Discrete Mathematics. Chapter 4. Relations and Digraphs Sanguk Noh
|
|
- Stephanie Greene
- 5 years ago
- Views:
Transcription
1 Discrete Mathematics Chapter 4. Relations and Digraphs Sanguk Noh
2 Table Product sets and partitions Relations and digraphs Paths in relations and digraphs Properties of relations Equivalence relations Operations on relations
3 Product sets and partitions Def.) product set or Cartesian product A B A B={(a,b) a A and b B} e.g.) A={,2,3} and B={r,s} A B={(,r),(,s),(2,r),(2,s),(3,r),(3,s)} Def.) partition or quotient set P of a nonempty set A Each element of A belongs to one of the sets in P. If A and A 2 are distinct elements of P, then A A 2 =. e.g.) A={a, b, c, d, e, f, g, h} A ={a,b,c,d}, A 2 ={a,c,e,f,g,h}, A 3 ={a,c,e,g}, A 4 ={b,d} A 5 ={f,h} {A, A 2 } : not a partition A A 2 P={A 3,A 4,A 5 } is a partition of A.
4 Relations and digraphs Def.) Let A and B be nonempty sets. Relation R from A to B A B. If R A B and (a,b) R, then a is related to b by R, a R b. When R A A, R is a relation on A. e.g.) A: the set of positive integers a R b iff a divides b, a b. Then, 4R2, 5R7. Def.) Let R A B be a relation from A to B. the domain of R, Dom(R) : the set of elements in A the range of R, Ran(R) : the set of elements in B the R-relative set of x, R(x)={y B x R y}, if R is a relation from A to B and x A. e.g.) A={a,b,c,d} and R A A. R={(a,a),(a,b),(b,c),(c,a),(d,c),(c,b)} If A ={c,d}, then R(A )={a,b,c}
5 Relations and digraphs Theorem : Let R be a relation from A to B, and let A A and A 2 A. If A A 2, then R(A ) R(A 2 ). R(A A 2 )=R(A ) R(A 2 ). R(A A 2 ) R(A ) R(A 2 ) e.g.) A={x x is an integer} R: A ={,,2}, A 2 ={9,3} R(A )={,,2, }, if x y. R(A 2 )={9,,, }, if x y. R(A ) R(A 2 )={9,,, } A A 2 =, R(A A 2 ) = n or n or 2 n
6 Relations and digraphs e.g.) Let A= {,2,3} and B={x,y,z,w,p,q}, and let R A B. Then, consider R={(,x),(,z),(2,w),(2,p),(2,q),(3,y)}. Let A ={,2} and A 2 ={2,3}. () R(A )={x,z,w,p,q} R(A 2 )={w,p,q,y} R(A ) R(A 2 )={x,y,z,w,p,q}=b Since A A 2 =A, R(A A 2 )=R(A)=B (2) R(A ) R(A 2 )={w,p,q}=r(a A 2 ) R(A ) R(A 2 ) R(A A 2 )
7 Relations and digraphs Def.) If A={a,a 2 a m }and B={b,b 2 b n } are finite sets, and R is a relation from A to B, then R: m n matrix M R =[m ij ], m ij = if (a i,b j ) R if (a i,b j ) R M R : the matrix of R. e.g.) A={,2,3}, B={r,s}, R={(,r),(2,s),(3,r)} the matrix of R, m n, M R = 2 3
8 Relations and digraphs Def.) digraph (or directed graph) of R vertices 2 3 Edge: a 3 R a e.g.) A={,2}, R={(,),(,2),(2,),(2,2)} R is a relation on A. 2
9 Relations and digraphs Def.) in-degree of a: the no. of b A s.t. (b,a) R out-degree of a: the no. of b A s.t. (a,b) R e.g.) The vertex in the previous figure has in-degree 2. e.g.) A={,4,5} R 4 5 Sol.) M R = R={(,4),(,5),(4,),(4,4),(5,4),(5,5)}
10 Relations and digraphs Def.) the restriction of R to B : R (B B) if R is a relation on a set A and B A. e.g.) R (B B) A={a,b,c}, B={a,b} R={(a,a),(a,c),(b,c),(b,a),(c,c)} B B={(a,a),(a,b),(b,a),(b,b)} R (B B) = {(a,a),(b,a)}
11 Paths in relations and digraphs Def.) a path of length n in R from a to b: π: a, x,x 2,,x n-, b such that a finite sequence arx, x Rx 2,, x n- Rb e.g.) 2 n+ elements π :,2,3 a path of length 2 from vertex to vertex 3 π 2 :,2,5, π 3 :2,3
12 Paths in relations and digraphs Def.) Cycle : a path that begins and ends at the same vertex. x R n y: There is a path of length n from x to y in R x R y: There is some path in R from x to y. connectivity relation for R. e.g.) A={a,b,c,d,e}, R={(a,a),(a,b),(b,c),(c,e),(c,d),(d,e)} (a) R 2 (b) R (a) R 2 ={(a,a),(a,b),(a,c),(b,d),(b,e),(c,e)} a R 2 a a R 2 b a R 2 c b R 2 d b R 2 e c R 2 e (b) R ={(a,a),(a,b),(a,c),(a,d),(a,e),(b,c,),(b,d),(b,e),(c,d),(c,e),(d,e)} a b d c e
13 Paths in relations and digraphs Theorem R is a relation on A ={a,a 2, a n }. M R2 =M R M R Proof) M R =[m ij ] M R2 =[n ij ] By the definition of M R M R, the i, jth element of M R M R is l iff the row i of M R and the column j of M R have a in the same relative position, say k. m ik = and m kj = for some k, k n. By the definition of M R, this means that a i R a k and a k R a j. Thus, a i R 2 a j, so n ij = M R M R = M R 2
14 Paths in relations and digraphs Theorem For n 2 and R a relation on a finite set A, we have M R n = M R M R M R (n factors). Proof by induction Basis step Let n=2. M 2 R = M R M R Induction hypothesis n=k for some k 2, M k R = M R.. M R (k factors) Induction step n=k+. M k+ R =[x ij ], M k R =[y ij ], and M R =[m ij ] if x ij =, we must have a path of length k+ from a i to a j. a i a s a j k
15 Paths in relations and digraphs y is = and m sj =, so M R k M R has a in position i,j. Similarly, if M R k M R has a l in position i,j, then x ij =. M R k+ = M R k M R M R k+ = M R k M R = (M R.. M R ) M R (k+ factors) Thus, by the induction, M R n = M R M R M R (n factors) is true for all n 2.
