Chapter 2 Sets and Functions.

Slides:



Advertisements
Similar presentations
Chapter 3 Direct Proof and Proof by Contrapositive
Advertisements

Instructor: Hayk Melikya
Section 1.6: Sets Sets are the most basic of discrete structures and also the most general. Several of the discrete structures we will study are built.
Analytical Methods in CS (CIS 505)
1 Learning Objectives for Section 7.2 Sets After today’s lesson, you should be able to Identify and use set properties and set notation. Perform set operations.
Set Notation.
This section will discuss the symbolism and concepts of set theory
Chapter 3 – Set Theory  .
Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set,
Mathematical Preliminaries (Hein 1.1 and 1.2) Sets are collections in which order of elements and duplication of elements do not matter. – {1,a,1,1} =
CS 103 Discrete Structures Lecture 10 Basic Structures: Sets (1)
Chapter 7 Logic, Sets, and Counting Section 2 Sets.
Barnett/Ziegler/Byleen Finite Mathematics 11e1 Chapter 7 Review Important Terms, Symbols, Concepts 7.1. Logic A proposition is a statement (not a question.
Mathematical Proofs. Chapter 1 Sets 1.1 Describing a Set 1.2 Subsets 1.3 Set Operations 1.4 Indexed Collections of Sets 1.5 Partitions of Sets.
CS201: Data Structures and Discrete Mathematics I
College Algebra Sixth Edition James Stewart Lothar Redlin Saleem Watson.
Copyright © Cengage Learning. All rights reserved.
Naïve Set Theory. Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude.
1 Introduction to Abstract Mathematics Sets Section 2.1 Basic Notions of Sets Section 2.2 Operations with sets Section 2.3 Indexed Sets Instructor: Hayk.
ELEMENTARY SET THEORY.
Chapter SETS DEFINITION OF SET METHODS FOR SPECIFYING SET SUBSETS VENN DIAGRAM SET IDENTITIES SET OPERATIONS.
2.1 Sets 2.2 Set Operations –Set Operations –Venn Diagrams –Set Identities –Union and Intersection of Indexed Collections 2.3 Functions 2.4 Sequences and.
Sets Definition: 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.
Copyright © Cengage Learning. All rights reserved. CHAPTER 8 RELATIONS.
Chapter 2 1. Chapter Summary Sets (This Slide) The Language of Sets - Sec 2.1 – Lecture 8 Set Operations and Set Identities - Sec 2.2 – Lecture 9 Functions.
“It is impossible to define every concept.” For example a “set” can not be defined. But Here are a list of things we shall simply assume about sets. A.
CPCS 222 Discrete Structures I
Section 6.1 Set and Set Operations. Set: A set is a collection of objects/elements. Ex. A = {w, a, r, d} Sets are often named with capital letters. Order.
1-1 Copyright © 2013, 2005, 2001 Pearson Education, Inc. Section 2.4, Slide 1 Chapter 2 Sets and Functions.
Sets, Permutations, and Combinations. Lecture 4-1: Sets Sets: Powerful tool in computer science to solve real world problems. A set is a collection of.
Chapter 1 Logic and Proof.
The Relation Induced by a Partition
Chapter 1 Logic and Proof.
Sets.
Dr. Ameria Eldosoky Discrete mathematics
Copyright © Cengage Learning. All rights reserved.
CHAPTER 3 SETS, BOOLEAN ALGEBRA & LOGIC CIRCUITS
Chapter 3 The Real Numbers.
The Language of Sets If S is a set, then
Copyright © Cengage Learning. All rights reserved.
CSNB 143 Discrete Mathematical Structures
1.1: Objectives Properties of Real Numbers
Set, Combinatorics, Probability & Number Theory
Solving Compound Inequalities
Chapter 2 Section 8.
Chapter 1 Logic and Proofs Homework 2 Given the statement “A valid password is necessary for you to log on to the campus server.” Express the statement.
Copyright © Cengage Learning. All rights reserved.
Chapter 2 Sets and Functions.
Chapter 5, Set Theory 1.
Chapter 3 The Real Numbers.
Chapter 3 The Real Numbers.
Chapter 3 The Real Numbers.
Taibah University College of Computer Science & Engineering Course Title: Discrete Mathematics Code: CS 103 Chapter 2 Sets Slides are adopted from “Discrete.
Chapter 2 Sets and Functions.
Chapter 2 Section 8.
CS100: Discrete structures
Algebra 1 Section 1.1.
Chapter 1 Section 1.
Precalculus Mathematics for Calculus Fifth Edition
CHAPTER 1 - Sets and Intervals
COUNTING AND PROBABILITY
CHAPTER 1 - Sets and Intervals
Copyright © Cengage Learning. All rights reserved.
2 Chapter Numeration Systems and Sets
Elementary Number Theory & Proofs
Chapter 3 The Real Numbers.
Chapter 7 Logic, Sets, and Counting
Copyright © Cengage Learning. All rights reserved.
Foundations of Discrete Mathematics
SETS, RELATIONS, FUNCTIONS
Presentation transcript:

