(Refer Slide Time: 1:41)

Save this PDF as:

Size: px
Start display at page:

Transcription

1 Discrete Mathematical Structures Dr. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture # 10 Sets Today we shall learn about sets. You must have learnt about sets in school itself. We shall learn a little bit more about sets in these one or two lectures. Now what is a set? A set is a collection of objects and the objects themselves are called elements of the set. (Refer Slide Time: 1:41) For example, you can have the set of natural numbers n or you can think about the set of books or you can think about the set of even non-negative integers less than 10 which is denoted by this. Usually a set is denoted like this and the elements are denoted like this. It is written within flower brackets all the elements are written within flower brackets. So S denotes a set which consists of elements a, b, c, d. Now you say that a belongs to S. Use this symbol for belonging to. So this means a belongs to S or a is in S. In contrast with that if you write e does not belong to S, it is not here, so e does not belong to S or e is not in S.

2 (Refer Slide Time: 3:02) Now, you can also have a definition like this. For example the set of even numbers even non-negative integers you can say that E is equal to x where x belongs to N it is a nonnegative integer and there is a y belonging to N such that x is equal to 2y. This denotes the set of all even non-negative integers. Now you are using a predicate to define e, such a definition is known as implicit definition whereas a definition of this form is known as explicit definition. (Refer Slide Time: 3:12) So a set can be defined using explicit definition or implicit definition. For example, take the set of integers greater than 10 this you can write as x x belongs to i i denotes the set of

3 integers AND x is greater than 10. So you are using a predicate to define this set. So the implicit notation is given there and the explicit notation for that will be set of integers greater than 10 so it is 11, 12, 13 and so on. If it is a finite set you can list all the elements, if it is an infinite set you can list a few of them and the way you write the dot the other members are specified. (Refer Slide Time: 4:48) So we have the concept of the set. And originally in the Naive set theory when it was proposed they found some difficulties in set theory. They found that there were some paradoxes and there were something which leads to contradiction and so on. So let us see the things which let to a contradiction or a paradox. Consider the universe of discourse, let the underlying set be set of all sets. Then define a set S like this; it consists of all sets which do not belong in themselves.

4 (Refer Slide Time: 4:50) A set S is defined like this; it consists of all sets which do not belong in themselves. Now the question is S a member of itself? What will happen? Just discuss this; S is equal to x, x belongs to x does S belong to S? This is the question. Suppose S belongs to S, S consists of all sets which do not belong in themselves from this you have to come to the conclusion that S does not belong to S because it does not satisfy the definition. Now suppose S does not belong to S it satisfies this definition so it should belong to S. So from this you conclude that S belongs to S. (Refer Slide Time: 7:10)

5 So you are arriving at a contradiction. So this is known as Russell s paradox, this is called Russell s paradox. So there seems to be some basic flaw in defining sets itself and so people were wondering whether the set theory will really be useful or not. But this paradox or some flaws should not lead one to give up set theory as it is because there are so many benefits achieved by sets. Even in programming languages you use sets you use set operations and so on. So what we need to do is we have to modify the Naive set theory a little bit and one way to do this is, you have different levels so the individual objects form one level, a set which consists of element from this should be in the next level and in this level you may have a set which consists of elements from the lower levels it may have some element from here a element from here and so on. And at the higher level you may have a set which consists of elements from the lower level. But the set will not have an element in the same level. (Refer Slide Time: 8:35) So if you distinguish like that between levels and allow for the definition of a set in such a way that a set can have only elements from the lower levels then this sort of a paradox will not arise because you will not have such in this definition is itself because a set will have only elements from the lower level. So if you modify that definition of set in that way this paradox will not arise and there will not be any flaw in the set theory and you can make use of set theory as it is. Now, sets are denoted by what is known as Venn diagram. The universal set is denoted like this; a set A may be denoted like this, another set B may be denoted like this, this portion will denote the set of elements common to A AND B or it denotes A intersection B. Like that you can represent sets by means of Venn diagram. Now let us consider some relations among sets.

6 (Refer Slide Time: 9:54) See relation between sets; Consider two sets A and B. Let A and B be sets then when do you say that A is a subset of B? This is denoted as A contained in B or it is denoted like this A contained B. If each element of A is an element of B that is A contained in B is saying it is equivalent to saying for all of x x belongs to A implies x belongs to B. (Refer Slide Time: 10:05) Now sometimes you use this to denote A contained in B and A contained in B if they are not equal this is the subset and this is the proper subset. In this case it is possible for A and B to be equal also, in this case B has at least one more element than A they are not equal.

7 (Refer Slide Time: 11:41) So when you say A is contained in B you could also write it as a B contains A, you have to write like this B contains A this is read as B contains A. So you read it as if A is contained in B we also write B contains A and say A is contained in B or B contains A, or B is a superset of A you can also say it like this A is a subset of B or B is a superset of A. We write A not contained in B that is contained in and then a slash over that, if A is not a subset of B. If A is contained in B and A is not equal to B we say A is a proper subset of B. Let U be the universe of discourse and A is a set then A will be a subset of U. U is the whole clause it is a big set a universal set so any set will be contained in that. Let A and B be sets then A is equal to B if and only if A is contained in B and B is contained in A.