16 Paths in relations and digraphs Page 4, #26 A={,2,3,4,5}, R: arb iff a<b (a) R 2 and R 3 R 2 = R 3 = Then, R=? (b) a R 2 b iff? (c) a R 3 b iff?
17 Properties of relations Def.) reflexive a relation R on a set A( R A A) is reflexive if (a,a) R for all a A. e.g.) A={,2,3}, R={(,),(2,2),(3,3)}:reflexive cf.) irreflexive if (a,a) R for all a A. e.g.) R={(,),(2,3)} Is the empty relation reflexive?
18 Properties of relations Def.) Symmetric A relation R A A is symmetric if whenever arb, then bra. Def.) asymmetric If whenever arb, then bra Def.) antisymmetric If whenever arb and bra, then a=b if whenever a b, arb or bra
19 Properties of relations e.g.) A: the set of integers. R={(a,b) A A a<b} Sol.) Symmetric if a<b, then b a, R is not symmetric. Asymmetric if a<b, then b a, R is asymmetric. Antisymmetric if a b, then either a b or b a, R is antisymmetric
20 Properties of relations e.g.) A={,2,3,4}, R={(,2),(2,2),(3,4),(4,)} R: not symmetric (,2) R but (2,) R not asymmetric (2,2) R antisymmetric! Let M R =[m ij ]. symmetric if m ij =, then m ji = if m ij =, then m ji = 2. asymmetric if m ij =, then m ji = m ii = for all i if R is asymmetric. 3. antisymmetric if i j, then m ij = or m ji =.
21 Properties of relations e.g.) M R = M R2 = M R3 = Reflexive Irreflexive M R M R2 M R3 Symmetric Asymmetric antisymmetric
22 Properties of relations Digraph and graph of symmetric relation Let A= {a,b,c} and R A A. a c a c b b Digraph of R graph of R R =
23 Properties of relations Def.) Transitive If whenever a R b and b R c, then a R c. e.g.) A: the set of integers. R : the realtion < a R b and b R c. a,b,c A a<b and b<c, then a<c, so a R c. Hence R is transitive. e.g.) A={,2,3,4} R={(,2),(,3),(4,2)} transitive! e.g.) A={,2,3} M R = R={(,),(,2),(,3),(2,3),(3,3)} Since (,2) and (2,3), then (,3). Since (,3) and (3,3), then (,3).
24 Properties of relations Theorem R AxA Reflexivity of R a R(a) for all a in A Symmetry of R a R(b) iff b R(a) Transitivity of R if b R(a) and c R(b), then c R(a)
25 Equivalence relations Def.) A relation R on a set A is an equivalence if it is reflexive, symmetric, and transitive. e.g.) Let A={,2,3,4} and R={(,),(,2),(2,),(2,2),(3,4),(4,3),(3,3),(4,4)} reflexive? symmetric? transitive?
26 Equivalence relations Theorem Let P be a partition of a set A. Define the relation R on A: a R b iff a and b are members of the same block. Then R is an equivalence relation on A. Proof Reflexive : If a A, then a R a. ( a is in the same block) Symmetric : If a R b, a and b are in the same block. So. b R a Transitive : If a R b and b R c, then a, b, and c must be in the same block of P. Thus, a R c. R is the equivalence relation determined by P.
27 Equivalence relations Theorem Let R be an equivalence relation on A, and let P be the collection of all distinct relative sets R(a) for a in A. Then P is a partition of A, and R is the equivalence relation determined by P. Proof (a) Every element of A belongs to some relative set. (b) If R(a) and R(b) are not identical, then R(a) R(b)=. (a) is true, since a R(a) by reflexivity of R. (b) If R(a) R(b), then R(a)=R(b). Then a R c and b R c Since R is symmetric, c R b, and a R b by transitivity. By Lemma, R(a)=R(b), Thus, P is a partition. By Theorem, P determines R.
