Cycles and Simple Cycles. Paths and Simple Paths. Trees. Problem: There is No Completely Standard Terminology!

Similar documents
Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example

Outline. Computer Science 331. Computation of Min-Cost Spanning Trees. Costs of Spanning Trees in Weighted Graphs

An undirected graph G = (V, E) V a set of vertices E a set of unordered edges (v,w) where v, w in V

Paths. Connectivity. Euler and Hamilton Paths. Planar graphs.

QUESTIONS BEGIN HERE!

CS 241 Analysis of Algorithms

(2) If we multiplied a row of B by λ, then the value is also multiplied by λ(here lambda could be 0). namely

ECE COMBINATIONAL BUILDING BLOCKS - INVEST 13 DECODERS AND ENCODERS

Graphs. Graphs. Graphs: Basic Terminology. Directed Graphs. Dr Papalaskari 1

CSE 373. Graphs 1: Concepts, Depth/Breadth-First Search reading: Weiss Ch. 9. slides created by Marty Stepp

QUESTIONS BEGIN HERE!

learning objectives learn what graphs are in mathematical terms learn how to represent graphs in computers learn about typical graph algorithms

0.1. Exercise 1: the distances between four points in a graph

5/7/13. Part 10. Graphs. Theorem Theorem Graphs Describing Precedence. Outline. Theorem 10-1: The Handshaking Theorem

Graphs. CSC 1300 Discrete Structures Villanova University. Villanova CSC Dr Papalaskari

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

CSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018

, each of which is a tree, and whose roots r 1. , respectively, are children of r. Data Structures & File Management

MAT3707. Tutorial letter 201/1/2017 DISCRETE MATHEMATICS: COMBINATORICS. Semester 1. Department of Mathematical Sciences MAT3707/201/1/2017

12/3/12. Outline. Part 10. Graphs. Circuits. Euler paths/circuits. Euler s bridge problem (Bridges of Konigsberg Problem)

Algorithmic and NP-Completeness Aspects of a Total Lict Domination Number of a Graph

5/9/13. Part 10. Graphs. Outline. Circuits. Introduction Terminology Implementing Graphs

CSC Design and Analysis of Algorithms. Example: Change-Making Problem

Why the Junction Tree Algorithm? The Junction Tree Algorithm. Clique Potential Representation. Overview. Chris Williams 1.

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

Graph Isomorphism. Graphs - II. Cayley s Formula. Planar Graphs. Outline. Is K 5 planar? The number of labeled trees on n nodes is n n-2

Garnir Polynomial and their Properties

Present state Next state Q + M N

1 Introduction to Modulo 7 Arithmetic

Outline. Circuits. Euler paths/circuits 4/25/12. Part 10. Graphs. Euler s bridge problem (Bridges of Konigsberg Problem)

Constructive Geometric Constraint Solving

b. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s?

Math 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes.

COMPLEXITY OF COUNTING PLANAR TILINGS BY TWO BARS

The University of Sydney MATH2969/2069. Graph Theory Tutorial 5 (Week 12) Solutions 2008

N=4 L=4. Our first non-linear data structure! A graph G consists of two sets G = {V, E} A set of V vertices, or nodes f

CS61B Lecture #33. Administrivia: Autograder will run this evening. Today s Readings: Graph Structures: DSIJ, Chapter 12

Designing A Concrete Arch Bridge

Solutions to Homework 5

Announcements. Not graphs. These are Graphs. Applications of Graphs. Graph Definitions. Graphs & Graph Algorithms. A6 released today: Risk

Seven-Segment Display Driver

COMP108 Algorithmic Foundations

CMSC 451: Lecture 2 Graph Basics Thursday, Aug 31, 2017

Multipoint Alternate Marking method for passive and hybrid performance monitoring

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura

CS 461, Lecture 17. Today s Outline. Example Run

Steinberg s Conjecture is false

Trees as operads. Lecture A formalism of trees

Planar Upward Drawings

Limits Indeterminate Forms and L Hospital s Rule

Exam 1 Solution. CS 542 Advanced Data Structures and Algorithms 2/14/2013

Outline. Binary Tree

Depth First Search. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong

Announcements. These are Graphs. This is not a Graph. Graph Definitions. Applications of Graphs. Graphs & Graph Algorithms

Round 7: Graphs (part I)

Minimum Spanning Trees

Greedy Algorithms, Activity Selection, Minimum Spanning Trees Scribes: Logan Short (2015), Virginia Date: May 18, 2016

Walk Like a Mathematician Learning Task:

Organization. Dominators. Control-flow graphs 8/30/2010. Dominators, control-dependence. Dominator relation of CFGs

Section 10.4 Connectivity (up to paths and isomorphism, not including)

Numbering Boundary Nodes

Lecture 20: Minimum Spanning Trees (CLRS 23)

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths.

