# Logic will get you from A to B. Imagination will take you everywhere.

Save this PDF as:

Size: px
Start display at page:

Download "Logic will get you from A to B. Imagination will take you everywhere."

## Transcription

1 Chapter 3 Predicate Logic Logic will get you from A to B. Imagination will take you everywhere. A. Einstein In the previous chapter, we studied propositional logic. This chapter is dedicated to another type of logic, called predicate logic. Let us start with a motivating example. Example 1. Consider the following two statements: Every SCE student must study discrete mathematics. Jackson is an SCE student. It looks logical to deduce that therefore, Jackson must study discrete mathematics. However, this cannot be expressed by propositional logic...you may try it, but you can already notice that none of the logical operators we have learnt are applicable here. We need new tools! Definition 0. A predicate is a statement that contains variables (predicate variables), and they may be true or false depending on the values of these variables. Example. P (x) = x is greater than x is a predicate. It contains one predicate variable x. If we choose x = 1, P (1) is 1 is greater than 1, which is a proposition (always false). 77

2 78 CHAPTER 3. PREDICATE LOGIC Limitation Of Propositional Logic Every SCE student must study discrete mathematics Jackson is a SCE student So Jackson must study discrete mathematics This idea can t be expressed with propositional logic What propositional logic allows to express: If Jackson is a SCE student he must study discrete mathematics Jackson is a SCE student So Jackson must study discrete mathematics Predicates Is the statement x is greater than x a proposition? Define P(x) = x is greater than x. Is P(1) a proposition? P(1)= 1 is greater than 1 (F) A predicate is a statement that contains variables (predicate variables) and that may be true or false depending on the values of these variables. P(x) is a predicate.

3 Since a predicate takes value true or false once instantiated (that is, once its variables are taking values), we may alternatively say that a predicate instantiated becomes a proposition. It is needed to explicit which are the values that a predicate variable can possibly take. Definition 1. The domain of a predicate variable is the collection of all possible values that the variable may take. Example 3. Consider the predicate P (x) = x is greater than x. Then the domain of x could be for example the set Z of all integers. It could alternatively be the set R of real numbers. Whether instantiations of a predicate are true or false may depend on the domain considered. When several predicate variables are involved, they may or not have different domains. Example 4. Consider the predicate P (x, y) = x > y, in two predicate variables. We have Z (the set of integers) as domain for both of them. Take x = 4, y = 3, then P (4, 3) = 4 > 3, which is a proposition taking the value true. Take x = 1, y =, then P (1, ) = 1 >, which is a proposition taking the value false. Note that in general P (x, y) P (y, x)! We now introduce two quantifiers (describing parts or quantities from a domain), the universal quantification and the existential quantification. Definition. A universal quantification is a quantifier meaning given any or for all. We use the following symbol: (universal quantification) Example 5. Here is a formal way to say that for all values that a predicate variable x can take in a domain D, the predicate is true: For example x D, P (x) (is true) for all x belonging to D x R, x 0. for all x belonging to the real numbers 79

4 80 CHAPTER 3. PREDICATE LOGIC Predicate Instantiated/Domain A predicate is a statement that contains variables (predicate variables) and that may be true or false depending on the values of these variables. A predicate instantiated (where variables are evaluated in specific values) is a proposition. The domain of a predicate variable is the collection of all possible values that the variable may take. o e.g. the domain of x in P(x): integer o Different variables may have different domains. Predicate logic extends (is more powerful than) propositional logic. 4/1 Example Let P(x, y) = x > y. Domain: integers, i.e. both x and y are integers. P(4, 3) means 4 >3, so P(4, 3) is TRUE; P(1, ) means 1>, so P(1, ) is FALSE; P(3,4) is false (in general, P(x,y) and P(y,x) not equal).

5 81 Statements like Quantification Some birds are angry. On the internet, no one knows who you are. The square of any real number is nonnegative. Rovio Universal Quantification A universal quantification is a quantifier (something that tells the amount or quantity) meaning "given any" or "for all". Symbol: E.g. x D P(x) is true iff P(x) is true for every x in D. universal quantifiers, for all, for every - is a member (or) element of, belonging to D domain of predicate variable The square of any real number is nonnegative. x R, x 0.

6 8 CHAPTER 3. PREDICATE LOGIC Existential Quantification An existential quantification is a quantifier (something that tells the amount or quantity) meaning there exists, there is at least one or for some. Symbol: E.g. x D, P(x) is true iff P(x) is true for at least one x in D. existential quantifier, there exists Some birds are angry. D={birds}, P(x)= x is angry. 8/1 Nested Quantification (I) A proposition may contain multiple quantifiers All rabbits are faster than all tortoises. Domains: R={rabbits}, T={tortoises} Predicate C(x, y): Rabbit x is faster than tortoise y In symbols x R ( y T, C(x, y)) or x R, y T, C(x, y) In words For any rabbit x, and for any tortoise y, x is faster than y.