28 Equivalence relations e.g.) Let A={,2,3,4} and R={(,),(,2),(2,),(2,2),(3,4),(4,3),(3,3),(4,4)} Find partition of A Sol.) R() = {,2}=R(2) R(3) = {3,4}=R(4) Given R, hence, P={{,2},{3,4}} Page 52, problem 2. Let A={a,b,c,d,e} and R AxA. M R = a b d d e a b c d e partition of A : A/R=?
29 Equivalence relations Page 5, #4. A={,2,3,4,5}, P={{,3,5},{2,4}} equivalence relation R?
30 Operations on relations Operations R,S A B. complementary relation : R 2. intersection : R S 3. union : R S 4. inverse : R - : relation from B to A R - B A e.g.) A={,2,3,4}, B={a,b,c} R={(,a), (,b), (2,b), (2,c), (3,b), (4,a)} S={(,b),(2,c),(3,b),(4,b)}
31 Operations on relations Sol.) A B = {(,a),.,(4,c)} (a) R=? (b) R S (c) R S (d) R -
32 Operations on relations Closures reflexive closure of R a d a d b c b c R (does not possess a reflexive property) The reflexive closure of R Symmetric closure of R If A={a,b,c,d} and R={(a,b),(b,c),(a,c),(c,d)}, then R - ={(b,a), (c,b),(c,a),(d,c)} the symmetric closure of R: R R - ={(a,b),(b,a),(b,c),(c,b),(a,c),(c,a),(c,d),(d,c)}
33 Operations on relations Transitive closure of R a d b c R is not transitive Method : Finding transitivity from R Method 2: computing R Method 3: Warshall s algorithm
34 Warshall s algorithm e.g.) A= {,2,3,4}, R={(,2),(2,3),(3,4),(2,)} Method (,2),(2,3) (,3). (,2),(2,) (,) Method 2 2 R ={(,),(,2),(,3),(,4),(2,) (3,4)} 4 3
35 Warshall s algorithm Method 3 Warshall s algorithm How to implement the transitive closure of R! Boolean matrix W k A={a,a 2,,a n }, R A A, k n W k =[t ij ] t ij = iff there is a path from a i to a j in R whose interior vertices come from the set {a,a 2,,a k }. W n : iff some path in R connects a i with a j. W n = M R (since any vertex must come from the set {a,a 2,,a n }) Suppose W k =[t ij ] and W k- =[s ij ] () s ij = or (2) s ik = and s kj = t ij = Subpath a k Subpath 2 a i a j
36 Warshall s algorithm Algorithm Step : Copy W k- into W k. W =M R. Step 2: List the locations p,p 2, in column k of W k-, where the entry is, and the locations q,q 2, in row k of W k-, where the entry is. Step 3: Put s in all the positions p i, q j of W k.
37 Warshall s algorithm e.g.) A={,2,3,4}, R={(,2),(2,3),(3,4),(2,)} W =M R = k 4(=n). k= 2-, -2 i k k j 2. k=2 i 2,2 j k=3 3 i 3,3 4 j 2 w = w 2 = w 3 = 4. k=4 4, 4-(None of s) 2 3 No new s are added. M R =W 4 =W 3
Relations Graphical View
Introduction Relations Computer Science & Engineering 235: Discrete Mathematics Christopher M. Bourke cbourke@cse.unl.edu Recall that a relation between elements of two sets is a subset of their Cartesian
More informationNotes. Relations. Introduction. Notes. Relations. Notes. Definition. Example. Slides by Christopher M. Bourke Instructor: Berthe Y.
Relations Slides by Christopher M. Bourke Instructor: Berthe Y. Choueiry Spring 2006 Computer Science & Engineering 235 Introduction to Discrete Mathematics Sections 7.1, 7.3 7.5 of Rosen cse235@cse.unl.edu
More informationCHAPTER 1. Relations. 1. Relations and Their Properties. Discussion
CHAPTER 1 Relations 1. Relations and Their Properties 1.1. Definition of a Relation. Definition 1.1.1. A binary relation from a set A to a set B is a subset R A B. If (a, b) R we say a is Related to b
More informationRelations --- Binary Relations. Debdeep Mukhopadhyay IIT Madras
Relations --- Binary Relations Debdeep Mukhopadhyay IIT Madras What is a relation? The mathematical concept of relation is based on the common notion of relationships among objects: One box is heavier
More informationSection Summary. Relations and Functions Properties of Relations. Combining Relations
Chapter 9 Chapter Summary Relations and Their Properties n-ary Relations and Their Applications (not currently included in overheads) Representing Relations Closures of Relations (not currently included
More informationChapter 9: Relations Relations
Chapter 9: Relations 9.1 - Relations Definition 1 (Relation). Let A and B be sets. A binary relation from A to B is a subset R A B, i.e., R is a set of ordered pairs where the first element from each pair
More informationDefinition: A binary relation R from a set A to a set B is a subset R A B. Example:
Chapter 9 1 Binary Relations Definition: A binary relation R from a set A to a set B is a subset R A B. Example: Let A = {0,1,2} and B = {a,b} {(0, a), (0, b), (1,a), (2, b)} is a relation from A to B.
