 5.3.2 Hamilton paths.  Definition 20: A Hamilton paths is a path that contains each vertex exactly once. A Hamilton circuit is a circuit that contains.

Slides:



Advertisements
Similar presentations
 Theorem 5.9: Let G be a simple graph with n vertices, where n>2. G has a Hamilton circuit if for any two vertices u and v of G that are not adjacent,
Advertisements

Chapter 8 Topics in Graph Theory
Lecture 5 Graph Theory. Graphs Graphs are the most useful model with computer science such as logical design, formal languages, communication network,
Walks, Paths and Circuits Walks, Paths and Circuits Sanjay Jain, Lecturer, School of Computing.
Graph-02.
 期中测验时间:本周五上午 9 : 40  教师 TA 答疑时间 : 周三晚上 6 : 00—8 : 30  地点:软件楼 315 房间,  教师 TA :李弋老师  开卷考试.
1 Slides based on those of Kenneth H. Rosen Slides by Sylvia Sorkin, Community College of Baltimore County - Essex Campus Graphs.
Shortest-paths. p2. Shortest-paths problems : G=(V,E) : weighted, directed graph w : E  R : weight function P=
Graph Traversals Visit vertices of a graph G to determine some property: Is G connected? Is there a path from vertex a to vertex b? Does G have a cycle?
Midwestern State University Department of Computer Science Dr. Ranette Halverson CMPS 2433 CHAPTER 4 - PART 2 GRAPHS 1.
CMSC 341 Graphs 2. 2 Weighted Shortest Path Problem Single-source shortest-path problem: Given as input a weighted graph, G = (V,E), and a distinguished.
 Graph Graph  Types of Graphs Types of Graphs  Data Structures to Store Graphs Data Structures to Store Graphs  Graph Definitions Graph Definitions.
1 Representing Graphs. 2 Adjacency Matrix Suppose we have a graph G with n nodes. The adjacency matrix is the n x n matrix A=[a ij ] with: a ij = 1 if.
Vertex Cut Vertex Cut: A separating set or vertex cut of a graph G is a set S  V(G) such that G-S has more than one component. a b c d e f g h i.
Applied Discrete Mathematics Week 12: Trees
What is the next line of the proof? a). Let G be a graph with k vertices. b). Assume the theorem holds for all graphs with k+1 vertices. c). Let G be a.
Vertex Cut Vertex Cut: A separating set or vertex cut of a graph G is a set SV(G) such that S has more than one component. Connectivity of G ((G)): The.
Is the following graph Hamiltonian- connected from vertex v? a). Yes b). No c). I have absolutely no idea v.
CTIS 154 Discrete Mathematics II1 8.2 Paths and Cycles Kadir A. Peker.
K-Coloring k-coloring: A k-coloring of a graph G is a labeling f: V(G)  S, where |S|=k. The labels are colors; the vertices of one color form a color.
Vertex Cut Vertex Cut: A separating set or vertex cut of a graph G is a set SV(G) such that G-S has more than one component. d f b e a g c i h.
K-Coloring k-coloring: A k-coloring of a graph G is a labeling f: V(G)  S, where |S|=k. The labels are colors; the vertices of one color form a color.
TECH Computer Science Graph Optimization Problems and Greedy Algorithms Greedy Algorithms  // Make the best choice now! Optimization Problems  Minimizing.
GRAPH Learning Outcomes Students should be able to:
Graphs CS /02/05 Graphs Slide 2 Copyright 2005, by the authors of these slides, and Ateneo de Manila University. All rights reserved Definition.
5.4 Shortest-path problem  Let G=(V,E,w) be a weighted connected simple graph, w is a function from edges set E to position real numbers set. We denoted.
Graph Theory Topics to be covered:
Trees and Distance. 2.1 Basic properties Acyclic : a graph with no cycle Forest : acyclic graph Tree : connected acyclic graph Leaf : a vertex of degree.
Fall 2015 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University 1.
7.1 and 7.2: Spanning Trees. A network is a graph that is connected –The network must be a sub-graph of the original graph (its edges must come from the.