7 Definition 3. An existential quantification is a quantifier meaning there exists, there is at least one or for some. We use the following symbol: (existential quantification) Example 6. Here is a formal way to say that for some values that a predicate variable x can take in a domain D, the predicate is true: x D, P (x) (is true) for some x belonging to D For example, for D = { birds }, P (x) = x is angry, x Some D, P (x) (is true). birds are angry The term nested quantification refers to statements involving several quantifiers. Here is a series of examples. Example 7. All statements involve two predicate variables x and y, where x has for domain R = { rabbits }, while y has for domain T = { tortoises }. The predicate used is C(x, y) = Rabbit x is faster than tortoise y. In logic symbolism, we write All rabbits are faster than all tortoises : 83 x For any R, y rabbit x for any T tortoise y C(x, y) (is true) x is faster than y. In logic symbolism, we write Every rabbit is faster than some tortoise : x For any R, y rabbit x there is T a tortoise y C(x, y) (is true). such that x is faster than y In logic formalism, we write There is a rabbit which is faster than all tortoises : x There exists R, y a rabbit x such that for any T tortoise y C(x, y) (is true). x is faster than y

8 84 CHAPTER 3. PREDICATE LOGIC Nested Quantification (II) Another example Every rabbit is faster than some tortoise. Domains: R={rabbits}, T={tortoises}. Predicate C(x, y): Rabbit x is faster than tortoise y In symbols x R ( y T, C(x, y)) or x R, y T, C(x, y) In words: For any rabbit x, there exists a (some) tortoise y, such that x is faster than y. Nested Quantification (III) Another example There is a rabbit which is faster than all tortoises. Domains: R={rabbits}, T={ tortoises}. Predicate C(x, y): Rabbit x is faster than tortoise y. In symbols (note the ordering in nesting) x R ( y T, C(x, y)) In words: There exists a rabbit x, such that for any tortoise y, this rabbit x is faster than y.

9 The same way we assigned truth values to propositions, we may assign truth values to quantified statements. ( x D, P (x)) is true exactly when P (x) is true for every x D. Thus it is false whenever there is at least one x for which P (x) is false. Formally, for D = {x 1,..., x n }, we have the following equivalence: ( x D, P (x)) (P (x 1 ) P (x )... P (x n )). ( x D, P (x)) is true exactly when P (x) is true for at least one x D. Thus it is false when P (x) is false for all x D. Formally, for D = {x 1,..., x n }, we have the following equivalence: ( x D, P (x)) (P (x 1 ) P (x )... P (x n )). Since ( x D, P (x)) takes truth values, it can also be negated, that is ( x D, P (x)) x D, P (x), where the equivalence follows from the fact that ( x D, P (x)) is false whenever for all x D P (x) is false. Similarly ( x D, P (x)) x D, P (x) (3.1) since ( x D, P (x)) is false whenever there is one x D for which P (x) is false. See Exercise 3 for negating a statement involving several quantifiers. We can similarly assign truth values to combinations of predicates, or negation of combinations of predicates. The equivalence ( x D, P (x) Q(x)) x D, (P (x) Q(x)) holds, setting P (x) = P (x) Q(x) and using (3.1) on P (x). Now (P (x) Q(x)) is a proposition for any instantiation of x, thus we can apply De Morgan laws: ( x D, P (x) Q(x)) x D, P (x) Q(x). Suppose now you are given a statement involving quantifies, whose truth table has to be determined. There are several ways to do so: (1) Method of Exhaustion, () Method of Case, and (3) Method of Logic Derivation. Method of exhaustion: if the domain contains a small number of elements, try them all! For example, if D = {5, 6, 7, 8, 9}, and P (x) = x D, x = x, then just compute x for all the values of x D to conclude that this false. 85

10 86 CHAPTER 3. PREDICATE LOGIC Truth Value of Quantified Statements Statement When true When false x D,P(x) x D,P(x) P(x) is true for every x. There is one x for which P(x) is true. There is one x for which P(x) is false. P(x) is false for every x. Assume that D consists of x 1, x,, x n x D, P(x) P(x 1 ) P(x ) P(x n ) x D, P(x) P(x 1 ) P(x ) P(x n ) Negation of Quantification Not all SCE students study hard = There is at least one SCE student who does not study hard ( x D, P(x)) x D, P(x) Negation of a universal quantification becomes an existential quantification. It is not the case that some students in this class are from NUS. = All students in this class are not from NUS ( x D, P(x)) x D, P(x) Negation of an existential quantification becomes an universal quantification.

