SlideShare a Scribd company logo
1 of 25
Download to read offline
MATH301- DISCRETE MATHEMATICS Copyright © Nahid Sultana 2014-2015. 
Dr. Nahid Sultana 
Email: nszakir@ud.edu.sa 
Chapter 2: Relations 
10/10/2014 
1
Topics 10/10/2014 
2 
 
Product sets 
 
Relations 
 
Inverse Relation, 
 
Representing Relations Using Matrices 
 
Composition of Relations 
 
Types of Relations 
 
Reflexive and Irreflexive Relations 
 
Symmetric and Antisymmetric Relations 
 
Transitive Relations 
 
Equivalence Relations 
 
Partial Ordering Relations 
 
Closure Properties Copyright © Nahid Sultana 2014-2015.
Product sets 
Definition: The ordered pair (x , y) is a single element consisting of pair of elements in which 
 
x is the first element (coordinate) 
 
y is the second element (coordinate). 
Definition: Two ordered pair (x , y) and (w , z) will be equal if 
x = w and y = z. 
Note: 
 
If {a, b} is a set, {a, b}= {b, a} 
 
If (a, b) is an ordered pair, then (a, b) ≠ (b, a) 10/10/2014 
3 
Copyright © Nahid Sultana 2014-2015.
Cartesian Product 10/10/2014 
4 
Definition: The Cartesian product of two sets A and B is the set of all ordered pairs (a, b) with 
Example: Let A = {x, y} and B = {1, 2}. Compute . Copyright © Nahid Sultana 2014-2015.
Relations 10/10/2014 
5 
Definition: A Relation R from set A to set B is a subset of A × B. 
 
If (a , b) ∈ R, we say that “a is related to b", and write aRb. 
 
If (a , b) ∉ R, we say that “a is not related to b“, and write aRb. 
 
If A = B, we often say that R ∈ A × A is a relation on A. 
Example: A = (1, 2, 3) and B = {x, y, z}, and let R = {(1, y), (1, z), (3, y)}. Then R is a relation from A to B ? . 
Yes--since R is a subset of A × B 
With respect to this relation, Copyright © Nahid Sultana 2014-2015.
Relations 
Solution: Note that these relations are on an infinite set and each of these relations is an infinite set. Checking the conditions that define each relation, we see that 
(1,1) is in R1, R3, R4 , and R6: 
(1,2) is in R1 and R6: 
(2,1) is in R2, R5, and R6: 
(1, −1) is in R2, R3, and R6 : 
(2,2) is in R1, R3, and R4. 
Example: Consider these relations on the set of integers: 
R1 = {(a,b) | a ≤ b}, R4 = {(a,b) | a = b}, 
R2 = {(a,b) | a > b}, R5 = {(a,b) | a = b + 1}, 
R3 = {(a,b) | a = b or a = −b}, R6 = {(a,b) | a + b ≤ 3}. 
Which of these relations contain each of the pairs 
(1,1), (1, 2), (2, 1), (1, −1), and (2, 2)? 10/10/2014 
6 
Copyright © Nahid Sultana 2014-2015.
Relations (Cont…) 10/10/2014 
7 
Definition: The domain of relation R is the set of all first elements of the ordered pairs which belong to R, denoted by Dom(R). 
Definition: The range is the set of second elements of the ordered pairs which belong to R, denoted by Ran(R). 
Example: A = (1, 2, 3) and B = {x, y, z}, and consider the relation 
R = {(1, y), (1, z), (3, y)}. 
Find the domain and range of R. 
The domain of R is Dom(R) = {1, 3} 
The range of R is Ran(R) = {y, z} Copyright © Nahid Sultana 2014-2015.
Inverse Relations 10/10/2014 
8 
Definition: Let R be any relation from set A to B. The inverse of R, denoted by R-1, is the relation from B to A denoted by R-1 = {(b , a)|(a , b)∈ R} 
Example: let A = {1, 2, 3} and B = {x, y, z}. Find the inverse of 
R = {(1, y), (1 , z), (3 , y)} 
Solution: R−1 = {(y , 1), (z , 1), (y , 3)} 
 
If R is any relation, then (R-1)-1 = R. 
 
The domain and range of R-1 are equal to the range and domain of R, respectively. 
 
If R is a relation on A, then R-1 is also a relation on A. Copyright © Nahid Sultana 2014-2015.
Representing Relations Using Matrices 
 
A relation between finite sets can be represented using a zero-one matrix. 
 
Suppose R is a relation from A = {a1, a2, …, am} to B = {b1, b2, …, bn}. 
 
The elements of the two sets can be listed in any particular arbitrary order. When A = B, we use the same ordering. 
 
The relation R is represented by the matrix 
MR = [mij], where 
 
The matrix representing R has a 1 as its (i,j) entry when ai is related to bj and a 0 if ai is not related to bj. 10/10/2014 
9 
Copyright © Nahid Sultana 2014-2015.
Examples of Representing Relations Using Matrices 
Example 1: Suppose that A = {1,2,3} and B = {1,2}. Let R be the relation from A to B containing (a,b) if a ∈ A, b ∈ B, and a > b. What is the matrix representing R (assuming the ordering of elements is the same as the increasing numerical order)? 
Solution: Here R = {(2,1), (3,1),(3,2)}. The matrix representing R is 10/10/2014 
10 
Copyright © Nahid Sultana 2014-2015.
Examples of Representing Relations Using Matrices (cont.) 
Example 2: Let A = {a1,a2, a3} and B = {b1,b2, b3,b4, b5}. Which ordered pairs are in the relation R represented by the matrix 
Solution: R = {(a1, b2), (a2, b1),(a2, b3), (a2, b4),(a3, b1), {(a3, b3), (a3, b5)}. 10/10/2014 
11 
Copyright © Nahid Sultana 2014-2015.
Composition of Relations 10/10/2014 
12 
Definition: Suppose A, B and C are sets, and 
 