8 (Refer Slide Time: 13:00) So if you use, if A is contained in B AND B is contained in A so when you say this they can be equal also when you have both these which means A is equal to B. So here you have to take it as inclusion and not proper inclusion. For any set A A is contained in A here again this represents subset and not proper subset. Sometimes you distinguish like this; this denotes subset and this denotes proper subset, sometimes in some notations this itself denotes subset. A set with no members is called an empty or null or void set. This is denoted by phi and a set with one member is called a singleton set. (Refer Slide Time: 14:20)

9 Let phi be an empty set and A is an arbitrary set then phi is contained in A. How do you prove that? This denotes the empty set and A is an arbitrary set a singleton set will be denoted like this having a single element this is a singleton set. Now you want to show phi is contained in every set A. What is the definition of A contained in B? x belongs to A implies x belongs to B this is the condition. Now phi is contained means x belongs to phi it should satisfy this condition x belongs to phi implies x belongs to A. But you can see that the premise of this implication is false. x belongs to phi is false so the whole implication is true so it satisfies this condition phi contained in A. This is true the whole implication is true so any empty set is contained in any set A. Next is, if phi and phi dash be sets which are both empty then phi is equal to phi dash. Again this you can prove like this; if phi is the empty set it is contained in any set so phi is contained in phi dash and if phi dash is an empty set it will be contained in any set so phi dash will be contained in phi. So from these you can conclude phi is equal to phi dash, there can be only one empty set. (Refer Slide Time: 16:33) Operations on sets: We will have some binary operations on sets let us consider what they are. Let A and B be sets, the union of A and B denoted by A union B is a set, A union B is equal to x x belongs to A OR x belongs to B. It consists of all elements contained in A and B both. The intersection of A and B denoted by A intersection B is the set A intersection B x belongs to A AND x belongs to A. So A intersection B consists of all those elements which are elements of both A and B. The difference of A and B or relative compliment of B with respect to A and is denoted as A minus B and that is the set A minus B is equal to x x belongs to A AND x does not belong to B.

10 (Refer Slide Time: 16:36) That is, this consists of all elements which are in A but not in B. Let us consider some example; Suppose A is equal to a, b, c, d and B is equal to c, d, e, f what is A union B? It will consist of elements which are either in A or in B that is a, b, c, d, e, f. A intersection B will consist of all those elements which are both in A and B that is c and d. And A minus B will consist of elements which are in A but not in B and in this case it will be a b in this example. (Refer Slide Time: 18:55)

11 Now if A and B are two sets and A intersection B is empty then A and B are said to be disjoint sets. If C is a collection of sets such that any two distinct elements of C are disjoint then you say C is a collection of pair wise disjoint sets. (Refer Slide Time: 19:04) Now, let us see what is meant by commutativity and associativity. This is a binary operation, let us denote this, this denotes a binary operation. And x operator y denotes the resultant obtained by applying the operator to x and y. This denotes the resultant obtained by applying the operation to the operands x and y. Then when do you say that operation is commutative, if x operator y is equal to y operator x and it is said to be associative if you perform the operation on x and y first and z that is equivalent to performing the operation on y and z first and then performing the operation with x and the resultant. So these are the definitions of commutative and associative operations.

12 (Refer Slide Time: 19:30) Let us see that the union and intersection operations are commutative and associative on sets. The set operations union and intersection are both commutative and associative. For arbitrary sets A, B and C you have A union B is equal to B union A. That is both of them will contain only elements which belong to both A and B. In a set the order is not important so I can denote a set by a, b, c, d this is the same as denoting b, d, c, a they denote the same sets. The order does not matter here. So A union B is equal to B union A, A intersection B is equal to B intersection A again it consists of all elements which are common to both A and B. And again it is also associative, union is associative. The left hand side will consist of all elements which belong to either A or B or C and that is the same with the right hand side. Similarly, intersection is also associative because it will consist of all elements which belong to A, B and C.

13 (Refer Slide Time: 20:40) Now formal proof can be written just using OR, AND and logical connectives. Now you have two binary operation; dell and square I will say then how do you define distributivity of one operator with respect to the other? Then dell distributes over square if the following hold; x dell y operator z is equal to x dell y operator x dell z. And similarly, y operator z then dell x is defined as y dell x operator z dell x. (Refer Slide Time: 22:17) For example, if you take multiplication and addition you can see that x into y plus z if you take the set of integers as the underlying set x dot y plus z will be x into y plus x into z. And similarly, x plus y dot z will be x dot z plus y dot z. So here dot distributes over

14 plus and multiplication distributes over addition. So in set also you can have union and intersection and let us see how they distribute. The set operations of union and intersection distribute over each other. For arbitrary sets A and B and C A union B intersection C is equal to A union B intersection A union C. You can use Venn diagram and convince yourself about this. You have the universal set like this, you have sets A, you have set B and you have set C. What is the left hand side B intersection C A union B intersection C what is B intersection C? This is B intersection C this portion is denoted by B intersection C and A union B intersection will be this whole thing A and B intersection C. (Refer Slide Time: 25:04) Now, again let me draw the same thing A, B and C. What is the right hand side? The right hand side is A union B A union B is this whole thing and A union C is this and this together. Now the intersection of that will be this.

15 (Refer Slid Time: 25:45) So you can easily see that A union B intersection C is equal to A union B intersection A union C. And similarly, you can also prove that A intersection B union C is equal to A intersection B union A intersection C it is a similar way you can prove. Let A, B, C, D be arbitrary subsets of a universe U then you have the following assertions. What is A union A? It is just A itself this is idiom portent laws they are called idiom portent laws. Similarly, A intersection A is also again A it consists of all elements in A only. And if you add the empty set to A A union phi in essence you are not adding anything so that is A. (Refer Slide Time: 27:15)

16 And A intersection phi means it will consist of all elements which are common to A and common to the empty set but empty set does not contain any elements so this is equal to empty set. And A minus B will consists of all elements which are in A but not in B so that will be obviously contained in A. Again you have, if A is contained in B and C is contained in D then you will have A union C contained in B union D. These things you can prove using Venn diagram. For example, consider A, A is contained in B so this is A, this is B, A is contained in B and C is contained in D B union D is the whole set and A union C is this portion. (Refer Slide Time: 28:10) We can see that A union C is contained in B union D. These are not very difficult to visualize. Similarly, if you have A is contained in B and C is contained in D then A intersection C is contained in B intersection D. You also have the following theorems A will be contained in A union B and A intersection B will be contained in A. If A is contained in B then A union B will be B because if A is contained in B B is a bigger set than A and all elements of A are already in B. So A union B is nothing but B itself. Similarly, if A is contained in B all elements of A or in B so if you take the intersection that means the elements common to both A and B that is nothing but A itself. And A minus phi denotes the set of all elements belonging to A but not belonging to the empty set. And you know that empty set does not have any elements so A minus phi is just A. And A intersection B minus A will be empty because B minus A consists of all elements which are in B but not in A and A has some elements so there is no common element between these two so their intersection will be empty. Similarly, you have A union B and B minus A is A union B because this denotes all elements of B which are not in A and this denotes the elements which are in A so the union will be A union B.