More informationRelations. Relations of Sets N-ary Relations Relational Databases Binary Relation Properties Equivalence Relations. Reading (Epp s textbook)
Relations Relations of Sets N-ary Relations Relational Databases Binary Relation Properties Equivalence Relations Reading (Epp s textbook) 8.-8.3. Cartesian Products The symbol (a, b) denotes the ordered
More informationAutomata and Languages
Automata and Languages Prof. Mohamed Hamada Software Engineering Lab. The University of Aizu Japan Mathematical Background Mathematical Background Sets Relations Functions Graphs Proof techniques Sets
More informationICS 6N Computational Linear Algebra Matrix Algebra
ICS 6N Computational Linear Algebra Matrix Algebra Xiaohui Xie University of California, Irvine xhx@uci.edu February 2, 2017 Xiaohui Xie (UCI) ICS 6N February 2, 2017 1 / 24 Matrix Consider an m n matrix
More informationCOMP 182 Algorithmic Thinking. Relations. Luay Nakhleh Computer Science Rice University
COMP 182 Algorithmic Thinking Relations Luay Nakhleh Computer Science Rice University Chapter 9, Section 1-6 Reading Material When we defined the Sorting Problem, we stated that to sort the list, the elements
More informationWhat are relations? f: A B
What are relations? Ch 9.1 What are relations? Notation Informally, a relation is a set of pairs of objects (or in general, set of n-tuples) that are related to each other by some rule. We will focus first
More informationWeek 4-5: Binary Relations
1 Binary Relations Week 4-5: Binary Relations The concept of relation is common in daily life and seems intuitively clear. For instance, let X be the set of all living human females and Y the set of all
More informationWeek 4-5: Binary Relations
1 Binary Relations Week 4-5: Binary Relations The concept of relation is common in daily life and seems intuitively clear. For instance, let X be the set of all living human females and Y the set of all
More informationDiscrete Mathematics. 2. Relations
Discrete Mathematics 2. Relations Binary Relations Let A, B be any two sets. A binary relation R from A to B is a subset of A B. E.g., Let < : N N : {(n,m) n < m} The notation a R b or arb means (a,b)îr.
More informationKevin James. MTHSC 3110 Section 2.1 Matrix Operations
MTHSC 3110 Section 2.1 Matrix Operations Notation Let A be an m n matrix, that is, m rows and n columns. We ll refer to the entries of A by their row and column indices. The entry in the i th row and j
More informationMath 4377/6308 Advanced Linear Algebra
2.3 Composition Math 4377/6308 Advanced Linear Algebra 2.3 Composition of Linear Transformations Jiwen He Department of Mathematics, University of Houston jiwenhe@math.uh.edu math.uh.edu/ jiwenhe/math4377
More informationCSC Discrete Math I, Spring Relations
CSC 125 - Discrete Math I, Spring 2017 Relations Binary Relations Definition: A binary relation R from a set A to a set B is a subset of A B Note that a relation is more general than a function Example:
More informationFall Inverse of a matrix. Institute: UC San Diego. Authors: Alexander Knop
Fall 2017 Inverse of a matrix Authors: Alexander Knop Institute: UC San Diego Row-Column Rule If the product AB is defined, then the entry in row i and column j of AB is the sum of the products of corresponding
More informationMatrix operations Linear Algebra with Computer Science Application
Linear Algebra with Computer Science Application February 14, 2018 1 Matrix operations 11 Matrix operations If A is an m n matrix that is, a matrix with m rows and n columns then the scalar entry in the
More informationChapter VI. Relations. Assumptions are the termites of relationships. Henry Winkler
Chapter VI Relations Assumptions are the termites of relationships. Henry Winkler Studying relationships between objects can yield important information about the objects themselves. In the real numbers,
More informationRelations, Functions, Binary Relations (Chapter 1, Sections 1.2, 1.3)
Relations, Functions, Binary Relations (Chapter 1, Sections 1.2, 1.3) CmSc 365 Theory of Computation 1. Relations Definition: Let A and B be two sets. A relation R from A to B is any set of ordered pairs
More informationMath 42, Discrete Mathematics
c Fall 2018 last updated 12/05/2018 at 15:47:21 For use by students in this class only; all rights reserved. Note: some prose & some tables are taken directly from Kenneth R. Rosen, and Its Applications,
More informationPacket #5: Binary Relations. Applied Discrete Mathematics
Packet #5: Binary Relations Applied Discrete Mathematics Table of Contents Binary Relations Summary Page 1 Binary Relations Examples Page 2 Properties of Relations Page 3 Examples Pages 4-5 Representations
More information2 Equivalence Relations
2 Equivalence Relations In mathematics, we often investigate relationships between certain objects (numbers, functions, sets, figures, etc.). If an element a of a set A is related to an element b of a
More informationMath.3336: Discrete Mathematics. Chapter 9 Relations
Math.3336: Discrete Mathematics Chapter 9 Relations Instructor: Dr. Blerina Xhabli Department of Mathematics, University of Houston https://www.math.uh.edu/ blerina Email: blerina@math.uh.edu Fall 2018
More informationRelationships between elements of sets occur in many contexts. Every day we deal with
C H A P T E R 9 Relations 9.1 Relations and Their Properties 9.2 n-ary Relations and Their Applications 9.3 Representing Relations 9.4 Closures of Relations 9.5 Equivalence Relations 9.6 Partial Orderings
More informationMAD 3105 PRACTICE TEST 2 SOLUTIONS
MAD 3105 PRACTICE TEST 2 SOLUTIONS 1. Let R be the relation defined below. Determine which properties, reflexive, irreflexive, symmetric, antisymmetric, transitive, the relation satisfies. Prove each answer.