R is a relation from A to B 
 
S is a relation from B to C 
 
Then the composition of R and S, denoted by R ∘ S, is a relation from A to C defined by R ∘ S = {(a , c)| ∃ b ∈ B, for which (a , b) ∈ R and (b , c) ∈ S} 
Example: Let A = {1, 2, 3, 4}, B = {a, b, c, d}, C = {x, y, z} and let R = {(1, a), (2, d), (3, a), (3, b), (3, d)} and S = {(b, x), (b, z), (c, y), (d, z)} Compute R ∘ S . 
Using arrow diagram, R ◦ S={(2,z), (3,x), (3,z)} Copyright © Nahid Sultana 2014-2015.
Composition of Relations (Cont…) 10/10/2014 
13 
Example: Let A = {1, 2, 3, 4}, B = {a, b, c, d}, C = {x, y, z} and let R = {(1, a), (2, d), (3, a), (3, b), (3, d)} and S = {(b, x), (b, z), (c, y), (d, z)}. Compute R ∘ S . 
There is another way of finding R ◦ S. 
Let MR and MS denote the matrix representations of the relations R and S, respectively. Then 
The nonzero entries in this matrix tell us which elements are related by R◦S. Thus R ◦ S={(2,z), (3,x), (3,z)} 
Multiplying MR and MS Copyright © Nahid Sultana 2014-2015.
Types of relations 10/10/2014 
14 
Types of relations which are defined on a set A. 
 
Reflexive and Irreflexive Relations 
 
Symmetric and Antisymmetric Relations 
 
Transitive Relations 
Definition: A relation R on a set A is reflexive if (a,a) ∈ R for all a ∈ A. 
Thus R is not reflexive if there exists a ∈ A such that (a, a)∉ R. 
Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1)(1, 2), (2, 1), (2, 2), (3, 3)} 
R2 = {(1, 1), (1, 2), (2, 1), (2, 2)} 
R3 = Φ 
Determine which relation is reflexive. Copyright © Nahid Sultana 2014-2015.
Types of relations (Cont…) 10/10/2014 
15 
Example: The following relations on the integers are reflexive: 
R1 = {(a,b) | a ≤ b}, 
R3 = {(a,b) | a = b or a = −b}, 
R4 = {(a,b) | a = b}. 
The following relations are not reflexive: 
R2 = {(a,b) | a > b} (note that 3 ≯ 3), 
R5 = {(a,b) | a = b + 1} (note that 3 ≠3 + 1), 
R6 = {(a,b) | a + b ≤ 3} (note that 4 + 4 ≰ 3). Copyright © Nahid Sultana 2014-2015.
Types of relations (Cont…) 10/10/2014 
16 
Definition: A relation R on a set A is symmetric if whenever aRb then bRa, i.e., if whenever (a, b) ∈ R then (b, a) ∈ R. Thus R is not symmetric if there exists a, b ∈ A such that (a, b) ∈ R but (b, a) ∉ R. 
Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1)(1, 2), (2, 1), (2, 2), (3, 3)} R2 = {(1, 1), (1, 2), (2, 2)} Determine which relation is symmetric. Copyright © Nahid Sultana 2014-2015.
Types of relations (Cont…) 10/10/2014 
17 
Definition: A relation R on a set A is antisymmetric if whenever aRb and bRa then a = b. 
Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1)(1, 2), (2, 1), (2, 2), (3, 3)} 
R2 = {(1, 1), (1, 2)} 
Determine which relation is antisymmetric. 
The contrapositive of this definition is that R is antisymmetric if whenever a ≠ b, then either (a,b) ∉ R or (b,a) ∉ R. 
Definition: A relation R is not antisymmetric if there exist a, b ∈ A such that (a,b)∈ R and (b, a) ∈ R but a ≠ b. 
Note: Not symmetric ≠ antisymmetric . Copyright © Nahid Sultana 2014-2015.
Types of relations (Cont…) 10/10/2014 
18 
Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1), (1, 2), (2, 3), (1, 3)} 
R2 = {(1, 1), (1, 2), (2,2), (2,3)} 
R3 = {(1, 1), (1, 2), (1,3), (3,3)} 
Determine which relation is transitive. 
Definition: A relation R on a set A is transitive if whenever aRb and bRc then aRc, that is, if whenever (a, b)∈R and (b, c)∈ R then (a, c)∈R. 
Thus R is not transitive if there exist a, b, c ∈ R such that 
(a,b)∈ R and (b, c) ∈ R but (a,c) ∉ R. 
If such a, b and c not exist, then R is transitive. Copyright © Nahid Sultana 2014-2015.
Equivalence relation 10/10/2014 
19 
Example: Consider the following relation on the set A = {1, 2, 3,4}: R = {(1, 1), (1, 2), (2,1), (2,2), (3,4), (4,3), (3,3), (4, 4)} 
Determine whether this relation is equivalence or not. 
Definition: A relation R on a set A is called an equivalence relation if R is reflexive, symmetric, and transitive. 
 
