fall 007 Unfortunately, the OCW notes on Kuratowski s theorem seem to have several things substantially wrong with the proof, and the notes from

More information

Section 3.1: Nonseparable Graphs Cut vertex of a connected graph G: A vertex x G such that G x is not connected. Theorem 3.1, p. 57: Every connected

Section 3.1: Nonseparable Graphs Cut vertex of a connected graph G: A vertex x G such that G x is not connected. Theorem 3.1, p. 57: Every connected Section 3.1: Nonseparable Graphs Cut vertex of a connected graph G: A vertex x G such that G x is not connected. Theorem 3.1, p. 57: Every connected graph G with at least 2 vertices contains at least 2

More information

CLAW-FREE 3-CONNECTED P 11 -FREE GRAPHS ARE HAMILTONIAN

CLAW-FREE 3-CONNECTED P 11 -FREE GRAPHS ARE HAMILTONIAN CLAW-FREE 3-CONNECTED P 11 -FREE GRAPHS ARE HAMILTONIAN TOMASZ LUCZAK AND FLORIAN PFENDER Abstract. We show that every 3-connected claw-free graph which contains no induced copy of P 11 is hamiltonian.

More information

MAS 341: GRAPH THEORY 2016 EXAM SOLUTIONS

MAS 341: GRAPH THEORY 2016 EXAM SOLUTIONS MS 41: PH THEOY 2016 EXM SOLUTIONS 1. Question 1 1.1. Explain why any alkane C n H 2n+2 is a tree. How many isomers does C 6 H 14 have? Draw the structure of the carbon atoms in each isomer. marks; marks

More information

5.1 Min-Max Theorem for General Matching

5.1 Min-Max Theorem for General Matching CSC5160: Combinatorial Optimization and Approximation Algorithms Topic: General Matching Date: 4/01/008 Lecturer: Lap Chi Lau Scribe: Jennifer X.M. WU In this lecture, we discuss matchings in general graph.

More information

The Game of Criss-Cross

The Game of Criss-Cross Chapter 5 The Game of Criss-Cross Euler Characteristic ( ) Overview. The regions on a map and the faces of a cube both illustrate a very natural sort of situation: they are each examples of regions that

More information

Graphs and networks Mixed exercise

Graphs and networks Mixed exercise Graphs and networks Mixed exercise E.g. 2 a, e and h are isomorphic. b and i are isomorphic. c and g are isomorphic. d and f are isomorphic. 3 a b i ii iii Pearson Education Ltd 208. Copying permitted

More information

Characterizing Graphs (3) Characterizing Graphs (1) Characterizing Graphs (2) Characterizing Graphs (4)

Characterizing Graphs (3) Characterizing Graphs (1) Characterizing Graphs (2) Characterizing Graphs (4) S-72.2420/T-79.5203 Basic Concepts 1 S-72.2420/T-79.5203 Basic Concepts 3 Characterizing Graphs (1) Characterizing Graphs (3) Characterizing a class G by a condition P means proving the equivalence G G

More information

Math Summer 2012

Math Summer 2012 Math 481 - Summer 2012 Final Exam You have one hour and fifty minutes to complete this exam. You are not allowed to use any electronic device. Be sure to give reasonable justification to all your answers.

More information

PACKING DIGRAPHS WITH DIRECTED CLOSED TRAILS

PACKING DIGRAPHS WITH DIRECTED CLOSED TRAILS PACKING DIGRAPHS WITH DIRECTED CLOSED TRAILS PAUL BALISTER Abstract It has been shown [Balister, 2001] that if n is odd and m 1,, m t are integers with m i 3 and t i=1 m i = E(K n) then K n can be decomposed

More information

Graphs and trees come up everywhere. We can view the internet as a graph (in many ways) Web search views web pages as a graph

Graphs and trees come up everywhere. We can view the internet as a graph (in many ways) Web search views web pages as a graph Graphs and Trees Graphs and trees come up everywhere. We can view the internet as a graph (in many ways) who is connected to whom Web search views web pages as a graph Who points to whom Niche graphs (Ecology):

More information

( ) A calculator may be used on the exam. The formulas below will be provided in the examination booklet.