More informationEquivalence, Order, and Inductive Proof
2/ chapter 4 Equivalence, Order, and Inductive Proof Good order is the foundation of all things. Edmund Burke (729 797) Classifying things and ordering things are activities in which we all engage from
More information3. R = = on Z. R, S, A, T.
6 Relations Let R be a relation on a set A, i.e., a subset of AxA. Notation: xry iff (x, y) R AxA. Recall: A relation need not be a function. Example: The relation R 1 = {(x, y) RxR x 2 + y 2 = 1} is not
More informationSet Theory. CSE 215, Foundations of Computer Science Stony Brook University
Set Theory CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.edu/~cse215 Set theory Abstract set theory is one of the foundations of mathematical thought Most mathematical
More informationMassachusetts Institute of Technology 6.042J/18.062J, Fall 02: Mathematics for Computer Science Professor Albert Meyer and Dr.
Massachusetts Institute of Technology 6.042J/18.062J, Fall 02: Mathematics for Computer Science Professor Albert Meyer and Dr. Radhika Nagpal Quiz 1 Appendix Appendix Contents 1 Induction 2 2 Relations
More information5. Partitions and Relations Ch.22 of PJE.
5. Partitions and Relations Ch. of PJE. We now generalize the ideas of congruence classes of Z to classes of any set X. The properties of congruence classes that we start with here are that they are disjoint
More informationNotes on Algebraic Structures. Peter J. Cameron
Notes on Algebraic Structures Peter J. Cameron ii Preface These are the notes of the second-year course Algebraic Structures I at Queen Mary, University of London, as I taught it in the second semester
More informationSets and Motivation for Boolean algebra
SET THEORY Basic concepts Notations Subset Algebra of sets The power set Ordered pairs and Cartesian product Relations on sets Types of relations and their properties Relational matrix and the graph of
More information1.4 Equivalence Relations and Partitions
24 CHAPTER 1. REVIEW 1.4 Equivalence Relations and Partitions 1.4.1 Equivalence Relations Definition 1.4.1 (Relation) A binary relation or a relation on a set S is a set R of ordered pairs. This is a very
More informationLogic, Set Theory and Computability [M. Coppenbarger]
7 Relations (Handout) Definition 7-1: A set r is a relation from X to Y if r X Y. If X = Y, then r is a relation on X. Definition 7-2: Let r be a relation from X to Y. The domain of r, denoted dom r, is
More informationRegular Expressions [1] Regular Expressions. Regular expressions can be seen as a system of notations for denoting ɛ-nfa
Regular Expressions [1] Regular Expressions Regular expressions can be seen as a system of notations for denoting ɛ-nfa They form an algebraic representation of ɛ-nfa algebraic : expressions with equations
More informationRelations, Functions, and Sequences
MCS-236: Graph Theory Handout #A3 San Skulrattanakulchai Gustavus Adolphus College Sep 13, 2010 Relations, Functions, and Sequences Relations An ordered pair can be constructed from any two mathematical
More informationCS Discrete Mathematics Dr. D. Manivannan (Mani)
CS 275 - Discrete Mathematics Dr. D. Manivannan (Mani) Department of Computer Science University of Kentucky Lexington, KY 40506 Course Website: www.cs.uky.edu/~manivann/cs275 Notes based on Discrete Mathematics
More informationToday. Binary relations establish a relationship between elements of two sets
Today Relations Binary relations and properties Relationship to functions n-ary relations Definitions Binary relations establish a relationship between elements of two sets Definition: Let A and B be two
More informationcse303 ELEMENTS OF THE THEORY OF COMPUTATION Professor Anita Wasilewska
cse303 ELEMENTS OF THE THEORY OF COMPUTATION Professor Anita Wasilewska LECTURE 1 Course Web Page www3.cs.stonybrook.edu/ cse303 The webpage contains: lectures notes slides; very detailed solutions to
More informationDiscrete Mathematics. W. Ethan Duckworth. Fall 2017, Loyola University Maryland
Discrete Mathematics W. Ethan Duckworth Fall 2017, Loyola University Maryland Contents 1 Introduction 4 1.1 Statements......................................... 4 1.2 Constructing Direct Proofs................................
More informationBASIC MATHEMATICAL TECHNIQUES
CHAPTER 1 ASIC MATHEMATICAL TECHNIQUES 1.1 Introduction To understand automata theory, one must have a strong foundation about discrete mathematics. Discrete mathematics is a branch of mathematics dealing
More information9 RELATIONS. 9.1 Reflexive, symmetric and transitive relations. MATH Foundations of Pure Mathematics
MATH10111 - Foundations of Pure Mathematics 9 RELATIONS 9.1 Reflexive, symmetric and transitive relations Let A be a set with A. A relation R on A is a subset of A A. For convenience, for x, y A, write
More informationMatrix Algebra 2.1 MATRIX OPERATIONS Pearson Education, Inc.