It follows three properties: 
1) 
For every a ∈ A, aRa. 
2) 
If aRb then bRa. 
3) 
If aRb and bRc, then aRc. 
The relation R is equivalence because R is reflexive, symmetric and transitive. Copyright © Nahid Sultana 2014-2015.
Equivalence relation (cont…) 10/10/2014 
20 
Example: Let A= ℤ, set of integers. Let R be defined by aRb 
iff a ≤ b. Determine whether this relation is equivalence or not. 
Therefore the relation R is not an equivalence. 
Solution: 
1) 
The relation R is reflexive a ≤ a. 
2) 
The relation R is not symmetric a ≤ b does not imply that b ≤ a . 
3) 
The relation R is transitive because a ≤ b and b ≤ c imply that a ≤ c. Copyright © Nahid Sultana 2014-2015.
Equivalence relation (cont…) 10/10/2014 
21 
Example: Prove that congruence modulo n is an equivalence relation on ℤ. 
Definition: For a given positive integer n ≥ 2, two integers a and b are called congruent modulo n, written as 
a ≡ b(mod n) 
if a - b is divisible by n. 
Solution: 
1) 
Reflexivity: For any a ∈ ℤ, we have a ≡ a(mod n) because a- a=0 is divisible by n. Hence the relation is reflexive. Cont to next slide….. Copyright © Nahid Sultana 2014-2015.
Equivalence relation (cont…) 10/10/2014 
22 
2) 
Symmetry: suppose a ≡ b(mod n) ⇒ a-b is divisible by n ⇒ (a-b)/n = k , for some k ∈ℤ ⇒ a-b = nk . Therefore, b-a = -(a-b) = -nk = n(-k) ⇒(b-a)/n = -k, so b-a is divisible by n as -k∈ℤ i.e. b ≡ a(mod n). Thus the relation is symmetric. 
3) Transitivity: suppose a ≡ b (mod n) and b ≡ c (mod n), then (a-b)/n =k and (b-c)/n=l for some k,l∈ℤ. i.e. a-b=nk and b-c=nl. By adding this two equations we get, a-c=n(k+l)⇒(a-c)/n=k+l. So a-c is divisible by n as k+l ∈ℤ, i.e. a ≡ c(mod n). Thus the relation is transitive. 
Hence this is an equivalence relation on ℤ. Copyright © Nahid Sultana 2014-2015. 
Solution: Cont…..
Equivalence class (cont…) 10/10/2014 
23 
Definition: For an equivalence relation R defined on A and for a∈ A, the set [a] = {x ∈ A| (a, x) ∈R} is called the equivalence class of a in A. 
Definition: Any b ∈ [a] is called a representative of this equivalence class. 
Definition: The collection of all equivalence classes of elements of A under an equivalence relation R is called the quotient set, denoted by A/R, i.e. 
A/R = {[a] | a ∈ A}. 
Note: The quotient set A/R is a partition of A. Copyright © Nahid Sultana 2014-2015.
Partial Orderings 10/10/2014 
24 
Definition : A relation R on a set S is called a partial ordering, or partial order, if it is reflexive, antisymmetric, and transitive. 
Definition: A set A together with a partial ordering R is called a partially ordered set or poset. 
Example: Show that the “greater than or equal” relation (≥) is a partial ordering on the set of integers. 
Solution: 
Reflexivity: a ≥ a for every integer a. 
Antisymmetry: If a ≥ b and b ≥ a , then a = b. 
Transitivity: If a ≥ b and b ≥ c , then a ≥ c. Copyright © Nahid Sultana 2014-2015.
Closure Properties 10/10/2014 
25 
 
Suppose R is a relation on A 
 
If R does not possess a particular relation (reflexive, symmetric, transitive) 
 
Then we may add as few new pairs as possible until we get a new relation R1 on A that have that required property. 
 
If such R1 exists, we call it the closure of R with respect to that property. 
 
Example: Reflexive closure, Symmetric closure, Transitive closure. Copyright © Nahid Sultana 2014-2015.

More Related Content

What's hot

Predicates and Quantifiers
Predicates and QuantifiersPredicates and Quantifiers
Predicates and Quantifiersblaircomp2003
 
Modular arithmetic
Modular arithmeticModular arithmetic
Modular arithmeticsangeetha s
 
CMSC 56 | Lecture 13: Relations and their Properties
CMSC 56 | Lecture 13: Relations and their PropertiesCMSC 56 | Lecture 13: Relations and their Properties
CMSC 56 | Lecture 13: Relations and their Propertiesallyn joy calcaben
 
Function and Its Types.
Function and Its Types.Function and Its Types.
Function and Its Types.Awais Bakshy
 
Discrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكرو
Discrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكروDiscrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكرو
Discrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكروDr. Khaled Bakro
 
Principle of mathematical induction
Principle of mathematical inductionPrinciple of mathematical induction
Principle of mathematical inductionKriti Varshney
 
CMSC 56 | Lecture 6: Sets & Set Operations
CMSC 56 | Lecture 6: Sets & Set OperationsCMSC 56 | Lecture 6: Sets & Set Operations
CMSC 56 | Lecture 6: Sets & Set Operationsallyn joy calcaben
 
recurrence relations
 recurrence relations recurrence relations
recurrence relationsAnurag Cheela
 
Relation and function
Relation and functionRelation and function
Relation and functionAadityaGera
 
Mathematical Logic - Part 1
Mathematical Logic - Part 1Mathematical Logic - Part 1
Mathematical Logic - Part 1blaircomp2003
 
Number theory
Number theory Number theory
Number theory tes31
 
Permutation and combination
Permutation and combinationPermutation and combination
Permutation and combinationSadia Zareen
 
Group abstract algebra
Group  abstract algebraGroup  abstract algebra
Group abstract algebraNaliniSPatil
 

What's hot (20)

Predicates and Quantifiers
Predicates and QuantifiersPredicates and Quantifiers
Predicates and Quantifiers
 
Set in discrete mathematics
Set in discrete mathematicsSet in discrete mathematics
Set in discrete mathematics
 
Modular arithmetic
Modular arithmeticModular arithmetic
Modular arithmetic
 
Sets, functions and groups
Sets, functions and groupsSets, functions and groups
Sets, functions and groups
 
Propositional logic
Propositional logicPropositional logic
Propositional logic
 
CMSC 56 | Lecture 13: Relations and their Properties
CMSC 56 | Lecture 13: Relations and their PropertiesCMSC 56 | Lecture 13: Relations and their Properties
CMSC 56 | Lecture 13: Relations and their Properties
 
Function and Its Types.
Function and Its Types.Function and Its Types.
Function and Its Types.
 
Discrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكرو
Discrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكروDiscrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكرو
Discrete mathematics Ch1 sets Theory_Dr.Khaled.Bakro د. خالد بكرو
 
Ring
RingRing
Ring
 
Principle of mathematical induction
Principle of mathematical inductionPrinciple of mathematical induction
Principle of mathematical induction
 
Number theory
Number theoryNumber theory
Number theory
 
CMSC 56 | Lecture 6: Sets & Set Operations
CMSC 56 | Lecture 6: Sets & Set OperationsCMSC 56 | Lecture 6: Sets & Set Operations
CMSC 56 | Lecture 6: Sets & Set Operations
 
recurrence relations
 recurrence relations recurrence relations
recurrence relations
 
Hasse diagram
Hasse diagramHasse diagram
Hasse diagram
 
Relation and function
Relation and functionRelation and function
Relation and function
 
Mathematical Logic - Part 1
Mathematical Logic - Part 1Mathematical Logic - Part 1
Mathematical Logic - Part 1
 
Number theory
Number theory Number theory
Number theory
 
Algebraic structures
Algebraic structuresAlgebraic structures
Algebraic structures
 
Permutation and combination
Permutation and combinationPermutation and combination
Permutation and combination
 