Chapter 2 Sets and Functions

Section 2.1 Basic Set Operations

The idea of a set or collection of things is common in our everyday experience. We speak of a football team, a flock of geese, or a finance committee. We do not have a formal definition of the concept “set,” but we use the informal understanding that a set is a collection of objects characterized by some defining property that allows us to think of the objects as a whole. The objects in a set are called elements or members of the set. Notation Meaning a  S Object a is an element of set S. a  S Object a is not an element of set S. To define a particular set, we have to indicate the property that characterizes its elements. For a finite set, this can be done by listing its members. For example, if set A consists of the elements 1, 2, and 3, then we write A = {1, 2, 3}. If B consists of just one member, say 5, then we write B = {5}. That is, we distinguish between the element 5 and the set that contains 5 as its only member.

For an infinite set we cannot list all the members, so a defining rule must be given. It is customary to set off the rule within braces, as in C = {x : x is prime}. Read, “C is the set of all x such that x is prime.” Definition 2.1.3 Let A and B be sets. We say that A is a subset of B (or A is contained in B) if every element of A is an element of B, and we denote this by writing A  B. If A is a subset of B and there exists an element in B that is not in A, then A is called a proper subset of B. This definition tells us what we must do if we want to prove A  B. We must show that “if x  A, then x  B” is a true statement. That is, we must show that each element of A satisfies the defining condition that characterizes B.

Definition 2.1.4 Let A and B be sets. We say that A is equal to B, written A = B, if A  B and B  A. When this definition is combined with the definition of subset, we see that proving A = B is equivalent to proving x  A  x  B and x  B  x  A. Note: in describing a set, the order in which the elements appear does not matter, nor does the number of times they are written. So the following sets are all equal: {1, 2, 3, 4} = {2, 4, 1, 3} = {1, 2, 3, 2, 4, 2}. Although we cannot give a formal definition of them now, it is convenient to name the following sets: will denote the set of all positive integers (or natural numbers). will denote the set of all rational numbers. will denote the set of all real numbers.

the elements are being chosen. Sometimes we abbreviate the notation. In constructing examples of sets it is often helpful to indicate a larger set from which the elements are being chosen. Sometimes we abbreviate the notation. {x : x  and 0 < x < 1} becomes {x  : 0 < x < 1} Read, “The set of all x in such that 0 < x < 1.” There is a standard notation that we use for interval subsets of the real numbers: [a, b] = {x    : a  x  b}, (a, b) = {x   : a < x < b}, [a, b) = {x    : a  x < b}, (a, b] = {x    : a < x  b}. We use a square bracket if the endpoint is included and a round parenthesis if the endpoint is not included. The set [a, b] is called a closed interval and the set (a, b) is called an open interval. We also have occasion to refer to the unbounded intervals: [a, ) = {x    : x  a}, (a, ) = {x    : x > a}, ( , b] = {x   : x  b}, ( , b) = {x    : x < b}. At this time no special significance should be attached to the symbols “  ” and “ –  ” as in [a, ) and (– , b]. They simply indicate that the interval contains all real numbers greater than or equal to a, or less than or equal to b, as the case may be.

Example 2.1.5* *Similar to Example 2.1.5 in the text. Let A = {1, 3}, B = {1, 3, 5}, and C = {x  : x2 = – 1}. Determine whether each statement is true or false. 3  B True 3 is one of the elements listed in B. 3  C False 32  – 1. A  B True Every element of A is an element of B. B  A False 5  B, but 5  A. C  A True Does C contain any elements that are not in A? No. So all the elements of C are contained in A. In fact, set C contains no members. It is an example of the empty set. We denote the empty set by the symbol  . Theorem 2.1.7 Let A be a set. Then   A. Proof: To prove that   A, we must establish that the implication if x  , then x  A is true. Since  has no members, the antecedent “x  ” is false for all x. Thus, according to our definition of implies, the implication is always true. 

There are three basic ways to form new sets from existing sets. Definition 2.1.8 Let A and B be sets. The union of A and B (denoted A  B), the intersection of A and B (denoted A  B), and the complement of B in A (denoted A \ B) are given by A  B = {x : x  A or x  B} A  B = {x : x  A and x  B} A \ B = {x : x  A and x  B} If A  B = , then A and B are said to be disjoint. These three set operations given above correspond in a natural way to three of the basic logical connectives: x  A  B iff (x  A)  (x  B) x  A  B iff (x  A)  (x  B) x  A \ B iff (x  A)  ~ (x  B).