2 Matrix Algebra 2.1 MATRIX OPERATIONS MATRIX OPERATIONS m n If A is an matrixthat is, a matrix with m rows and n columnsthen the scalar entry in the ith row and jth column of A is denoted by a ij and
More informationAny Wizard of Oz fans? Discrete Math Basics. Outline. Sets. Set Operations. Sets. Dorothy: How does one get to the Emerald City?
Any Wizard of Oz fans? Discrete Math Basics Dorothy: How does one get to the Emerald City? Glynda: It is always best to start at the beginning Outline Sets Relations Proofs Sets A set is a collection of
More informationLecture Notes in Linear Algebra
Lecture Notes in Linear Algebra Dr. Abdullah Al-Azemi Mathematics Department Kuwait University February 4, 2017 Contents 1 Linear Equations and Matrices 1 1.2 Matrices............................................
More informationCS100: DISCRETE STRUCTURES. Lecture 3 Matrices Ch 3 Pages:
CS100: DISCRETE STRUCTURES Lecture 3 Matrices Ch 3 Pages: 246-262 Matrices 2 Introduction DEFINITION 1: A matrix is a rectangular array of numbers. A matrix with m rows and n columns is called an m x n
More informationDefinition: A binary relation R from a set A to a set B is a subset R A B. Example:
Section 9.1 Rela%onships Relationships between elements of sets occur in many contexts. Every day we deal with relationships such as those between a business and its telephone number, an employee and his
More informationOn Regularity of Incline Matrices
International Journal of Algebra, Vol. 5, 2011, no. 19, 909-924 On Regularity of Incline Matrices A. R. Meenakshi and P. Shakila Banu Department of Mathematics Karpagam University Coimbatore-641 021, India
More informationLecture 8: Equivalence Relations
Lecture 8: Equivalence Relations 1 Equivalence Relations Next interesting relation we will study is equivalence relation. Definition 1.1 (Equivalence Relation). Let A be a set and let be a relation on
More informationToday s topics. Binary Relations. Inverse Relations. Complementary Relations. Let R:A,B be any binary relation.
Today s topics Binary Relations Relations Kinds of relations n-ary relations Representations of relations Reading: Sections 7.-7.3 Upcoming Upcoming Minesweeper Let A, B be any sets. A binary relation
More informationReading 11 : Relations and Functions
CS/Math 240: Introduction to Discrete Mathematics Fall 2015 Reading 11 : Relations and Functions Instructor: Beck Hasti and Gautam Prakriya In reading 3, we described a correspondence between predicates
More information0 Sets and Induction. Sets
0 Sets and Induction Sets A set is an unordered collection of objects, called elements or members of the set. A set is said to contain its elements. We write a A to denote that a is an element of the set
More informationSection 7.1 Relations and Their Properties. Definition: A binary relation R from a set A to a set B is a subset R A B.
Section 7.1 Relations and Their Properties Definition: A binary relation R from a set A to a set B is a subset R A B. Note: there are no constraints on relations as there are on functions. We have a common
More informationright angle an angle whose measure is exactly 90ᴼ
right angle an angle whose measure is exactly 90ᴼ m B = 90ᴼ B two angles that share a common ray A D C B Vertical Angles A D C B E two angles that are opposite of each other and share a common vertex two
More informationGenerating Permutations and Combinations
Generating Permutations and Combinations March 0, 005 Generating Permutations We have learned that there are n! permutations of {,,, n} It is important in many instances to generate a list of such permutations
More informationCOT3100 SI Final Exam Review
1 Symbols COT3100 SI Final Exam Review Jarrett Wendt Spring 2018 You ve learned a plethora of new Mathematical symbols this semester. Let s see if you know them all and what they re used for. How many
More informationChapter 1. Sets and Numbers
Chapter 1. Sets and Numbers 1. Sets A set is considered to be a collection of objects (elements). If A is a set and x is an element of the set A, we say x is a member of A or x belongs to A, and we write
More informationSpring Based on Partee, ter Meulen, & Wall (1993), Mathematical Methods in Linguistics
1 / 17 L545 Spring 2013 Based on Partee, ter Meulen, & Wall (1993), Mathematical Methods in Linguistics 2 / 17 Why set theory? Set theory sets the foundation for much of mathematics For us: provides precise
More informationReview Problems for Midterm Exam II MTH 299 Spring n(n + 1) 2. = 1. So assume there is some k 1 for which
Review Problems for Midterm Exam II MTH 99 Spring 014 1. Use induction to prove that for all n N. 1 + 3 + + + n(n + 1) = n(n + 1)(n + ) Solution: This statement is obviously true for n = 1 since 1()(3)
More informationKevin James. MTHSC 412 Section 3.1 Definition and Examples of Rings
MTHSC 412 Section 3.1 Definition and Examples of Rings A ring R is a nonempty set R together with two binary operations (usually written as addition and multiplication) that satisfy the following axioms.