Group abstract algebra
Group  abstract algebraGroup  abstract algebra
Group abstract algebra
 

Similar to Chapter 2: Relations

dm_13_RelationsAndTheirProperties (1).pdf
dm_13_RelationsAndTheirProperties (1).pdfdm_13_RelationsAndTheirProperties (1).pdf
dm_13_RelationsAndTheirProperties (1).pdfSanjanaAdri
 
dm_13_RelationsAndTheirProperties (1).pptx
dm_13_RelationsAndTheirProperties (1).pptxdm_13_RelationsAndTheirProperties (1).pptx
dm_13_RelationsAndTheirProperties (1).pptxRockyIslam5
 
Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Himanshu Dua
 
Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Himanshu Dua
 
Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Himanshu Dua
 
Discrete-Chapter 08 Relations
Discrete-Chapter 08 RelationsDiscrete-Chapter 08 Relations
Discrete-Chapter 08 RelationsWongyos Keardsri
 
Relation in Discrete Mathematics
Relation in Discrete Mathematics Relation in Discrete Mathematics
Relation in Discrete Mathematics NANDINI SHARMA
 
Relations between two sets
Relations between two setsRelations between two sets
Relations between two setsYassirdino
 
Introduction to The Relations in Mathematics.pptx
Introduction to The Relations in Mathematics.pptxIntroduction to The Relations in Mathematics.pptx
Introduction to The Relations in Mathematics.pptxJadhavShaileshShashi
 
Ncert class-12-mathematics-part-1
Ncert class-12-mathematics-part-1Ncert class-12-mathematics-part-1
Ncert class-12-mathematics-part-1RAHUL SINGH
 
Week 5 ( basic concept of relation )
Week 5 ( basic concept of relation )Week 5 ( basic concept of relation )
Week 5 ( basic concept of relation )OliverBaltazar2
 

Similar to Chapter 2: Relations (20)

Relations
RelationsRelations
Relations
 
Relations
RelationsRelations
Relations
 
dm_13_RelationsAndTheirProperties (1).pdf
dm_13_RelationsAndTheirProperties (1).pdfdm_13_RelationsAndTheirProperties (1).pdf
dm_13_RelationsAndTheirProperties (1).pdf
 
dm_13_RelationsAndTheirProperties (1).pptx
dm_13_RelationsAndTheirProperties (1).pptxdm_13_RelationsAndTheirProperties (1).pptx
dm_13_RelationsAndTheirProperties (1).pptx
 
Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Final relation1 m_tech(cse)
Final relation1 m_tech(cse)
 
Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Final relation1 m_tech(cse)
Final relation1 m_tech(cse)
 
Final relation1 m_tech(cse)
Final relation1 m_tech(cse)Final relation1 m_tech(cse)
Final relation1 m_tech(cse)
 
Per5 relasi
Per5 relasiPer5 relasi
Per5 relasi
 
Discrete-Chapter 08 Relations
Discrete-Chapter 08 RelationsDiscrete-Chapter 08 Relations
Discrete-Chapter 08 Relations
 
Relation in Discrete Mathematics
Relation in Discrete Mathematics Relation in Discrete Mathematics
Relation in Discrete Mathematics
 
Properties of relations
Properties of relationsProperties of relations
Properties of relations
 
Relations between two sets
Relations between two setsRelations between two sets
Relations between two sets
 
Relation and function_xii
Relation and function_xiiRelation and function_xii
Relation and function_xii
 
Relations
RelationsRelations
Relations
 
Introduction to The Relations in Mathematics.pptx
Introduction to The Relations in Mathematics.pptxIntroduction to The Relations in Mathematics.pptx
Introduction to The Relations in Mathematics.pptx
 
Lemh101
Lemh101Lemh101
Lemh101
 
Introductions to Relations
Introductions to RelationsIntroductions to Relations
Introductions to Relations
 
Ncert class-12-mathematics-part-1
Ncert class-12-mathematics-part-1Ncert class-12-mathematics-part-1
Ncert class-12-mathematics-part-1
 
Relations
RelationsRelations
Relations
 
Week 5 ( basic concept of relation )
Week 5 ( basic concept of relation )Week 5 ( basic concept of relation )
Week 5 ( basic concept of relation )
 

More from nszakir

Chapter-4: More on Direct Proof and Proof by Contrapositive
Chapter-4: More on Direct Proof and Proof by ContrapositiveChapter-4: More on Direct Proof and Proof by Contrapositive
Chapter-4: More on Direct Proof and Proof by Contrapositivenszakir
 
Chapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVE
Chapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVEChapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVE
Chapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVEnszakir
 
Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...
Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...
Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...nszakir
 
Chapter 6 part2-Introduction to Inference-Tests of Significance, Stating Hyp...
Chapter 6 part2-Introduction to Inference-Tests of Significance,  Stating Hyp...Chapter 6 part2-Introduction to Inference-Tests of Significance,  Stating Hyp...
Chapter 6 part2-Introduction to Inference-Tests of Significance, Stating Hyp...nszakir
 
Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...
Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...
Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...nszakir
 
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...nszakir
 
Chapter 5 part1- The Sampling Distribution of a Sample Mean
Chapter 5 part1- The Sampling Distribution of a Sample MeanChapter 5 part1- The Sampling Distribution of a Sample Mean
Chapter 5 part1- The Sampling Distribution of a Sample Meannszakir
 
Chapter 4 part4- General Probability Rules
Chapter 4 part4- General Probability RulesChapter 4 part4- General Probability Rules
Chapter 4 part4- General Probability Rulesnszakir
 
Chapter 4 part3- Means and Variances of Random Variables
Chapter 4 part3- Means and Variances of Random VariablesChapter 4 part3- Means and Variances of Random Variables
Chapter 4 part3- Means and Variances of Random Variablesnszakir
 
Chapter 4 part2- Random Variables
Chapter 4 part2- Random VariablesChapter 4 part2- Random Variables
Chapter 4 part2- Random Variablesnszakir
 
Chapter 4 part1-Probability Model
Chapter 4 part1-Probability ModelChapter 4 part1-Probability Model
Chapter 4 part1-Probability Modelnszakir
 