Mathematical concepts and proofs always occur within the context of some mathematical system. It is customary for the elements of the system to be called the universal set. Then any set under consideration is a subset of this universal set. Example 2.1.10 Let A = {1, 2, 3, 4} and B = {2, 4, 6} be subsets of the universal set U = {1, 2, 3, 4, 5, 6}. Then A  B = {1, 2, 3, 4, 6}. If you toss the elements of A and B into the same bag, this is what you get. A  B = {2, 4} These are the elements that A and B have in common. A \ B = {1, 3} If you start with A and throw out anything that’s in B, this is what’s left. U \B = {1, 3, 5}. If you start with all of U and throw out anything that’s in B, this is what’s left. Note: The complement of B in the universal set U, namely U \B, is sometimes called the complement of B.

One way to visualize set operations is by use of Venn diagrams as shown below. The rectangle represents the universal set U. Set A is the blue circle on the left. Set B is the yellow circle on the right. If we color both circles, the total colored area is the union: A  B. And the green area where they overlap is the intersection: A  B. U A B

Theorem 2.1.13 Let A, B, and C be subsets of a universal set U. Then the following statements are true. (a) A  (U \ A) = U (b) A  (U \ A) =  (c) U \(U \ A) = A (d) A  (B  C ) = (A  B)  (A  C ) (e) A  (B  C ) = (A  B)  (A  C ) (f ) A \(B  C ) = (A \ B)  (A \ C) (g) A \(B  C ) = (A \ B)  (A \ C) The proofs of most of these are left as exercises, but we will do part (d) to illustrate the process.

Proof: We begin by showing that A  (B  C )  (A  B)  (A  C ). Theorem 2.1.13 (d) A  (B  C ) = (A  B)  (A  C ). Proof: We begin by showing that A  (B  C )  (A  B)  (A  C ). If x  _____________, then either x  A or x  B  C. If x  A, then certainly x  A  B and x  A  C. Thus x  __________________. On the other hand, if _____________, then x  B and x  C. But this implies that x  A  B and _____________, so x  (A  B)  (A  C ). Hence A  (B  C )  (A  B)  (A  C ). A  (B  C ) (A  B)  (A  C ) x  (B  C) x  (A  C)

Theorem 2.1.13 (d) A  (B  C ) = (A  B)  (A  C ). Conversely, if y  (A  B)  (A  C ), then ____________ and ___________. There are two cases to consider: when y  A and when y  A. If y  A, then y  A  (B  C ) and this part is done. On the other hand, if ___________ , then since y  A  B, we must have y  B. Similarly, since y  A  C and y  A, we have ___________. Thus ______________, and this implies that y  A  (B  C ). Hence (A  B)  (A  C )  A  (B  C ).  y  (A  B) y  (A  C) y  A y  C y  (B  C)

Comments on the proof of Theorem 2.1.13 (d). Notice how the argument divides naturally into parts, the second part being introduced by the word “conversely.” This word is appropriate because the second half of the argument is indeed the converse of the first half. 2. In the first part the point in A  (B  C ) was called x and in the second part the point in (A  B)  (A  C ) was called y. Why is this? The choice of a name is completely arbitrary, and in fact the same name could have been used in both parts. It is important to realize that the two parts are separate arguments; we start over from scratch in proving the converse and can use nothing that was derived about the point x in the first part. By using different names for the points in the two parts we emphasize this separateness. It is common practice, however, to use the same name (such as x) for the arbitrary point in both parts.

Comments on the proof of Theorem 2.1.13 (d). Notice that each half of the argument also has two parts or cases, the second case being introduced by the phrase “on the other hand.” This type of division of the argument is necessary when dealing with unions. If x  S  T, then x  S or x  T. Each of the possibilities must be followed to its logical conclusion, and both “bridges” must lead to the same desired result (or to a contradiction, which would show that only one alternative could occur). When proving that one set, say S, is a subset of another set, say T, it is common to begin with the phrase “If x  S, then…” It is also acceptable to begin with “Let x  S ” and then conclude that x  T. The subtle difference between these phrases is that “Let x  S ” assumes that S is nonempty, so there is an x in S to choose. This might seem to be an unwarranted assumption, but really it is not. If S is the empty set, then of course S  T, so the only nontrivial case to prove is when S is nonempty.

Sometimes we wish to form unions or intersections of more than 2 or 3 sets. To do this we need to extend our previous definitions. Definition 2.1.15 If for each element j in a nonempty set J there corresponds a set Aj, then A = {Aj : j  J } is called an indexed family of sets with J as the index set. The union of all the sets in A is defined by = {x: x  Aj for some j  J } and the intersection is = {x: x  Aj for all j  J }. If J = , we write and . In general, if B is a nonempty collection of sets, then we let = {x: x  B for some B  B  } and = {x: x  B for all B  B  }.

Example 2.1.17* For each k  N, let Ak = [1/k, 3 – 1/k]. Find and *Similar to Example 2.1.17 in the text. For each k  N, let Ak = [1/k, 3 – 1/k]. Find and We have etc. To find the union of all these sets, we note that the left endpoint is getting closer and closer to 0, and the right endpoint is approaching (but never reaches) 3. So, To find the intersection of the sets, we ask, “What numbers are in all of the sets?” We find