CSNB143 – Discrete Structure Topic 9 – Graph. Learning Outcomes Student should be able to identify graphs and its components. Students should know how.
1 CS104 : Discrete Structures Chapter V Graph Theory.
The Tutte Polynomial Graph Polynomials winter 05/06.
 Rooted tree and binary tree  Theorem 5.19: A full binary tree with t leaves contains i=t-1 internal vertices.
5.5.2 M inimum spanning trees  Definition 24: A minimum spanning tree in a connected weighted graph is a spanning tree that has the smallest possible.
5.5.3 Rooted tree and binary tree  Definition 25: A directed graph is a directed tree if the graph is a tree in the underlying undirected graph.  Definition.
Graphs.  Definition A simple graph G= (V, E) consists of vertices, V, a nonempty set of vertices, and E, a set of unordered pairs of distinct elements.
Fall 2015 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University 1.
5.5.2 M inimum spanning trees  Definition 24: A minimum spanning tree in a connected weighted graph is a spanning tree that has the smallest possible.
Chapter 5 Graphs  the puzzle of the seven bridge in the Königsberg,  on the Pregel.
5.8 Graph Matching  Example: Set of worker assign to a set of task  Four tasks are to be assigned to four workers.  – Worker 1 is qualified to do tasks.
 周二下午 1 : 30—4 : 15 在软件楼 4 楼密码与信 息安全实验室答疑  周三下午 1 : 15 到 3 : 15 期中测验.
Chap. 11 Graph Theory and Applications 1. Directed Graph 2.
MAT 2720 Discrete Mathematics Section 8.2 Paths and Cycles
 Quotient graph  Definition 13: Suppose G(V,E) is a graph and R is a equivalence relation on the set V. We construct the quotient graph G R in the follow.
Basic properties Continuation
Chapter 11 - Graph CSNB 143 Discrete Mathematical Structures.
Chapter 20: Graphs. Objectives In this chapter, you will: – Learn about graphs – Become familiar with the basic terminology of graph theory – Discover.
1) Find and label the degree of each vertex in the graph.
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
COMPSCI 102 Introduction to Discrete Mathematics.
Spanning Trees Dijkstra (Unit 10) SOL: DM.2 Classwork worksheet Homework (day 70) Worksheet Quiz next block.
Chap 7 Graph Def 1: Simple graph G=(V,E) V : nonempty set of vertices E : set of unordered pairs of distinct elements of V called edges Def 2: Multigraph.
1. 期中测验时间和地点: 11 月 4 日, 上午 9:40—11 : 40 地点: 教室 2. 答疑时间和地点: 1)11 月 1 日 ( 周五 )13:00—15:00 软件楼 319 2)11 月 2 日和 3 日, 14:00—17:00 软件楼 3 楼 机房讨论室.
1 GRAPH Learning Outcomes Students should be able to: Explain basic terminology of a graph Identify Euler and Hamiltonian cycle Represent graphs using.
5.6 Prefix codes and optimal tree Definition 31: Codes with this property which the bit string for a letter never occurs as the first part of the bit string.
Trees.
Graph theory Definitions Trees, cycles, directed graphs.
Advanced Algorithms Analysis and Design
Connected Components Minimum Spanning Tree
G-v, or G-{v} When we remove a vertex v from a graph, we must remove all edges incident with the vertex v. When a edge is removed from a graph, without.
Connectivity Section 10.4.
Theorem 5.13: For vT‘, l’(v)= min{l(v), l(vk)+w(vk, v)}
Discrete Mathematics Lecture 13_14: Graph Theory and Tree
Euler and Hamilton Paths
N(S) ={vV|uS,{u,v}E(G)}
5.3.2 Hamilton paths.
Definition 8: Graphs that have a number assigned to each edge or each vertex are called weighted graphs weighted digraphs.
Graph Theory: Cuts and Connectivity
GRAPHS.
Presentation transcript:

 Hamilton paths

 Definition 20: A Hamilton paths is a path that contains each vertex exactly once. A Hamilton circuit is a circuit that contains each vertex exactly once except for the first vertex, which is also the last.