Chapter 3 part3-Toward Statistical Inference
Chapter 3 part3-Toward Statistical InferenceChapter 3 part3-Toward Statistical Inference
Chapter 3 part3-Toward Statistical Inferencenszakir
 
Chapter 3 part2- Sampling Design
Chapter 3 part2- Sampling DesignChapter 3 part2- Sampling Design
Chapter 3 part2- Sampling Designnszakir
 
Chapter 3 part1-Design of Experiments
Chapter 3 part1-Design of ExperimentsChapter 3 part1-Design of Experiments
Chapter 3 part1-Design of Experimentsnszakir
 
Chapter 2 part2-Correlation
Chapter 2 part2-CorrelationChapter 2 part2-Correlation
Chapter 2 part2-Correlationnszakir
 
Chapter 2 part1-Scatterplots
Chapter 2 part1-ScatterplotsChapter 2 part1-Scatterplots
Chapter 2 part1-Scatterplotsnszakir
 
Chapter 2 part3-Least-Squares Regression
Chapter 2 part3-Least-Squares RegressionChapter 2 part3-Least-Squares Regression
Chapter 2 part3-Least-Squares Regressionnszakir
 
Density Curves and Normal Distributions
Density Curves and Normal DistributionsDensity Curves and Normal Distributions
Density Curves and Normal Distributionsnszakir
 
Describing Distributions with Numbers
Describing Distributions with NumbersDescribing Distributions with Numbers
Describing Distributions with Numbersnszakir
 
Displaying Distributions with Graphs
Displaying Distributions with GraphsDisplaying Distributions with Graphs
Displaying Distributions with Graphsnszakir
 

More from nszakir (20)

Chapter-4: More on Direct Proof and Proof by Contrapositive
Chapter-4: More on Direct Proof and Proof by ContrapositiveChapter-4: More on Direct Proof and Proof by Contrapositive
Chapter-4: More on Direct Proof and Proof by Contrapositive
 
Chapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVE
Chapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVEChapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVE
Chapter-3: DIRECT PROOF AND PROOF BY CONTRAPOSITIVE
 
Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...
Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...
Chapter 7 : Inference for Distributions(The t Distributions, One-Sample t Con...
 
Chapter 6 part2-Introduction to Inference-Tests of Significance, Stating Hyp...
Chapter 6 part2-Introduction to Inference-Tests of Significance,  Stating Hyp...Chapter 6 part2-Introduction to Inference-Tests of Significance,  Stating Hyp...
Chapter 6 part2-Introduction to Inference-Tests of Significance, Stating Hyp...
 
Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...
Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...
Chapter 6 part1- Introduction to Inference-Estimating with Confidence (Introd...
 
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
 
Chapter 5 part1- The Sampling Distribution of a Sample Mean
Chapter 5 part1- The Sampling Distribution of a Sample MeanChapter 5 part1- The Sampling Distribution of a Sample Mean
Chapter 5 part1- The Sampling Distribution of a Sample Mean
 
Chapter 4 part4- General Probability Rules
Chapter 4 part4- General Probability RulesChapter 4 part4- General Probability Rules
Chapter 4 part4- General Probability Rules
 
Chapter 4 part3- Means and Variances of Random Variables
Chapter 4 part3- Means and Variances of Random VariablesChapter 4 part3- Means and Variances of Random Variables
Chapter 4 part3- Means and Variances of Random Variables
 
Chapter 4 part2- Random Variables
Chapter 4 part2- Random VariablesChapter 4 part2- Random Variables
Chapter 4 part2- Random Variables
 
Chapter 4 part1-Probability Model
Chapter 4 part1-Probability ModelChapter 4 part1-Probability Model
Chapter 4 part1-Probability Model
 
Chapter 3 part3-Toward Statistical Inference
Chapter 3 part3-Toward Statistical InferenceChapter 3 part3-Toward Statistical Inference
Chapter 3 part3-Toward Statistical Inference
 
Chapter 3 part2- Sampling Design
Chapter 3 part2- Sampling DesignChapter 3 part2- Sampling Design
Chapter 3 part2- Sampling Design
 
Chapter 3 part1-Design of Experiments
Chapter 3 part1-Design of ExperimentsChapter 3 part1-Design of Experiments
Chapter 3 part1-Design of Experiments
 
Chapter 2 part2-Correlation
Chapter 2 part2-CorrelationChapter 2 part2-Correlation
Chapter 2 part2-Correlation
 
Chapter 2 part1-Scatterplots
Chapter 2 part1-ScatterplotsChapter 2 part1-Scatterplots
Chapter 2 part1-Scatterplots
 
Chapter 2 part3-Least-Squares Regression
Chapter 2 part3-Least-Squares RegressionChapter 2 part3-Least-Squares Regression
Chapter 2 part3-Least-Squares Regression
 
Density Curves and Normal Distributions
Density Curves and Normal DistributionsDensity Curves and Normal Distributions
Density Curves and Normal Distributions
 
Describing Distributions with Numbers
Describing Distributions with NumbersDescribing Distributions with Numbers
Describing Distributions with Numbers
 
Displaying Distributions with Graphs
Displaying Distributions with GraphsDisplaying Distributions with Graphs
Displaying Distributions with Graphs
 

Recently uploaded

Orientation, design and principles of polyhouse
Orientation, design and principles of polyhouseOrientation, design and principles of polyhouse
Orientation, design and principles of polyhousejana861314
 
GFP in rDNA Technology (Biotechnology).pptx
GFP in rDNA Technology (Biotechnology).pptxGFP in rDNA Technology (Biotechnology).pptx
GFP in rDNA Technology (Biotechnology).pptxAleenaTreesaSaji
 
Luciferase in rDNA technology (biotechnology).pptx
Luciferase in rDNA technology (biotechnology).pptxLuciferase in rDNA technology (biotechnology).pptx
Luciferase in rDNA technology (biotechnology).pptxAleenaTreesaSaji
 
Bentham & Hooker's Classification. along with the merits and demerits of the ...
Bentham & Hooker's Classification. along with the merits and demerits of the ...Bentham & Hooker's Classification. along with the merits and demerits of the ...
Bentham & Hooker's Classification. along with the merits and demerits of the ...Nistarini College, Purulia (W.B) India
 