17 (Refer Slide Time: 28:23) Similarly you can also prove these two very easily, A minus B union C will be A minus B intersection A minus C and A minus B intersection C will be A minus B union A minus C. You can prove these things using Venn diagram. Let U be a universe and A be a subset of U. The complement of A denoted by A bar is the set A bar is equal to U minus A. That is the set of all elements x where x does not belong to A. (Refer Slide Time: 30:30) So, if the underlying universal set is given like this and A is this set the complement A bar will be the set of all elements which are in U but not in A this denotes the

18 complement of A. A bar is the set of elements in U and x does not belong to A so the complement will be denoted like this. Now, we can very easily see that let A be an arbitrary subset of some universe U, then what can you say about A union A bar? This is A this is A bar so A union A bar will be the universal set. And there are no common elements between A and A bar so A intersection A bar will be the empty set. Now we have to prove that the complement of a set is unique. You are fixing the universal set and you want to show that the complement is unique, that is denoted by this theorem. Let A and B be subsets of a universe U then B is A s compliment A bar if and only if A union B is equal to U and A intersection B is phi. Let us see how we can prove that. The theorem states if and only if so you have to prove in both directions. So suppose B is equal to A bar then by the previous theorem the previous theorem states this; A union A bar is U and A intersection A bar is phi. Now we know that B is A bar so A union B is equal to U and A intersection B where A bar is B is empty. The other way round we have to prove that if these are true that would imply B is equal to A bar. (Refer Slide Time: 33:43) Let us see how we can prove that. Now B you can write as B intersection U and what is U? U is A union A bar. Now, using distributive law this is B intersection A union B intersection A bar. And what is B intersection A? That is empty. We know that A intersection B is empty so this is empty set union B intersection A bar. And this you can write as A bar intersection A union B intersection A bar or if I use commutativity you can write it as A bar intersection B. And using the distributive law on the other way round this is A bar intersection A union B. But what is A union B? A union B is the universal set so this is A bar intersection U is equal to A bar. So starting from B we have come to the conclusion that B is equal to A bar.

19 (Refer Slide Time: 35:55) Let us see the steps once more. We know that A union B is the universal set and A intersection B is the empty set. Then we want to show that B is equal to A bar we proceed like this B is equal to B intersection U because any set if you intersect it with universal set that will be the set itself. And I can write the universal set as a A union A bar. Now, using distributive laws this will become equal to B intersection A union B intersection A bar. But we know that A intersection B is empty that is B intersection A is also empty, this is empty union this. But the empty set again I can again write it as A bar intersection A and this portion you can use commutativity and write it as A bar intersection B. And applying the distributive law in the reverse you can write it as A bar intersection A union B. And A union B we know is the universal set so A bar intersection universal set is nothing but A bar. So B is equal to A bar or the complement of the set is unique. Now you can see that if A bar is an arbitrary set then A bar bar is A. That is if you take the complement of set A and again take the complement that will be equal to the original set. The complement of the complement of A is A. And similar to what we studied as De Morgan s laws in logic here also you have De Morgan s laws.

20 (Refer Slide Time: 37:15) A and B are arbitrary subsets then A union B complement is equal to A bar intersection B bar and A intersection B complement is equal to A bar union B bar. This you can see with Venn diagram very easily. You have two sets underlying universal set is this and you have two sets A B, A union B is this, the complement of this is this portion. A union B complement is the dotted portion and you can see that if you take the complement of A this will be the portion. The whole thing will be the complement and the complement of B which will be this portion. So if you take the complement of A that is the dotted portion and the complement of B which is the slash portion the intersection will give you this portion because this will not be in the intersection this portion also will not be in the intersection so A bar intersection B bar is equal to A union B bar, these two are equal.

21 (Refer Slide Time: 39:18) And similarly you can prove the other way round also. That is you have two sets A B what is A intersection B? This is it so A intersection B complement is the whole set except this portion. And A complement is the whole portion except A and B complement is the whole portion except B and you can very easily see that this is A bar union B bar. So when you take the complement the intersection becomes union and the union becomes intersection. (Refer Slide Time: 40:05)

22 These are known as De Morgan s laws and you can see the similarity between saying that complement of p AND q is p OR q and so on. NOT OR rather NOT of p AND q is NOT p OR NOT q see the similarity between this and set uni[que] (Refer Slide Time: 40:31) Let C be a collection of subsets in some universe U. The union of the members of C denoted by the union of S belongs to C S is the set union and it is denoted by this, the set of all elements such that S belongs to C and x belongs to S. This is extending the definition of union not to two sets but a collection of sets. Similarly, this is extending the definition of intersection not to two sets alone but extending to a finite collection of sets. You denote the intersection as x such that x belongs to all of them.

23 (Refer Slide Time: 40:33) We have seen that you can define a set explicitly or implicitly. Explicitly means you just enumerate all the elements of the set. Implicitly means you define the set using a predicate. Apart from these two types of definitions there is one more type of definition of sets which is known as inductive definition. (Refer Slide Time: 41:48) Let us see how we can define a set inductively. Now the inductive definition of a set always consists of three components. The first components are the basis or the basis clause where you define the basic building blocks. The basis or basis clause of the definition establishes that certain objects are in the set. This part of the definition has a

24 dual function of establishing that the set being defined is not empty and characterizing the building blocks which will be used to construct the remainder of the set. (Refer Slide Time: 41:53) I will give an example later but first let us go through this. Apart from the basis step there will be an induction step or an induction clause. And here this inductive definition has the second part as induction; it denotes the ways in which elements of the set can be a combined to obtain no elements. The inductive clause always asserts that if objects x, y, z etc are elements of the set then they can be combined in certain specified ways to create other objects which are also in the set. Thus while the basis clause describes the building blocks of the set the inductive clause describes the operations which can be performed on objects in order to construct new elements of the set.