( ) A calculator may be used on the exam. The formulas below will be provided in the examination booklet. The Geometry and Honors Geometry Semester examination will have the following types of questions: Selected Response Student Produced Response (Grid-in) Short nswer calculator may be used on the exam. The

More information

The University of Sydney MATH2969/2069. Graph Theory Tutorial 2 (Week 9) 2008

The University of Sydney MATH2969/2069. Graph Theory Tutorial 2 (Week 9) 2008 The University of Sydney MATH99/09 Graph Theory Tutorial (Week 9) 00. Show that the graph on the left is Hamiltonian, but that the other two are not. To show that the graph is Hamiltonian, simply find

More information

CS261: A Second Course in Algorithms Lecture #16: The Traveling Salesman Problem

CS261: A Second Course in Algorithms Lecture #16: The Traveling Salesman Problem CS61: A Second Course in Algorithms Lecture #16: The Traveling Salesman Problem Tim Roughgarden February 5, 016 1 The Traveling Salesman Problem (TSP) In this lecture we study a famous computational problem,

More information

Hamiltonian cycles in bipartite quadrangulations on the torus

Hamiltonian cycles in bipartite quadrangulations on the torus Hamiltonian cycles in bipartite quadrangulations on the torus Atsuhiro Nakamoto and Kenta Ozeki Abstract In this paper, we shall prove that every bipartite quadrangulation G on the torus admits a simple

More information

Math 443/543 Graph Theory Notes 2: Transportation problems

Math 443/543 Graph Theory Notes 2: Transportation problems Math 443/543 Graph Theory Notes 2: Transportation problems David Glickenstein September 15, 2014 1 Readings This is based on Chartrand Chapter 3 and Bondy-Murty 18.1, 18.3 (part on Closure of a Graph).

More information

Math 776 Graph Theory Lecture Note 1 Basic concepts

Math 776 Graph Theory Lecture Note 1 Basic concepts Math 776 Graph Theory Lecture Note 1 Basic concepts Lectured by Lincoln Lu Transcribed by Lincoln Lu Graph theory was founded by the great Swiss mathematician Leonhard Euler (1707-178) after he solved

More information

2. CONNECTIVITY Connectivity

2. CONNECTIVITY Connectivity 2. CONNECTIVITY 70 2. Connectivity 2.1. Connectivity. Definition 2.1.1. (1) A path in a graph G = (V, E) is a sequence of vertices v 0, v 1, v 2,..., v n such that {v i 1, v i } is an edge of G for i =

More information

Chapter 4. square sum graphs. 4.1 Introduction

Chapter 4. square sum graphs. 4.1 Introduction Chapter 4 square sum graphs In this Chapter we introduce a new type of labeling of graphs which is closely related to the Diophantine Equation x 2 + y 2 = n and report results of our preliminary investigations

More information

Degree of nonsimple graphs. Chemistry questions. Degree Sequences. Pigeon party.

Degree of nonsimple graphs. Chemistry questions. Degree Sequences. Pigeon party. 1. WEEK 1 PROBLEMS 1.1. Degree of nonsimple graphs. In the lecture notes we defined the degree d(v) of a vertex v to be the number of vertices adjacent to v. To see why Euler s theorem doesn t hold for

More information

PAIRED-DOMINATION. S. Fitzpatrick. Dalhousie University, Halifax, Canada, B3H 3J5. and B. Hartnell. Saint Mary s University, Halifax, Canada, B3H 3C3

PAIRED-DOMINATION. S. Fitzpatrick. Dalhousie University, Halifax, Canada, B3H 3J5. and B. Hartnell. Saint Mary s University, Halifax, Canada, B3H 3C3 Discussiones Mathematicae Graph Theory 18 (1998 ) 63 72 PAIRED-DOMINATION S. Fitzpatrick Dalhousie University, Halifax, Canada, B3H 3J5 and B. Hartnell Saint Mary s University, Halifax, Canada, B3H 3C3

More information

These are not polished as solutions, but ought to give a correct idea of solutions that work. Note that most problems have multiple good solutions.