 Theorem 5.8: Suppose G(V,E) that has a Hamilton circuit, then for each nonempty proper subset S of V(G), the result which  (G- S)≤|S| holds, where G-S is the subgraph of G by omitting all vertices of S from V(G).  (G-S)=1 , |S|=2 The graph G has not any Hamilton circuit, if there is a nonempty purely subgraph S of G so that  (G-S)>|S|.

 Omit {b,h,i} from V,   (G-S)=4>3=|S| , The graph has not any Hamilton circuit

 If  (G-S)≤|S| for each nonempty proper subset S of G, then G has a Hamilton circuit or has not any Hamilton circuit.  For example: Petersen graph

 Proof: Let C be a Hamilton circuit of G(V,E). Then  (C-S)≤|S| for each nonempty proper subset S of V  Why?  Let us apply induction on the number of elements of S.  |S|=1,  The result holds  Suppose that result holds for |S|=k.  Let |S|=k+1  Let S=S' ∪ {v} , then |S'|=k  By the inductive hypothesis,  (C-S')≤|S'|  V(C-S)=V(G-S)  Thus C-S is a spanning subgraph of G-S  Therefore  (G-S)≤  (C-S)≤|S|

 Theorem 5.9: Let G be a simple graph with n vertices, where n>2. G has a Hamilton circuit if for any two vertices u and v of G that are not adjacent, d(u)+d(v)≥n. n=8,d(u)=d(v)=3, u and v are not adjacent, d(u)+d(v)=6<8, But there is a Hamilton circuit in the graph. Note:1)if G has a Hamilton circuit, then G has a Hamilton path Hamilton circuit :v 1,v 2,v 3,…v n,v 1 Hamilton path:v 1,v 2,v 3,…v n, 2)If G has a Hamilton path, then G has a Hamilton circuit or has not any Hamilton circuit

 Corollary 1: Let G be a simple graph with n vertices, n>2. G has a Hamilton circuit if each vertex has degree greater than or equal to n/2.  Proof: If any two vertices of G are adjacent,then G has a Hamilton circuit v 1,v 2,v 3,…v n,v 1 。  If G has two vertices u and v that are not adjacent, then d(u)+d(v)≥n.  By the theorem 5.9, G has a Hamilton circuit.  K n has a Hamilton circuit where n≥3

 Theorem 5.10: Let the number of edges of G be m. Then G has a Hamilton circuit if m≥(n 2 - 3n+6)/2,where n is the number of vertices of G.  Proof: If any two vertices of G are adjacent,then G has a Hamilton circuit v 1,v 2,v 3,…v n,v 1.  Suppose that u and v are any two vertices of G that are not adjacent.  Let H be the graph produced by eliminating u and v from G.  Thus H has n-2 vertices and m-d(u)-d(v) edges.

 Theorem : Let G be a simple graph with n vertices, n>2. G has a Hamilton path if for any two vertices u and v of G that are not adjacent, d(u)+d(v)  n-1.

5.4 Shortest-path problem  Let G=(V,E,w) be a weighted connected simple graph, w is a function from edges set E to position real numbers set. We denoted the weighted of edge {i,j} by w(i,j), and w(i,j)=+  when {i,j}  E  Definition 21: Let the length of a path p in a weighted graph G =(V,E,w) be the sum of the weights of the edges of this path. We denoted by w(p). The distance between two vertices u and v is the length of a shortest path between u and v, we denoted by d(u,v).

 Dijkstra’s algorithm (E.W.Dijkstra)  In 1959

 Let G=(V,E,w) and |V|=n where w>0. Suppose that S is a nonempty subset of V and v 1  S. Let T=V-S. Example: Suppose that (u,v',v'',v''',  v) is a shortest path between u and v. Then (u,v',v'',v''') is a shortest path between u and v'''.

 Exercise P306 3,4,5,6,18  Next: Shortest-path problem  Trees and their properties 7.4 P273