25 (Refer Slide Time: 42:38) Let us take one or two examples and see. For example take the set of nonnegative integers N, N is 0, 1, 2, 3, etc. You can try to define this set inductively like this; basis clause is 0 belongs to N. Then the induction clause is, this is the basic building block, if x belongs to N then x plus 1 belongs to N. (Refer Slide Time: 44:15) So starting with 0, 0 plus 1 is 1 will belong to the set, from 1 plus 1 2 will belong to the set and you will be able to get all elements of the set. This is just to illustrate how it works. Actually there is a slight flaw here in the sense that you are trying to define natural numbers using addition but actually addition is defined after you define the

26 natural numbers. This is just to show you a simple example. Let me a take up a different example. Consider the set of S, S is the set of well formed strings of parenthesis. (Refer Slide Time: 45:20) Let me take one particular parenthesis the square brackets alone, so this is an element which will belong to S. Again another element this also is a well formed string this will belong to S. Another element is this and this will belong to S. But this does not belong to S because they are not matching right parenthesis occurring before the left parenthesis. Or you cannot have something which does not belong to S because you have one pair of parenthesis and this does not have a matching left parenthesis. And similarly if you have, this does not belong to S.

27 (Refer Slide Time: 46:16) How can we define this set, a string of well formed parenthesis inductively? So as a basis clause you will have, the basis steps you will have this belongs to S. As mentioned earlier the basis clause gives you the basic building block and also it tells you that the set is non empty. Let us go back to the definition. The basis or basis clause of the definition establishes that certain objects are in the set. This part of the definition has the dual function of establishing that the set being defined is not empty and of characterizing the building blocks which will be used to construct the remainder of the set. Now, what are the induction clauses or what is the induction clause here? Starting with this how will you find more and more elements of the set? Now in this example it will be like this; if x belongs to S x is a well formed string of parentheses then this belongs to S. So if you have a matching well formed string x adding one more left parenthesis and one more right parenthesis still gives you the well formed string of parenthesis. This is one and another one is if x, y belongs to S then x concatenated with y or x by the side of y belongs to S. That is if you have a well formed string of parenthesis here then placing by its side another well formed string y gives you a well formed string of parenthesis. For example if I have something like this, this is x this is x and y is say this is x y is this, then x, y will give you this, this is a well formed parenthesis each left element has a matching right parenthesis. So you can try to define many of these sets in this manner. This is called inductive definition of a set. Now first we consider that there will be three parts to the definition. The first part is basis part which we have seen. The second part is the induction clause this tells you how you are going to perform some operations starting from some elements of the set and build more and more elements of the set.

28 I will read this again; the induction or inductive clause of an inductive definition establishes the ways in which elements of the set can be combined to obtain new elements. The inductive clause always asserts that if objects x, y, z are elements of the set then they can be combined in certain specified ways to create other objects which are also in the set. So basis clause describes the basic building blocks and inductive clause tells you how to build more and more elements. Apart from that there is one more clause which is called the extremal clause. The third part of the definition is known as the extremal clause. The extremal clause asserts that unless an object can be shown to be a member of the set by applying the basis and the inductive clauses a finite number of times then the object is not a member of the set. That is you must be able to get all the elements of the set from the basis clause and build more and more elements making use of the operation defined in the induction clause. All elements should be got that way and no other element will be got. The extremal clause of an inductive definition of a set has a variety of forms. You can say it any one of the following ways. One way of saying that is, no object is a member of S unless it is being so follows from a finite number of applications of the basis and inductive clauses so you can call the extremal clause in this manner. Or you can say it in this manner also; the set S is the smallest set which satisfies the basis and inductive clauses. You are not going to get any more elements only those elements which are built from the basis clause and the induction clause. (Refer Slide Time: 51:20) Or you can even say it in one of these two ways, let us see how you can say that. The set S is the set such that S satisfies the basis and inductive clauses and no proper subset of S satisfies them. Or put it in another way you can say it like this; if T is a subset of S such

29 that T satisfies the basis and the induction clauses then T is equal to S. Or you can say it in this way; the set S is the intersection of all sets which satisfy the properties specified by the basis and the inductive clauses. (Refer Slide Time: 52:07) Now we have seen that apart from the implicit definition and the explicit definition you can also define sets in a inductive manner. And in this definition the definition will consist of three parts; the basis clause, the induction clause and the extremal clause. This sort of a definition of a set by induction is used in proofs like proof by induction. We shall see what is proof by induction in the next lecture. Now the third part of the definition is called an extremal clause. It tells you that the elements of the set are built from the basis and the induction clause and all elements are built that way no other elements will be built and so on. So we saw four different ways of mentioning the extremal clause. The part you must realize is whatever set you define inductively either it is set of well formed strings of parenthesis, the set of integers, set of even integers or whatever it is the extremal clause is the same. The form of the extremal clause or the statement of the extremal clause is the same for all definitions which use inductive definition but it must be stated explicitly. The third point apart from specifying the basis clause and the induction clause you must specifically mention the extremal clause. Without that the definition is not complete even though the extremal clause is the same for all definitions. So we have seen this definition let us see how to make use of this in proofs by induction in the next lecture.