More Foundations. Undirected Graphs. Degree. A Theorem. Graphs, Products, & Relations

Using the Printable Sticker Function. Using the Edit Screen. Computer. Tablet. ScanNCutCanvas

S i m p l i f y i n g A l g e b r a SIMPLIFYING ALGEBRA.

Problem solving by search

CS200: Graphs. Graphs. Directed Graphs. Graphs/Networks Around Us. What can this represent? Sometimes we want to represent directionality:

Chapter 18. Minimum Spanning Trees Minimum Spanning Trees. a d. a d. a d. f c

A 4-state solution to the Firing Squad Synchronization Problem based on hybrid rule 60 and 102 cellular automata

Nefertiti. Echoes of. Regal components evoke visions of the past MULTIPLE STITCHES. designed by Helena Tang-Lim

10/30/12. Today. CS/ENGRD 2110 Object- Oriented Programming and Data Structures Fall 2012 Doug James. DFS algorithm. Reachability Algorithms

CS September 2018

Weighted Graphs. Weighted graphs may be either directed or undirected.

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata

GREEDY TECHNIQUE. Greedy method vs. Dynamic programming method:

Solutions for HW11. Exercise 34. (a) Use the recurrence relation t(g) = t(g e) + t(g/e) to count the number of spanning trees of v 1

Two Approaches to Analyzing the Permutations of the 15 Puzzle

Graph Contraction and Connectivity

12. Traffic engineering

16.unified Introduction to Computers and Programming. SOLUTIONS to Examination 4/30/04 9:05am - 10:00am

Integration Continued. Integration by Parts Solving Definite Integrals: Area Under a Curve Improper Integrals

Grade 7/8 Math Circles March 4/5, Graph Theory I- Solutions

CS 103 BFS Alorithm. Mark Redekopp

NEW APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA

NP-Completeness. CS3230 (Algorithm) Traveling Salesperson Problem. What s the Big Deal? Given a Problem. What s the Big Deal? What s the Big Deal?

Decimals DECIMALS.

Properties of Hexagonal Tile local and XYZ-local Series

EE1000 Project 4 Digital Volt Meter

1. Determine whether or not the following binary relations are equivalence relations. Be sure to justify your answers.

ENGR 323 BHW 15 Van Bonn 1/7

The University of Sydney MATH 2009

10. EXTENDING TRACTABILITY

Spanning Trees. BFS, DFS spanning tree Minimum spanning tree. March 28, 2018 Cinda Heeren / Geoffrey Tien 1

c 2009 Society for Industrial and Applied Mathematics

Weighted graphs -- reminder. Data Structures LECTURE 15. Shortest paths algorithms. Example: weighted graph. Two basic properties of shortest paths

Research Article On the Genus of the Zero-Divisor Graph of Z n

a b c cat CAT A B C Aa Bb Cc cat cat Lesson 1 (Part 1) Verbal lesson: Capital Letters Make The Same Sound Lesson 1 (Part 1) continued...

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees

Shortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk

Transcription:

Outlin Computr Sin 331, Spnnin, n Surphs Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #30 1 Introution 2 3 Dinition 4 Spnnin 5 6 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 1 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 2 / 20 Introution, Spnnin n Surphs Pths n Simpl Pths Dinition: A pth in n unirt rph G = (V, E) is squn o zro or mor s in G Gols or th Ltur: W will introu prtiulr typ o rph (r) tr tht will us in initions o rph prolms, n rph lorithms, throuhout th rst o this ours Aitionl importnt initions n rph proprtis will lso introu (v 0, v 1 ), (v 1, v 2 ), (v 2, v 3 ),..., (v k 1, v k ) whr th son vrtx (shown) in h is th irst vrtx (shown) in th nxt. Th pth shown ov is pth rom v 0 (th irst vrtx in th irst ) to v k (th son vrtx in th inl ). This is simpl pth i v 0, v 1,..., v k r istint. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 3 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 4 / 20