Work, Energy and Power for class 10 ICSE Physics
Work, Energy and Power for class 10 ICSE PhysicsWork, Energy and Power for class 10 ICSE Physics
Work, Energy and Power for class 10 ICSE Physicsvishikhakeshava1
 
G9 Science Q4- Week 1-2 Projectile Motion.ppt
G9 Science Q4- Week 1-2 Projectile Motion.pptG9 Science Q4- Week 1-2 Projectile Motion.ppt
G9 Science Q4- Week 1-2 Projectile Motion.pptMAESTRELLAMesa2
 
Cultivation of KODO MILLET . made by Ghanshyam pptx
Cultivation of KODO MILLET . made by Ghanshyam pptxCultivation of KODO MILLET . made by Ghanshyam pptx
Cultivation of KODO MILLET . made by Ghanshyam pptxpradhanghanshyam7136
 
A relative description on Sonoporation.pdf
A relative description on Sonoporation.pdfA relative description on Sonoporation.pdf
A relative description on Sonoporation.pdfnehabiju2046
 
Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )aarthirajkumar25
 
Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...
Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...
Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...jana861314
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Patrick Diehl
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​  ​Nanoparticles synthesis and characterization​  ​
Nanoparticles synthesis and characterization​ ​kaibalyasahoo82800
 
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝soniya singh
 
Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...anilsa9823
 
VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PPRINCE C P
 
Natural Polymer Based Nanomaterials
Natural Polymer Based NanomaterialsNatural Polymer Based Nanomaterials
Natural Polymer Based NanomaterialsAArockiyaNisha
 
Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |aasikanpl
 
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxSOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxkessiyaTpeter
 

Recently uploaded (20)

Orientation, design and principles of polyhouse
Orientation, design and principles of polyhouseOrientation, design and principles of polyhouse
Orientation, design and principles of polyhouse
 
GFP in rDNA Technology (Biotechnology).pptx
GFP in rDNA Technology (Biotechnology).pptxGFP in rDNA Technology (Biotechnology).pptx
GFP in rDNA Technology (Biotechnology).pptx
 
Luciferase in rDNA technology (biotechnology).pptx
Luciferase in rDNA technology (biotechnology).pptxLuciferase in rDNA technology (biotechnology).pptx
Luciferase in rDNA technology (biotechnology).pptx
 
Bentham & Hooker's Classification. along with the merits and demerits of the ...
Bentham & Hooker's Classification. along with the merits and demerits of the ...Bentham & Hooker's Classification. along with the merits and demerits of the ...
Bentham & Hooker's Classification. along with the merits and demerits of the ...
 
Work, Energy and Power for class 10 ICSE Physics
Work, Energy and Power for class 10 ICSE PhysicsWork, Energy and Power for class 10 ICSE Physics
Work, Energy and Power for class 10 ICSE Physics
 
G9 Science Q4- Week 1-2 Projectile Motion.ppt
G9 Science Q4- Week 1-2 Projectile Motion.pptG9 Science Q4- Week 1-2 Projectile Motion.ppt
G9 Science Q4- Week 1-2 Projectile Motion.ppt
 
The Philosophy of Science
The Philosophy of ScienceThe Philosophy of Science
The Philosophy of Science
 
Cultivation of KODO MILLET . made by Ghanshyam pptx
Cultivation of KODO MILLET . made by Ghanshyam pptxCultivation of KODO MILLET . made by Ghanshyam pptx
Cultivation of KODO MILLET . made by Ghanshyam pptx
 
A relative description on Sonoporation.pdf
A relative description on Sonoporation.pdfA relative description on Sonoporation.pdf
A relative description on Sonoporation.pdf
 
Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )
 
Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...
Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...
Traditional Agroforestry System in India- Shifting Cultivation, Taungya, Home...
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​  ​Nanoparticles synthesis and characterization​  ​
Nanoparticles synthesis and characterization​ ​
 
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
 
Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
Lucknow 💋 Russian Call Girls Lucknow Finest Escorts Service 8923113531 Availa...
 
VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C P
 
9953056974 Young Call Girls In Mahavir enclave Indian Quality Escort service
9953056974 Young Call Girls In Mahavir enclave Indian Quality Escort service9953056974 Young Call Girls In Mahavir enclave Indian Quality Escort service
9953056974 Young Call Girls In Mahavir enclave Indian Quality Escort service
 
Natural Polymer Based Nanomaterials
Natural Polymer Based NanomaterialsNatural Polymer Based Nanomaterials
Natural Polymer Based Nanomaterials
 
Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Mukherjee Nagar(Delhi) |
 
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxSOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
 