These are not polished as solutions, but ought to give a correct idea of solutions that work. Note that most problems have multiple good solutions. CSE 591 HW Sketch Sample Solutions These are not polished as solutions, but ought to give a correct idea of solutions that work. Note that most problems have multiple good solutions. Problem 1 (a) Any

More information

3 and 4-Bandwidth Critical Graphs

3 and 4-Bandwidth Critical Graphs and -Bandwidth Critical Graphs nn Kilzer ugust, 00 bstract This paper investigates and -bandwidth critical graphs. It concludes Holly Westerfield s proof that only six types of -bandwidth critical graphs

More information

Topics in Combinatorial Optimization February 5, Lecture 2

Topics in Combinatorial Optimization February 5, Lecture 2 8.997 Topics in Combinatorial Optimization February 5, 2004 Lecture 2 Lecturer: Michel X. Goemans Scribe: Robert Kleinberg In this lecture, we will: Present Edmonds algorithm for computing a maximum matching

More information

USA Mathematical Talent Search Round 2 Solutions Year 23 Academic Year

USA Mathematical Talent Search Round 2 Solutions Year 23 Academic Year 1//3. Find all the ways of placing the integers 1,, 3,..., 16 in the boxes below, such that each integer appears in exactly one box, and the sum of every pair of neighboring integers is a perfect square.

More information

WUCT121. Discrete Mathematics. Graphs

WUCT121. Discrete Mathematics. Graphs WUCT121 Discrete Mathematics Graphs WUCT121 Graphs 1 Section 1. Graphs 1.1. Introduction Graphs are used in many fields that require analysis of routes between locations. These areas include communications,

More information

Graph Theory. Part of Texas Counties.

Graph Theory. Part of Texas Counties. Graph Theory Part of Texas Counties. We would like to visit each of the above counties, crossing each county only once, starting from Harris county. Is this possible? This problem can be modeled as a graph.

More information

MATH20902: Discrete Maths, Solutions to Problem Set 1. These solutions, as well as the corresponding problems, are available at

MATH20902: Discrete Maths, Solutions to Problem Set 1. These solutions, as well as the corresponding problems, are available at MATH20902: Discrete Maths, Solutions to Problem Set 1 These solutions, as well as the corresponding problems, are available at https://bit.ly/mancmathsdiscrete.. (1). The upper panel in the figure below

More information

The University of Sydney MATH 2009

The University of Sydney MATH 2009 The University of Sydney MTH 009 GRPH THEORY Tutorial solutions 00. Show that the graph on the left is Hamiltonian, but that the other two are not. To show that the graph is Hamiltonian, simply find a

More information

Extremal Graph Theory: Turán s Theorem

Extremal Graph Theory: Turán s Theorem Bridgewater State University Virtual Commons - Bridgewater State University Honors Program Theses and Projects Undergraduate Honors Program 5-9-07 Extremal Graph Theory: Turán s Theorem Vincent Vascimini

More information

Lecture 5: Graphs. Rajat Mittal. IIT Kanpur

Lecture 5: Graphs. Rajat Mittal. IIT Kanpur Lecture : Graphs Rajat Mittal IIT Kanpur Combinatorial graphs provide a natural way to model connections between different objects. They are very useful in depicting communication networks, social networks

More information

Computer Science 280 Fall 2002 Homework 10 Solutions

Computer Science 280 Fall 2002 Homework 10 Solutions Computer Science 280 Fall 2002 Homework 10 Solutions Part A 1. How many nonisomorphic subgraphs does W 4 have? W 4 is the wheel graph obtained by adding a central vertex and 4 additional "spoke" edges

More information

11.4 Bipartite Multigraphs

11.4 Bipartite Multigraphs 11.4 Bipartite Multigraphs Introduction Definition A graph G is bipartite if we can partition the vertices into two disjoint subsets U and V such that every edge of G has one incident vertex in U and the