Theory of Computation Prof. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras

Theory of Computation Prof. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture No. # 31 Recursive Sets, Recursively Innumerable Sets, Encoding

4.1. Definitions. A set may be viewed as any well defined collection of objects, called elements or members of the set.

Section 4. Set Theory 4.1. Definitions A set may be viewed as any well defined collection of objects, called elements or members of the set. Sets are usually denoted with upper case letters, A, B, X, Y,

Sets and set operations

CS 441 Discrete Mathematics for CS Lecture 7 Sets and set operations Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square asic discrete structures Discrete math = study of the discrete structures used

Sections 2.1, 2.2 and 2.4

SETS Sections 2.1, 2.2 and 2.4 Chapter Summary Sets The Language of Sets Set Operations Set Identities Introduction Sets are one of the basic building blocks for the types of objects considered in discrete

The Language of Mathematics

CHPTER 2 The Language of Mathematics 2.1. Set Theory 2.1.1. Sets. set is a collection of objects, called elements of the set. set can be represented by listing its elements between braces: = {1, 2, 3,

Clicker Question. Theorems/Proofs and Computational Problems/Algorithms MC215: MATHEMATICAL REASONING AND DISCRETE STRUCTURES

MC215: MATHEMATICAL REASONING AND DISCRETE STRUCTURES Tuesday, 1/21/14 General course Information Sets Reading: [J] 1.1 Optional: [H] 1.1-1.7 Exercises: Do before next class; not to hand in [J] pp. 12-14:

Digital System Design Prof. D Roychoudhry Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur

Digital System Design Prof. D Roychoudhry Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture - 04 Digital Logic II May, I before starting the today s lecture

Applications of Methods of Proof

CHAPTER 4 Applications of Methods of Proof 1. Set Operations 1.1. Set Operations. The set-theoretic operations, intersection, union, and complementation, defined in Chapter 1.1 Introduction to Sets are

MODULAR ARITHMETIC. a smallest member. It is equivalent to the Principle of Mathematical Induction.

MODULAR ARITHMETIC 1 Working With Integers The usual arithmetic operations of addition, subtraction and multiplication can be performed on integers, and the result is always another integer Division, on

Announcements. CompSci 230 Discrete Math for Computer Science Sets. Introduction to Sets. Sets

CompSci 230 Discrete Math for Computer Science Sets September 12, 2013 Prof. Rodger Slides modified from Rosen 1 nnouncements Read for next time Chap. 2.3-2.6 Homework 2 due Tuesday Recitation 3 on Friday

Lecture 1. Basic Concepts of Set Theory, Functions and Relations

September 7, 2005 p. 1 Lecture 1. Basic Concepts of Set Theory, Functions and Relations 0. Preliminaries...1 1. Basic Concepts of Set Theory...1 1.1. Sets and elements...1 1.2. Specification of sets...2

CmSc 175 Discrete Mathematics Lesson 10: SETS A B, A B

CmSc 175 Discrete Mathematics Lesson 10: SETS Sets: finite, infinite, : empty set, U : universal set Describing a set: Enumeration = {a, b, c} Predicates = {x P(x)} Recursive definition, e.g. sequences

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March

2.1 Sets, power sets. Cartesian Products.

Lecture 8 2.1 Sets, power sets. Cartesian Products. Set is an unordered collection of objects. - used to group objects together, - often the objects with similar properties This description of a set (without

3. Mathematical Induction

3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)

Elementary Number Theory We begin with a bit of elementary number theory, which is concerned

CONSTRUCTION OF THE FINITE FIELDS Z p S. R. DOTY Elementary Number Theory We begin with a bit of elementary number theory, which is concerned solely with questions about the set of integers Z = {0, ±1,

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement

Automata and Formal Languages

Automata and Formal Languages Winter 2009-2010 Yacov Hel-Or 1 What this course is all about This course is about mathematical models of computation We ll study different machine models (finite automata,

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products

Chapter 3 Cartesian Products and Relations The material in this chapter is the first real encounter with abstraction. Relations are very general thing they are a special type of subset. After introducing

Cartesian Products and Relations

Cartesian Products and Relations Definition (Cartesian product) If A and B are sets, the Cartesian product of A and B is the set A B = {(a, b) :(a A) and (b B)}. The following points are worth special

2.1 The Algebra of Sets

Chapter 2 Abstract Algebra 83 part of abstract algebra, sets are fundamental to all areas of mathematics and we need to establish a precise language for sets. We also explore operations on sets and relations

2. Methods of Proof Types of Proofs. Suppose we wish to prove an implication p q. Here are some strategies we have available to try.

2. METHODS OF PROOF 69 2. Methods of Proof 2.1. Types of Proofs. Suppose we wish to prove an implication p q. Here are some strategies we have available to try. Trivial Proof: If we know q is true then

CS 3719 (Theory of Computation and Algorithms) Lecture 4

CS 3719 (Theory of Computation and Algorithms) Lecture 4 Antonina Kolokolova January 18, 2012 1 Undecidable languages 1.1 Church-Turing thesis Let s recap how it all started. In 1990, Hilbert stated a

We give a basic overview of the mathematical background required for this course.

1 Background We give a basic overview of the mathematical background required for this course. 1.1 Set Theory We introduce some concepts from naive set theory (as opposed to axiomatic set theory). The

Mathematical Induction. Lecture 10-11

Mathematical Induction Lecture 10-11 Menu Mathematical Induction Strong Induction Recursive Definitions Structural Induction Climbing an Infinite Ladder Suppose we have an infinite ladder: 1. We can reach

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao,David Tse Note 11

CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao,David Tse Note Conditional Probability A pharmaceutical company is marketing a new test for a certain medical condition. According

Math212a1010 Lebesgue measure.

Math212a1010 Lebesgue measure. October 19, 2010 Today s lecture will be devoted to Lebesgue measure, a creation of Henri Lebesgue, in his thesis, one of the most famous theses in the history of mathematics.

Definition 14 A set is an unordered collection of elements or objects.