Chapter 2: Relations

  • 1. MATH301- DISCRETE MATHEMATICS Copyright © Nahid Sultana 2014-2015. Dr. Nahid Sultana Email: nszakir@ud.edu.sa Chapter 2: Relations 10/10/2014 1
  • 2. Topics 10/10/2014 2  Product sets  Relations  Inverse Relation,  Representing Relations Using Matrices  Composition of Relations  Types of Relations  Reflexive and Irreflexive Relations  Symmetric and Antisymmetric Relations  Transitive Relations  Equivalence Relations  Partial Ordering Relations  Closure Properties Copyright © Nahid Sultana 2014-2015.
  • 3. Product sets Definition: The ordered pair (x , y) is a single element consisting of pair of elements in which  x is the first element (coordinate)  y is the second element (coordinate). Definition: Two ordered pair (x , y) and (w , z) will be equal if x = w and y = z. Note:  If {a, b} is a set, {a, b}= {b, a}  If (a, b) is an ordered pair, then (a, b) ≠ (b, a) 10/10/2014 3 Copyright © Nahid Sultana 2014-2015.
  • 4. Cartesian Product 10/10/2014 4 Definition: The Cartesian product of two sets A and B is the set of all ordered pairs (a, b) with Example: Let A = {x, y} and B = {1, 2}. Compute . Copyright © Nahid Sultana 2014-2015.
  • 5. Relations 10/10/2014 5 Definition: A Relation R from set A to set B is a subset of A × B.  If (a , b) ∈ R, we say that “a is related to b", and write aRb.  If (a , b) ∉ R, we say that “a is not related to b“, and write aRb.  If A = B, we often say that R ∈ A × A is a relation on A. Example: A = (1, 2, 3) and B = {x, y, z}, and let R = {(1, y), (1, z), (3, y)}. Then R is a relation from A to B ? . Yes--since R is a subset of A × B With respect to this relation, Copyright © Nahid Sultana 2014-2015.
  • 6. Relations Solution: Note that these relations are on an infinite set and each of these relations is an infinite set. Checking the conditions that define each relation, we see that (1,1) is in R1, R3, R4 , and R6: (1,2) is in R1 and R6: (2,1) is in R2, R5, and R6: (1, −1) is in R2, R3, and R6 : (2,2) is in R1, R3, and R4. Example: Consider these relations on the set of integers: R1 = {(a,b) | a ≤ b}, R4 = {(a,b) | a = b}, R2 = {(a,b) | a > b}, R5 = {(a,b) | a = b + 1}, R3 = {(a,b) | a = b or a = −b}, R6 = {(a,b) | a + b ≤ 3}. Which of these relations contain each of the pairs (1,1), (1, 2), (2, 1), (1, −1), and (2, 2)? 10/10/2014 6 Copyright © Nahid Sultana 2014-2015.
  • 7. Relations (Cont…) 10/10/2014 7 Definition: The domain of relation R is the set of all first elements of the ordered pairs which belong to R, denoted by Dom(R). Definition: The range is the set of second elements of the ordered pairs which belong to R, denoted by Ran(R). Example: A = (1, 2, 3) and B = {x, y, z}, and consider the relation R = {(1, y), (1, z), (3, y)}. Find the domain and range of R. The domain of R is Dom(R) = {1, 3} The range of R is Ran(R) = {y, z} Copyright © Nahid Sultana 2014-2015.
  • 8. Inverse Relations 10/10/2014 8 Definition: Let R be any relation from set A to B. The inverse of R, denoted by R-1, is the relation from B to A denoted by R-1 = {(b , a)|(a , b)∈ R} Example: let A = {1, 2, 3} and B = {x, y, z}. Find the inverse of R = {(1, y), (1 , z), (3 , y)} Solution: R−1 = {(y , 1), (z , 1), (y , 3)}  If R is any relation, then (R-1)-1 = R.  The domain and range of R-1 are equal to the range and domain of R, respectively.  If R is a relation on A, then R-1 is also a relation on A. Copyright © Nahid Sultana 2014-2015.
  • 9. Representing Relations Using Matrices  A relation between finite sets can be represented using a zero-one matrix.  Suppose R is a relation from A = {a1, a2, …, am} to B = {b1, b2, …, bn}.  The elements of the two sets can be listed in any particular arbitrary order. When A = B, we use the same ordering.  The relation R is represented by the matrix MR = [mij], where  The matrix representing R has a 1 as its (i,j) entry when ai is related to bj and a 0 if ai is not related to bj. 10/10/2014 9 Copyright © Nahid Sultana 2014-2015.
  • 10. Examples of Representing Relations Using Matrices Example 1: Suppose that A = {1,2,3} and B = {1,2}. Let R be the relation from A to B containing (a,b) if a ∈ A, b ∈ B, and a > b. What is the matrix representing R (assuming the ordering of elements is the same as the increasing numerical order)? Solution: Here R = {(2,1), (3,1),(3,2)}. The matrix representing R is 10/10/2014 10 Copyright © Nahid Sultana 2014-2015.
  • 11. Examples of Representing Relations Using Matrices (cont.) Example 2: Let A = {a1,a2, a3} and B = {b1,b2, b3,b4, b5}. Which ordered pairs are in the relation R represented by the matrix Solution: R = {(a1, b2), (a2, b1),(a2, b3), (a2, b4),(a3, b1), {(a3, b3), (a3, b5)}. 10/10/2014 11 Copyright © Nahid Sultana 2014-2015.
  • 12. Composition of Relations 10/10/2014 12 Definition: Suppose A, B and C are sets, and  R is a relation from A to B  S is a relation from B to C  Then the composition of R and S, denoted by R ∘ S, is a relation from A to C defined by R ∘ S = {(a , c)| ∃ b ∈ B, for which (a , b) ∈ R and (b , c) ∈ S} Example: Let A = {1, 2, 3, 4}, B = {a, b, c, d}, C = {x, y, z} and let R = {(1, a), (2, d), (3, a), (3, b), (3, d)} and S = {(b, x), (b, z), (c, y), (d, z)} Compute R ∘ S . Using arrow diagram, R ◦ S={(2,z), (3,x), (3,z)} Copyright © Nahid Sultana 2014-2015.
  • 13. Composition of Relations (Cont…) 10/10/2014 13 Example: Let A = {1, 2, 3, 4}, B = {a, b, c, d}, C = {x, y, z} and let R = {(1, a), (2, d), (3, a), (3, b), (3, d)} and S = {(b, x), (b, z), (c, y), (d, z)}. Compute R ∘ S . There is another way of finding R ◦ S. Let MR and MS denote the matrix representations of the relations R and S, respectively. Then The nonzero entries in this matrix tell us which elements are related by R◦S. Thus R ◦ S={(2,z), (3,x), (3,z)} Multiplying MR and MS Copyright © Nahid Sultana 2014-2015.