Pths n Simpl Pths Cyls n Simpl Cyls Dinition: A yl (in n unirt rph G = (V, E)) is pth with lnth rtr thn zro rom som vrtx to itsl: Dinition: Th lnth o pth is th lnth o th squn o s in it. Thus th pth shown in th prvious sli hs lnth k. Dinition: An unirt rph G = (V, E) is onnt rph i thr is pth rom u to v, or vry pir o vrtis u, v V. A yl (v 0, v 1 ), (v 1, v 2 ),..., (v k 2, v k 1 ), (v k 1, v 0 ) is simpl yl i v 0, v 1,..., v k 1 r istint. A rph G = (V, E) is yli i it os not hv ny yls. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 5 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 6 / 20 Dinition Prolm: Thr is No Compltly Stnr Trminoloy! Prolm with Trminoloy Dirnt rrns tn to us ths trms irntly! For xmpl, in som txtooks, simpl yl is onsir to kin o simpl pth, n th inition o yl ivn is th sm s th inition o simpl yl ivn ov Othr rrns only ll somthin pth i it is simpl pth, s in ov; thy only ll somthin yl i it is simpl yl; n thy us th trm wlk to rr to th mor nrl kin o pth tht is in in ths nots Consqun: You shoul hk th initions o ths trms in ny othr rrns tht you us! Dinition: A r tr is onnt yli rph. Frquntly w just ll r tr tr. I w intiy on vrtx s th root, thn th rsult is th kin o root tr w hv sn or. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 7 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 8 / 20

1 2 Consir rph G = (V, E): 1 I G is onnt thn E V 1 2 I G is yli thn E V 1 3 I G is onnt n yli thn E = V 1 S th ltur supplmnt or proos. Consir rph G = (V, E). W will us th ollowin proprtis to hrtriz trs: 1 I G is tr thn it hs V 1 s 2 An yli rph with V 1 s is tr 3 A onnt rph with V 1 s is tr S th ltur supplmnt or proos. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 9 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 10 / 20 Spnnin Spnnin Spnnin Suppos G = (V, E) is s ollows. I G = (V, E) is onnt unirt rph, thn spnnin tr o G is surph Ĝ = ( V, Ê) o G suh tht V = V (so tht Ĝ inlus ll th vrtis in G) Ê E Ĝ is tr. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 11 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 12 / 20

Spnnin Spnnin Tr 1 Tr 2 Is th ollowin rph G 1 = (V 1, E 1 ) spnnin tr o G? Ys! Is th ollowin rph G 2 = (V 2, E 2 ) lso spnnin tr o G? Ys! Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 13 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 14 / 20 Tr 3 Spnnin Is th ollowin rph G 3 = (V 3, E 3 ) is lso spnnin tr o G? No! Dosn t spn G (vrtx missin) Suppos G = (V, E) is rph. Ĝ = ( V, Ê) is surph o G i Ĝ is rph suh tht V V n Ê E G = (Ṽ, Ẽ) is n inu surph o G i G is surph o G n, urthrmor { (u, v) E u, v Ṽ Ẽ = tht it possily oul }, tht is, G inlus ll th s rom G Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 15 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 16 / 20

G 2 is n inu surph o G 1. G 3 is surph o G 1, ut G 3 is not n inu surph o G 1. G 1 G 2 G 3 Lt G = (V, E) n lt s V. Construt sust V p o V, sust E p o E, n untion π : V V {NIL} s ollows. Initilly, V p = {s}, E p =, n π(v) = NIL or vry vrtx v V. Th ollowin stp is prorm, twn 0 n V 1 tims: Pik som vrtx u rom th st V p. Pik som vrtx v V suh tht v / V p n (u, v) E. (Th pross must n i this is not possil to o.) St π(v) to u, th vrtx v to th st V p, n th (u, v) = (π(v), v) to E p Not tht V p V, E p E, n h in E p onnts pirs o vrtis tht h lons to V p h tim th ov (intrior) stp is prorm so tht G p = (V p, E p ) is lwys surph o G. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 17 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 18 / 20 Prssor Surph Proprty h i h i π NIL h Th rph G p = (V p, E p ) tht hs n onstrut is ll prssor surph. Clim: Lt G p = (V p, E p ) prssor surph o n unirt rph G. ) G p is surph o G n G p is tr. ) I V p = V thn G p is spnnin tr o G. Proo. Prt () is tru us E p = V p 1, y th onstrution o V p n o E p, n G p is lwys onnt, so G p is tr, s wll s surph o G. Prt () now ollows y th t tht E p is sust o E, so tht G p is surph o G, n y th t tht V p = V. Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 19 / 20 Mik Joson (Univrsity o Clry) Computr Sin 331 Ltur #30 20 / 20