Chapter 4 Set Theory Definition 14 A set is an unordered collection of elements or objects. Primitive Notation EXAMPLE {1, 2, 3} is a set containing 3 elements: 1, 2, and 3. EXAMPLE {1, 2, 3} = {3, 2,

SOLUTIONS TO ASSIGNMENT 1 MATH 576

SOLUTIONS TO ASSIGNMENT 1 MATH 576 SOLUTIONS BY OLIVIER MARTIN 13 #5. Let T be the topology generated by A on X. We want to show T = J B J where B is the set of all topologies J on X with A J. This amounts

1 / Basic Structures: Sets, Functions, Sequences, and Sums - definition of a set, and the use of the intuitive notion that any property whatever there

C H A P T E R Basic Structures: Sets, Functions, Sequences, and Sums.1 Sets. Set Operations.3 Functions.4 Sequences and Summations Much of discrete mathematics is devoted to the study of discrete structures,

Reading 13 : Finite State Automata and Regular Expressions

CS/Math 24: Introduction to Discrete Mathematics Fall 25 Reading 3 : Finite State Automata and Regular Expressions Instructors: Beck Hasti, Gautam Prakriya In this reading we study a mathematical model

Basic Concepts of Set Theory, Functions and Relations

March 1, 2006 p. 1 Basic Concepts of Set Theory, Functions and Relations 1. Basic Concepts of Set Theory...1 1.1. Sets and elements...1 1.2. Specification of sets...2 1.3. Identity and cardinality...3

not to be republishe NCERT SETS Chapter Introduction 1.2 Sets and their Representations

SETS Chapter 1 In these days of conflict between ancient and modern studies; there must surely be something to be said for a study which did not begin with Pythagoras and will not end with Einstein; but

13 Infinite Sets. 13.1 Injections, Surjections, and Bijections. mcs-ftl 2010/9/8 0:40 page 379 #385

mcs-ftl 2010/9/8 0:40 page 379 #385 13 Infinite Sets So you might be wondering how much is there to say about an infinite set other than, well, it has an infinite number of elements. Of course, an infinite

MA651 Topology. Lecture 6. Separation Axioms.

MA651 Topology. Lecture 6. Separation Axioms. This text is based on the following books: Fundamental concepts of topology by Peter O Neil Elements of Mathematics: General Topology by Nicolas Bourbaki Counterexamples

(Refer Slide Time: 01.26)

Discrete Mathematical Structures Dr. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture # 27 Pigeonhole Principle In the next few lectures

6.3 Conditional Probability and Independence

222 CHAPTER 6. PROBABILITY 6.3 Conditional Probability and Independence Conditional Probability Two cubical dice each have a triangle painted on one side, a circle painted on two sides and a square painted

Lecture Note 1 Set and Probability Theory. MIT 14.30 Spring 2006 Herman Bennett

Lecture Note 1 Set and Probability Theory MIT 14.30 Spring 2006 Herman Bennett 1 Set Theory 1.1 Definitions and Theorems 1. Experiment: any action or process whose outcome is subject to uncertainty. 2.

INTRODUCTORY SET THEORY

M.Sc. program in mathematics INTRODUCTORY SET THEORY Katalin Károlyi Department of Applied Analysis, Eötvös Loránd University H-1088 Budapest, Múzeum krt. 6-8. CONTENTS 1. SETS Set, equal sets, subset,

Discrete Mathematics

Discrete Mathematics Chih-Wei Yi Dept. of Computer Science National Chiao Tung University March 16, 2009 2.1 Sets 2.1 Sets 2.1 Sets Basic Notations for Sets For sets, we ll use variables S, T, U,. We can

The Set Data Model CHAPTER 7. 7.1 What This Chapter Is About

CHAPTER 7 The Set Data Model The set is the most fundamental data model of mathematics. Every concept in mathematics, from trees to real numbers, is expressible as a special kind of set. In this book,

Set Theory Basic Concepts and Definitions

Set Theory Basic Concepts and Definitions The Importance of Set Theory One striking feature of humans is their inherent need and ability to group objects according to specific criteria. Our prehistoric

MATH10040 Chapter 2: Prime and relatively prime numbers

MATH10040 Chapter 2: Prime and relatively prime numbers Recall the basic definition: 1. Prime numbers Definition 1.1. Recall that a positive integer is said to be prime if it has precisely two positive

Mathematical Induction

Mathematical Induction (Handout March 8, 01) The Principle of Mathematical Induction provides a means to prove infinitely many statements all at once The principle is logical rather than strictly mathematical,

THE LANGUAGE OF SETS AND SET NOTATION

THE LNGGE OF SETS ND SET NOTTION Mathematics is often referred to as a language with its own vocabulary and rules of grammar; one of the basic building blocks of the language of mathematics is the language

Lecture 17 : Equivalence and Order Relations DRAFT

CS/Math 240: Introduction to Discrete Mathematics 3/31/2011 Lecture 17 : Equivalence and Order Relations Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT Last lecture we introduced the notion

Problems on Discrete Mathematics 1

Problems on Discrete Mathematics 1 Chung-Chih Li 2 Kishan Mehrotra 3 Syracuse University, New York L A TEX at January 11, 2007 (Part I) 1 No part of this book can be reproduced without permission from

6.2 Permutations continued

6.2 Permutations continued Theorem A permutation on a finite set A is either a cycle or can be expressed as a product (composition of disjoint cycles. Proof is by (strong induction on the number, r, of

COMP 250 Fall 2012 lecture 2 binary representations Sept. 11, 2012

Binary numbers The reason humans represent numbers using decimal (the ten digits from 0,1,... 9) is that we have ten fingers. There is no other reason than that. There is nothing special otherwise about

Homework 5 Solutions

Homework 5 Solutions 4.2: 2: a. 321 = 256 + 64 + 1 = (01000001) 2 b. 1023 = 512 + 256 + 128 + 64 + 32 + 16 + 8 + 4 + 2 + 1 = (1111111111) 2. Note that this is 1 less than the next power of 2, 1024, which

Basic Proof Techniques

Basic Proof Techniques David Ferry dsf43@truman.edu September 13, 010 1 Four Fundamental Proof Techniques When one wishes to prove the statement P Q there are four fundamental approaches. This document

This asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements.