  • 14. Types of relations 10/10/2014 14 Types of relations which are defined on a set A.  Reflexive and Irreflexive Relations  Symmetric and Antisymmetric Relations  Transitive Relations Definition: A relation R on a set A is reflexive if (a,a) ∈ R for all a ∈ A. Thus R is not reflexive if there exists a ∈ A such that (a, a)∉ R. Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1)(1, 2), (2, 1), (2, 2), (3, 3)} R2 = {(1, 1), (1, 2), (2, 1), (2, 2)} R3 = Φ Determine which relation is reflexive. Copyright © Nahid Sultana 2014-2015.
  • 15. Types of relations (Cont…) 10/10/2014 15 Example: The following relations on the integers are reflexive: R1 = {(a,b) | a ≤ b}, R3 = {(a,b) | a = b or a = −b}, R4 = {(a,b) | a = b}. The following relations are not reflexive: R2 = {(a,b) | a > b} (note that 3 ≯ 3), R5 = {(a,b) | a = b + 1} (note that 3 ≠3 + 1), R6 = {(a,b) | a + b ≤ 3} (note that 4 + 4 ≰ 3). Copyright © Nahid Sultana 2014-2015.
  • 16. Types of relations (Cont…) 10/10/2014 16 Definition: A relation R on a set A is symmetric if whenever aRb then bRa, i.e., if whenever (a, b) ∈ R then (b, a) ∈ R. Thus R is not symmetric if there exists a, b ∈ A such that (a, b) ∈ R but (b, a) ∉ R. Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1)(1, 2), (2, 1), (2, 2), (3, 3)} R2 = {(1, 1), (1, 2), (2, 2)} Determine which relation is symmetric. Copyright © Nahid Sultana 2014-2015.
  • 17. Types of relations (Cont…) 10/10/2014 17 Definition: A relation R on a set A is antisymmetric if whenever aRb and bRa then a = b. Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1)(1, 2), (2, 1), (2, 2), (3, 3)} R2 = {(1, 1), (1, 2)} Determine which relation is antisymmetric. The contrapositive of this definition is that R is antisymmetric if whenever a ≠ b, then either (a,b) ∉ R or (b,a) ∉ R. Definition: A relation R is not antisymmetric if there exist a, b ∈ A such that (a,b)∈ R and (b, a) ∈ R but a ≠ b. Note: Not symmetric ≠ antisymmetric . Copyright © Nahid Sultana 2014-2015.
  • 18. Types of relations (Cont…) 10/10/2014 18 Example: Consider the following relations on the set A = {1, 2, 3}: R1 = {(1, 1), (1, 2), (2, 3), (1, 3)} R2 = {(1, 1), (1, 2), (2,2), (2,3)} R3 = {(1, 1), (1, 2), (1,3), (3,3)} Determine which relation is transitive. Definition: A relation R on a set A is transitive if whenever aRb and bRc then aRc, that is, if whenever (a, b)∈R and (b, c)∈ R then (a, c)∈R. Thus R is not transitive if there exist a, b, c ∈ R such that (a,b)∈ R and (b, c) ∈ R but (a,c) ∉ R. If such a, b and c not exist, then R is transitive. Copyright © Nahid Sultana 2014-2015.
  • 19. Equivalence relation 10/10/2014 19 Example: Consider the following relation on the set A = {1, 2, 3,4}: R = {(1, 1), (1, 2), (2,1), (2,2), (3,4), (4,3), (3,3), (4, 4)} Determine whether this relation is equivalence or not. Definition: A relation R on a set A is called an equivalence relation if R is reflexive, symmetric, and transitive.  It follows three properties: 1) For every a ∈ A, aRa. 2) If aRb then bRa. 3) If aRb and bRc, then aRc. The relation R is equivalence because R is reflexive, symmetric and transitive. Copyright © Nahid Sultana 2014-2015.
  • 20. Equivalence relation (cont…) 10/10/2014 20 Example: Let A= ℤ, set of integers. Let R be defined by aRb iff a ≤ b. Determine whether this relation is equivalence or not. Therefore the relation R is not an equivalence. Solution: 1) The relation R is reflexive a ≤ a. 2) The relation R is not symmetric a ≤ b does not imply that b ≤ a . 3) The relation R is transitive because a ≤ b and b ≤ c imply that a ≤ c. Copyright © Nahid Sultana 2014-2015.
  • 21. Equivalence relation (cont…) 10/10/2014 21 Example: Prove that congruence modulo n is an equivalence relation on ℤ. Definition: For a given positive integer n ≥ 2, two integers a and b are called congruent modulo n, written as a ≡ b(mod n) if a - b is divisible by n. Solution: 1) Reflexivity: For any a ∈ ℤ, we have a ≡ a(mod n) because a- a=0 is divisible by n. Hence the relation is reflexive. Cont to next slide….. Copyright © Nahid Sultana 2014-2015.
  • 22. Equivalence relation (cont…) 10/10/2014 22 2) Symmetry: suppose a ≡ b(mod n) ⇒ a-b is divisible by n ⇒ (a-b)/n = k , for some k ∈ℤ ⇒ a-b = nk . Therefore, b-a = -(a-b) = -nk = n(-k) ⇒(b-a)/n = -k, so b-a is divisible by n as -k∈ℤ i.e. b ≡ a(mod n). Thus the relation is symmetric. 3) Transitivity: suppose a ≡ b (mod n) and b ≡ c (mod n), then (a-b)/n =k and (b-c)/n=l for some k,l∈ℤ. i.e. a-b=nk and b-c=nl. By adding this two equations we get, a-c=n(k+l)⇒(a-c)/n=k+l. So a-c is divisible by n as k+l ∈ℤ, i.e. a ≡ c(mod n). Thus the relation is transitive. Hence this is an equivalence relation on ℤ. Copyright © Nahid Sultana 2014-2015. Solution: Cont…..
  • 23. Equivalence class (cont…) 10/10/2014 23 Definition: For an equivalence relation R defined on A and for a∈ A, the set [a] = {x ∈ A| (a, x) ∈R} is called the equivalence class of a in A. Definition: Any b ∈ [a] is called a representative of this equivalence class. Definition: The collection of all equivalence classes of elements of A under an equivalence relation R is called the quotient set, denoted by A/R, i.e. A/R = {[a] | a ∈ A}. Note: The quotient set A/R is a partition of A. Copyright © Nahid Sultana 2014-2015.
  • 24. Partial Orderings 10/10/2014 24 Definition : A relation R on a set S is called a partial ordering, or partial order, if it is reflexive, antisymmetric, and transitive. Definition: A set A together with a partial ordering R is called a partially ordered set or poset. Example: Show that the “greater than or equal” relation (≥) is a partial ordering on the set of integers. Solution: Reflexivity: a ≥ a for every integer a. Antisymmetry: If a ≥ b and b ≥ a , then a = b. Transitivity: If a ≥ b and b ≥ c , then a ≥ c. Copyright © Nahid Sultana 2014-2015.
  • 25. Closure Properties 10/10/2014 25  Suppose R is a relation on A  If R does not possess a particular relation (reflexive, symmetric, transitive)  Then we may add as few new pairs as possible until we get a new relation R1 on A that have that required property.  If such R1 exists, we call it the closure of R with respect to that property.  Example: Reflexive closure, Symmetric closure, Transitive closure. Copyright © Nahid Sultana 2014-2015.