More informationWeek 4-5: Generating Permutations and Combinations
Week 4-5: Generating Permutations and Combinations February 27, 2017 1 Generating Permutations We have learned that there are n! permutations of {1, 2,...,n}. It is important in many instances to generate
More informationAutomata Theory for Presburger Arithmetic Logic
Automata Theory for Presburger Arithmetic Logic References from Introduction to Automata Theory, Languages & Computation and Constraints in Computational Logic Theory & Application Presented by Masood
More informationCS2013: Relations and Functions
CS2013: Relations and Functions 20/09/16 Kees van Deemter 1 Relations and Functions Some background for CS2013 Necessary for understanding the difference between Deterministic FSAs (DFSAs) and NonDeterministic
More informationChapter 0. Introduction: Prerequisites and Preliminaries
Chapter 0. Sections 0.1 to 0.5 1 Chapter 0. Introduction: Prerequisites and Preliminaries Note. The content of Sections 0.1 through 0.6 should be very familiar to you. However, in order to keep these notes
More informationMATHEMATICS. IMPORTANT FORMULAE AND CONCEPTS for. Final Revision CLASS XII CHAPTER WISE CONCEPTS, FORMULAS FOR QUICK REVISION.
MATHEMATICS IMPORTANT FORMULAE AND CONCEPTS for Final Revision CLASS XII 2016 17 CHAPTER WISE CONCEPTS, FORMULAS FOR QUICK REVISION Prepared by M. S. KUMARSWAMY, TGT(MATHS) M. Sc. Gold Medallist (Elect.),
More informationRelations. Definition 1 Let A and B be sets. A binary relation R from A to B is any subset of A B.
Chapter 5 Relations Definition 1 Let A and B be sets. A binary relation R from A to B is any subset of A B. If A = B then a relation from A to B is called is called a relation on A. Examples A relation
More informationSets are one of the basic building blocks for the types of objects considered in discrete mathematics.
Section 2.1 Introduction Sets are one of the basic building blocks for the types of objects considered in discrete mathematics. Important for counting. Programming languages have set operations. Set theory
More informationCHAPTER I. Rings. Definition A ring R is a set with two binary operations, addition + and
CHAPTER I Rings 1.1 Definitions and Examples Definition 1.1.1. A ring R is a set with two binary operations, addition + and multiplication satisfying the following conditions for all a, b, c in R : (i)
More informationPrelims Linear Algebra I Michaelmas Term 2014
Prelims Linear Algebra I Michaelmas Term 2014 1 Systems of linear equations and matrices Let m,n be positive integers. An m n matrix is a rectangular array, with nm numbers, arranged in m rows and n columns.
More informationRelations. We have seen several types of abstract, mathematical objects, including propositions, predicates, sets, and ordered pairs and tuples.
Relations We have seen several types of abstract, mathematical objects, including propositions, predicates, sets, and ordered pairs and tuples. Relations use ordered tuples to represent relationships among
More informationBoolean Inner-Product Spaces and Boolean Matrices
Boolean Inner-Product Spaces and Boolean Matrices Stan Gudder Department of Mathematics, University of Denver, Denver CO 80208 Frédéric Latrémolière Department of Mathematics, University of Denver, Denver
More informationPartial cubes: structures, characterizations, and constructions
Partial cubes: structures, characterizations, and constructions Sergei Ovchinnikov San Francisco State University, Mathematics Department, 1600 Holloway Ave., San Francisco, CA 94132 Abstract Partial cubes
More informationOut-colourings of Digraphs
Out-colourings of Digraphs N. Alon J. Bang-Jensen S. Bessy July 13, 2017 Abstract We study vertex colourings of digraphs so that no out-neighbourhood is monochromatic and call such a colouring an out-colouring.
More informationLecture 3 Linear Algebra Background
Lecture 3 Linear Algebra Background Dan Sheldon September 17, 2012 Motivation Preview of next class: y (1) w 0 + w 1 x (1) 1 + w 2 x (1) 2 +... + w d x (1) d y (2) w 0 + w 1 x (2) 1 + w 2 x (2) 2 +...
More informationSet, functions and Euclidean space. Seungjin Han
Set, functions and Euclidean space Seungjin Han September, 2018 1 Some Basics LOGIC A is necessary for B : If B holds, then A holds. B A A B is the contraposition of B A. A is sufficient for B: If A holds,
More informationLinear Algebra M1 - FIB. Contents: 5. Matrices, systems of linear equations and determinants 6. Vector space 7. Linear maps 8.
Linear Algebra M1 - FIB Contents: 5 Matrices, systems of linear equations and determinants 6 Vector space 7 Linear maps 8 Diagonalization Anna de Mier Montserrat Maureso Dept Matemàtica Aplicada II Translation:
More informationDynamic Programming. Shuang Zhao. Microsoft Research Asia September 5, Dynamic Programming. Shuang Zhao. Outline. Introduction.
Microsoft Research Asia September 5, 2005 1 2 3 4 Section I What is? Definition is a technique for efficiently recurrence computing by storing partial results. In this slides, I will NOT use too many formal
More informationLecture 3: Matrix and Matrix Operations
Lecture 3: Matrix and Matrix Operations Representation, row vector, column vector, element of a matrix. Examples of matrix representations Tables and spreadsheets Scalar-Matrix operation: Scaling a matrix
More informationPart IA Numbers and Sets
Part IA Numbers and Sets Definitions Based on lectures by A. G. Thomason Notes taken by Dexter Chua Michaelmas 2014 These notes are not endorsed by the lecturers, and I have modified them (often significantly)
More information7 Matrix Operations. 7.0 Matrix Multiplication + 3 = 3 = 4
7 Matrix Operations Copyright 017, Gregory G. Smith 9 October 017 The product of two matrices is a sophisticated operations with a wide range of applications. In this chapter, we defined this binary operation,
More information1. To be a grandfather. Objects of our consideration are people; a person a is associated with a person b if a is a grandfather of b.