3. Axioms of Set theory Before presenting the axioms of set theory, we first make a few basic comments about the relevant first order logic. We will give a somewhat more detailed discussion later, but

Lecture 16 : Relations and Functions DRAFT

CS/Math 240: Introduction to Discrete Mathematics 3/29/2011 Lecture 16 : Relations and Functions Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT In Lecture 3, we described a correspondence

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2

CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 2 Proofs Intuitively, the concept of proof should already be familiar We all like to assert things, and few of us

1. Prove that the empty set is a subset of every set.

1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since

CHAPTER 3. Methods of Proofs. 1. Logical Arguments and Formal Proofs

CHAPTER 3 Methods of Proofs 1. Logical Arguments and Formal Proofs 1.1. Basic Terminology. An axiom is a statement that is given to be true. A rule of inference is a logical rule that is used to deduce

The Foundations: Logic and Proofs. Chapter 1, Part III: Proofs

The Foundations: Logic and Proofs Chapter 1, Part III: Proofs Rules of Inference Section 1.6 Section Summary Valid Arguments Inference Rules for Propositional Logic Using Rules of Inference to Build Arguments

Discrete Mathematics Set Operations

Discrete Mathematics 1-3. Set Operations Introduction to Set Theory A setis a new type of structure, representing an unordered collection (group, plurality) of zero or more distinct (different) objects.

3 0 + 4 + 3 1 + 1 + 3 9 + 6 + 3 0 + 1 + 3 0 + 1 + 3 2 mod 10 = 4 + 3 + 1 + 27 + 6 + 1 + 1 + 6 mod 10 = 49 mod 10 = 9.

SOLUTIONS TO HOMEWORK 2 - MATH 170, SUMMER SESSION I (2012) (1) (Exercise 11, Page 107) Which of the following is the correct UPC for Progresso minestrone soup? Show why the other numbers are not valid

Introduction to Proofs

Chapter 1 Introduction to Proofs 1.1 Preview of Proof This section previews many of the key ideas of proof and cites [in brackets] the sections where they are discussed thoroughly. All of these ideas are

INCIDENCE-BETWEENNESS GEOMETRY

INCIDENCE-BETWEENNESS GEOMETRY MATH 410, CSUSM. SPRING 2008. PROFESSOR AITKEN This document covers the geometry that can be developed with just the axioms related to incidence and betweenness. The full

WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT?

WHAT ARE MATHEMATICAL PROOFS AND WHY THEY ARE IMPORTANT? introduction Many students seem to have trouble with the notion of a mathematical proof. People that come to a course like Math 216, who certainly

Sets (DSLC 1.2) Intuitively, a set is a collection of things, called its elements, ormembers. To say that x is an element of S, wewrite

Sets (DSLC 1.2) Intuitively, a set is a collection of things, called its elements, ormembers. To say that x is an element of S, wewrite x 2 S. Other ways of saying this: x belongs to S, S contains x, x

Mathematics Review for MS Finance Students

Mathematics Review for MS Finance Students Anthony M. Marino Department of Finance and Business Economics Marshall School of Business Lecture 1: Introductory Material Sets The Real Number System Functions,

Discrete Mathematics: Solutions to Homework (12%) For each of the following sets, determine whether {2} is an element of that set.

Discrete Mathematics: Solutions to Homework 2 1. (12%) For each of the following sets, determine whether {2} is an element of that set. (a) {x R x is an integer greater than 1} (b) {x R x is the square

Data Communications Prof. Ajit Pal Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture # 03 Data and Signal

(Refer Slide Time: 00:01:23) Data Communications Prof. Ajit Pal Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture # 03 Data and Signal Hello viewers welcome

Fundamentele Informatica II

Fundamentele Informatica II Answer to selected exercises 1 John C Martin: Introduction to Languages and the Theory of Computation M.M. Bonsangue (and J. Kleijn) Fall 2011 Let L be a language. It is clear

CHAPTER 2. Set, Whole Numbers, and Numeration

CHAPTER 2 Set, Whole Numbers, and Numeration 2.1. Sets as a Basis for Whole Numbers A set is a collection of objects, called the elements or members of the set. Three common ways to define sets: (1) A

LESSON SUMMARY. Set Operations and Venn Diagrams

LESSON SUMMARY CXC CSEC MATHEMATICS UNIT Three: Set Theory Lesson 4 Set Operations and Venn Diagrams Textbook: Mathematics, A Complete Course by Raymond Toolsie, Volumes 1 and 2. (Some helpful exercises

A Little Set Theory (Never Hurt Anybody)

A Little Set Theory (Never Hurt Anybody) Matthew Saltzman Department of Mathematical Sciences Clemson University Draft: August 21, 2013 1 Introduction The fundamental ideas of set theory and the algebra

WRITING PROOFS. Christopher Heil Georgia Institute of Technology

WRITING PROOFS Christopher Heil Georgia Institute of Technology A theorem is just a statement of fact A proof of the theorem is a logical explanation of why the theorem is true Many theorems have this

PART I. THE REAL NUMBERS

PART I. THE REAL NUMBERS This material assumes that you are already familiar with the real number system and the representation of the real numbers as points on the real line. I.1. THE NATURAL NUMBERS

Solutions to In-Class Problems Week 4, Mon.