More information

Solution : a) C(18, 1)C(325, 1) = 5850 b) C(18, 1) + C(325, 1) = 343

Solution : a) C(18, 1)C(325, 1) = 5850 b) C(18, 1) + C(325, 1) = 343 DISCRETE MATHEMATICS HOMEWORK 5 SOL Undergraduate Course College of Computer Science Zhejiang University Fall-Winter 2014 HOMEWORK 5 P344 1. There are 18 mathematics majors and 325 computer science majors

More information

Graph Theory Mini-course

Graph Theory Mini-course Graph Theory Mini-course Anthony Varilly PROMYS, Boston University, Boston, MA 02215 Abstract Intuitively speaking, a graph is a collection of dots and lines joining some of these dots. Many problems in

More information

2 hours THE UNIVERSITY OF MANCHESTER. 22 May :00 16:00

2 hours THE UNIVERSITY OF MANCHESTER. 22 May :00 16:00 2 hours THE UNIVERSITY OF MANCHESTER DISCRETE MATHEMATICS 22 May 2015 14:00 16:00 Answer ALL THREE questions in Section A (30 marks in total) and TWO of the THREE questions in Section B (50 marks in total).

More information

THE PRINCIPLE OF INDUCTION. MARK FLANAGAN School of Electrical, Electronic and Communications Engineering University College Dublin

THE PRINCIPLE OF INDUCTION. MARK FLANAGAN School of Electrical, Electronic and Communications Engineering University College Dublin THE PRINCIPLE OF INDUCTION MARK FLANAGAN School of Electrical, Electronic and Communications Engineering University College Dublin The Principle of Induction: Let a be an integer, and let P(n) be a statement

More information

2012 Pascal Contest (Grade 9)

2012 Pascal Contest (Grade 9) The ENTRE for EUTION in MTHEMTIS and OMPUTING www.cemc.uwaterloo.ca 01 Pascal ontest (Grade 9) Thursday, February 3, 01 (in North merica and South merica) Friday, February 4, 01 (outside of North merica

More information

Modules. 6 Hamilton Graphs (4-8 lectures) Introduction Necessary conditions and sufficient conditions Exercises...

Modules. 6 Hamilton Graphs (4-8 lectures) Introduction Necessary conditions and sufficient conditions Exercises... Modules 6 Hamilton Graphs (4-8 lectures) 135 6.1 Introduction................................ 136 6.2 Necessary conditions and sufficient conditions............. 137 Exercises..................................

More information

Advanced Combinatorial Optimization September 17, Lecture 3. Sketch some results regarding ear-decompositions and factor-critical graphs.

Advanced Combinatorial Optimization September 17, Lecture 3. Sketch some results regarding ear-decompositions and factor-critical graphs. 18.438 Advanced Combinatorial Optimization September 17, 2009 Lecturer: Michel X. Goemans Lecture 3 Scribe: Aleksander Madry ( Based on notes by Robert Kleinberg and Dan Stratila.) In this lecture, we

More information

Final Exam May 6, 2015

Final Exam May 6, 2015 CSC 1300 - Discrete Structures Final Exam May 6, 2015 Name: Question Value Score 1 15 2 25 3 20 4 15 5 20 6 20 7 10 8 10 9 10 10 20 11 15 12 20 TOTAL 200 No calculators or any reference except your one

More information

Notebook Assignments

Notebook Assignments Notebook Assignments These six assignments are a notebook using techniques from class in the single concrete context of graph theory. This is supplemental to your usual assignments, and is designed for

More information

Graph Coloring. Margaret M. Fleck. 3 May This lecture discusses the graph coloring problem (section 9.8 of Rosen).

Graph Coloring. Margaret M. Fleck. 3 May This lecture discusses the graph coloring problem (section 9.8 of Rosen). Graph Coloring Margaret M. Fleck 3 May 2010 This lecture discusses the graph coloring problem (section 9.8 of Rosen). 1 Announcements Makeup quiz last day of classes (at the start of class). Your room

More information