20 [161016-1020 ] 3.3 Binary relations In mathematics, as in everyday situations, we often speak about a relationship between objects, which means an idea of two objects being related or associated one
More informationWriting Assignment 2 Student Sample Questions
Writing Assignment 2 Student Sample Questions 1. Let P and Q be statements. Then the statement (P = Q) ( P Q) is a tautology. 2. The statement If the sun rises from the west, then I ll get out of the bed.
More informationMTH5112 Linear Algebra I MTH5212 Applied Linear Algebra (2017/2018)
MTH5112 Linear Algebra I MTH5212 Applied Linear Algebra (2017/2018) COURSEWORK 3 SOLUTIONS Exercise ( ) 1. (a) Write A = (a ij ) n n and B = (b ij ) n n. Since A and B are diagonal, we have a ij = 0 and
More informationLinear Equations in Linear Algebra
1 Linear Equations in Linear Algebra 1.7 LINEAR INDEPENDENCE LINEAR INDEPENDENCE Definition: An indexed set of vectors {v 1,, v p } in n is said to be linearly independent if the vector equation x x x
More informationRela%ons and Their Proper%es. Slides by A. Bloomfield
Rela%ons and Their Proper%es Slides by A. Bloomfield What is a rela%on Let A and B be sets. A binary rela%on R is a subset of A B Example Let A be the students in a the CS major A = {Alice, Bob, Claire,
More informationAlgebraic structures I
MTH5100 Assignment 1-10 Algebraic structures I For handing in on various dates January March 2011 1 FUNCTIONS. Say which of the following rules successfully define functions, giving reasons. For each one
More informationEighth Homework Solutions
Math 4124 Wednesday, April 20 Eighth Homework Solutions 1. Exercise 5.2.1(e). Determine the number of nonisomorphic abelian groups of order 2704. First we write 2704 as a product of prime powers, namely
More informationMATH1240 Definitions and Theorems
MATH1240 Definitions and Theorems 1 Fundamental Principles of Counting For an integer n 0, n factorial (denoted n!) is defined by 0! = 1, n! = (n)(n 1)(n 2) (3)(2)(1), for n 1. Given a collection of n
More informationChapter 6. Properties of Regular Languages
Chapter 6 Properties of Regular Languages Regular Sets and Languages Claim(1). The family of languages accepted by FSAs consists of precisely the regular sets over a given alphabet. Every regular set is
More informationChapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations
Chapter 1: Systems of linear equations and matrices Section 1.1: Introduction to systems of linear equations Definition: A linear equation in n variables can be expressed in the form a 1 x 1 + a 2 x 2
More informationSECTION A(1) k k 1= = or (rejected) k 1. Suggested Solutions Marks Remarks. 1. x + 1 is the longest side of the triangle. 1M + 1A
SECTION A(). x + is the longest side of the triangle. ( x + ) = x + ( x 7) (Pyth. theroem) x x + x + = x 6x + 8 ( x )( x ) + x x + 9 x = (rejected) or x = +. AP and PB are in the golden ratio and AP >
More informationWorksheet on Relations
Worksheet on Relations Recall the properties that relations can have: Definition. Let R be a relation on the set A. R is reflexive if for all a A we have ara. R is irreflexive or antireflexive if for all
More informationLecture Notes 1 Basic Concepts of Mathematics MATH 352
Lecture Notes 1 Basic Concepts of Mathematics MATH 352 Ivan Avramidi New Mexico Institute of Mining and Technology Socorro, NM 87801 June 3, 2004 Author: Ivan Avramidi; File: absmath.tex; Date: June 11,
More informationA FIRST COURSE IN LINEAR ALGEBRA. An Open Text by Ken Kuttler. Matrix Arithmetic
A FIRST COURSE IN LINEAR ALGEBRA An Open Text by Ken Kuttler Matrix Arithmetic Lecture Notes by Karen Seyffarth Adapted by LYRYX SERVICE COURSE SOLUTION Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)
More informationOn Generalized k-primary Rings
nternational Mathematical Forum, Vol. 7, 2012, no. 54, 2695-2704 On Generalized k-primary Rings Adil Kadir Jabbar and Chwas Abas Ahmed Department of Mathematics, School of Science Faculty of Science and
More informationLinear Algebra. Linear Equations and Matrices. Copyright 2005, W.R. Winfrey
Copyright 2005, W.R. Winfrey Topics Preliminaries Systems of Linear Equations Matrices Algebraic Properties of Matrix Operations Special Types of Matrices and Partitioned Matrices Matrix Transformations
More informationPROPOSITIONS AND LOGICAL OPERATIONS
1 PROPOSITIONS AND LOGICAL OPERATIONS INTRODUCTION What is logic? Logic is the discipline that deals with the methods of reasoning. Logic provides you rules & techniques for determining whether a given
More information