Massachusetts Institute of Technology 6.042J/18.062J, Fall 05: Mathematics for Computer Science September 26 Prof. Albert R. Meyer and Prof. Ronitt Rubinfeld revised September 26, 2005, 1050 minutes Solutions

POWER SETS AND RELATIONS

POWER SETS AND RELATIONS L. MARIZZA A. BAILEY 1. The Power Set Now that we have defined sets as best we can, we can consider a sets of sets. If we were to assume nothing, except the existence of the empty

1 Introduction to Counting

1 Introduction to Counting 1.1 Introduction In this chapter you will learn the fundamentals of enumerative combinatorics, the branch of mathematics concerned with counting. While enumeration problems can

What is a set? Sets. Specifying a Set. Notes. The Universal Set. Specifying a Set 10/29/13

What is a set? Sets CS 231 Dianna Xu set is a group of objects People: {lice, ob, Clara} Colors of a rainbow: {red, orange, yellow, green, blue, purple} States in the S: {labama, laska, Virginia, } ll

3 Some Integer Functions

3 Some Integer Functions A Pair of Fundamental Integer Functions The integer function that is the heart of this section is the modulo function. However, before getting to it, let us look at some very simple

So let us begin our quest to find the holy grail of real analysis.

1 Section 5.2 The Complete Ordered Field: Purpose of Section We present an axiomatic description of the real numbers as a complete ordered field. The axioms which describe the arithmetic of the real numbers

SUBGROUPS OF CYCLIC GROUPS. 1. Introduction In a group G, we denote the (cyclic) group of powers of some g G by

SUBGROUPS OF CYCLIC GROUPS KEITH CONRAD 1. Introduction In a group G, we denote the (cyclic) group of powers of some g G by g = {g k : k Z}. If G = g, then G itself is cyclic, with g as a generator. Examples

C H A P T E R Regular Expressions regular expression

7 CHAPTER Regular Expressions Most programmers and other power-users of computer systems have used tools that match text patterns. You may have used a Web search engine with a pattern like travel cancun

CHAPTER 3 Numbers and Numeral Systems

CHAPTER 3 Numbers and Numeral Systems Numbers play an important role in almost all areas of mathematics, not least in calculus. Virtually all calculus books contain a thorough description of the natural,

0N1 (MATH19861) Mathematics for Foundation Year. math19861.html. Lecture Notes.

0N1 (MATH19861) Mathematics for Foundation Year http://www.maths.manchester.ac.uk/~avb/ math19861.html Lecture Notes 16 Sep 2016 0N1 Mathematics Course Arrangements 16 Sep 2016 2 Arrangements for the Course

Foundations of Mathematics I Set Theory (only a draft)

Foundations of Mathematics I Set Theory (only a draft) Ali Nesin Mathematics Department Istanbul Bilgi University Kuştepe Şişli Istanbul Turkey anesin@bilgi.edu.tr February 12, 2004 2 Contents I Naive

k, then n = p2α 1 1 pα k

Powers of Integers An integer n is a perfect square if n = m for some integer m. Taking into account the prime factorization, if m = p α 1 1 pα k k, then n = pα 1 1 p α k k. That is, n is a perfect square

THE DIMENSION OF A VECTOR SPACE

THE DIMENSION OF A VECTOR SPACE KEITH CONRAD This handout is a supplementary discussion leading up to the definition of dimension and some of its basic properties. Let V be a vector space over a field

Mathematical Induction

Mathematical Induction In logic, we often want to prove that every member of an infinite set has some feature. E.g., we would like to show: N 1 : is a number 1 : has the feature Φ ( x)(n 1 x! 1 x) How

An Interesting Way to Combine Numbers

An Interesting Way to Combine Numbers Joshua Zucker and Tom Davis November 28, 2007 Abstract This exercise can be used for middle school students and older. The original problem seems almost impossibly

LEARNING OBJECTIVES FOR THIS CHAPTER

CHAPTER 2 American mathematician Paul Halmos (1916 2006), who in 1942 published the first modern linear algebra book. The title of Halmos s book was the same as the title of this chapter. Finite-Dimensional

Finite and discrete probability distributions

8 Finite and discrete probability distributions To understand the algorithmic aspects of number theory and algebra, and applications such as cryptography, a firm grasp of the basics of probability theory

1 if 1 x 0 1 if 0 x 1

Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or

(IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems.

3130CIT: Theory of Computation Turing machines and undecidability (IALC, Chapters 8 and 9) Introduction to Turing s life, Turing machines, universal machines, unsolvable problems. An undecidable problem

CHAPTER 7 GENERAL PROOF SYSTEMS

CHAPTER 7 GENERAL PROOF SYSTEMS 1 Introduction Proof systems are built to prove statements. They can be thought as an inference machine with special statements, called provable statements, or sometimes

Notes on Complexity Theory Last updated: August, 2011. Lecture 1

Notes on Complexity Theory Last updated: August, 2011 Jonathan Katz Lecture 1 1 Turing Machines I assume that most students have encountered Turing machines before. (Students who have not may want to look

Basic Probability Concepts

page 1 Chapter 1 Basic Probability Concepts 1.1 Sample and Event Spaces 1.1.1 Sample Space A probabilistic (or statistical) experiment has the following characteristics: (a) the set of all possible outcomes

Handout #1: Mathematical Reasoning

Math 101 Rumbos Spring 2010 1 Handout #1: Mathematical Reasoning 1 Propositional Logic A proposition is a mathematical statement that it is either true or false; that is, a statement whose certainty or

Semantics for the Predicate Calculus: Part I

Semantics for the Predicate Calculus: Part I (Version 0.3, revised 6:15pm, April 14, 2005. Please report typos to hhalvors@princeton.edu.) The study of formal logic is based on the fact that the validity

Regular Languages and Finite Automata

Regular Languages and Finite Automata 1 Introduction Hing Leung Department of Computer Science New Mexico State University Sep 16, 2010 In 1943, McCulloch and Pitts [4] published a pioneering work on a

Cryptography and Network Security Department of Computer Science and Engineering Indian Institute of Technology Kharagpur

Cryptography and Network Security Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Module No. # 01 Lecture No. # 05 Classic Cryptosystems (Refer Slide Time: 00:42)