11 87 Negation of Quantification ( x D, P(x) Q(x)) x D (P(x) Q(x)) (negation of quantification) x D ( P(x) Q(x)) (DeMorgan) Example: Not all students in this class are using Facebook and (also) Google+ There is some (at least one) student in this class who is not using Facebook or not using Google+ (or may be using neither) How To Determine Truth Value Statement When true When false x D,P(x) x D,P(x) P(x) is true for every x. There is one x for which P(x) is true. There is one x for which P(x) is false. P(x) is false for every x. Systematic approaches: Method of exhaustion Method of case Method of logic derivation

12 88 CHAPTER 3. PREDICATE LOGIC Method of Exhaustion Let D={5,6,7,8,9}. Is x D, x=x true or false? 5=5 5, 6=36 6, 7=49 7,8=64 8, 9=81 9 So, false! Limitation? Domain may be too large to try out all options E.g., all integers Method of Case Positive examples to prove existential quantification Let Z denote all integers. Is x Z, x=x true or false? Take x =0 or 1 and we have it. True. Counterexample to disprove universal quantification Let R denote all reals. Is x R, x > x true or false? Take x=0.3 as a counterexample. False. Positive example is not a proof of universal quantification Negative example is not disproof of existential quantification May be hard to find suitable cases even if such cases do exist!

13 Method of Case. Suppose you want to show that the truth value of ( x, P (x)) is true. For this, you just need to find one case, one instantiation of x, for which P (x) is true. Example 8. P (x) = x Z, x = x is true, take x = 1 for example. Thus such an x exists. Similarly, if you want to show that the truth value of ( x, P (x)) is false, it is enough to find one counterexmaple. Example 9. P (x) = x R, x > x is false, take x = 0.3 for example. Thus P (x) cannot be true for all x. However, you cannot show that ( x, P (x)) is false using some examples, you need to prove that you cannot find a single x in your domain for which P (x) is true! Vice-versa, you cannot show that ( x, P (x)) is true by giving some examples, you need to show that this is always true, for every x in the domain considered. To do this, again, if the domain is small, one may use the exhaustion method of trying all options, but if the domain is big (or infinite), like Z, we need another method. Method of Logic Derivation. This method consists of using logical steps to transform one logical expression into another. Example 30. Suppose you want to know the truth value of x, (P (x) Q(x)), x has for domain D = {x 1,..., x n }. Then x, (P (x) Q(x)) is true if there is an x i D for which P (x i ) Q(x i ) is true that is ( x, (P (x) Q(x))) (P (x 1 ) Q(x 1 ))... (P (x n ) Q(x n )) but now this new expression becomes true exactly when at least one P (x i ) or Q(x j ) is true, that is ( x, (P (x) Q(x))) ( x, P (x)) ( x, Q(x)). When trying to derive logic steps involving quantifiers, one should be very careful with the ordering of the quantifiers...a typical example is that does not hold in general! x, y, P (x, y) y, x, P (x, y) Example 31. Consider the predicate P (x, y) = x admires y. Then x, y, P (x, y) means that everyone admires someone, while y, x, P (x, y) means that there exists one person who is admired by everyone! 89

14 90 CHAPTER 3. PREDICATE LOGIC Method Of Logic Derivation Consider an (arbitrary) domain with n members. Is x(p(x) Q(x)) logically equivalent to xp(x) xq(x)? x(p(x) Q(x)) [P(x1) Q(x1)] [P(xn) Q(xn)] [P(x1) P(xn)] [Q(x1) Q(xn)] xp(x) xq(x) Order Of Nesting Matters Is x y P(x,y) y x P(x,y) in general? LHS: y can vary with respect to x, y is fixed with respect to x Let P(x, y) = x admires y. LHS = Every person admires some people, RHS= Some people are admired by everyone Consider x,y R+, and let P(x,y) be xy=1. Is x y P(x,y) y x P(x,y)? Consider X={9, 10, 15}, Y={,3}. Let Q(x,y): y divides x Then, is x X y Y Q(x,y) y Y x X Q(x,y)?

15 We have seen so far how quantified statements have truth values, how they can be combined with an AND operator, or negated with a negation operator. We next discuss how they can be combined with the if then conditional operator: x D, (P (x) Q(x)) which means, for all x in its domain D, if P (x) is true, then Q(x) is true. Example 3. Take P (x) = x > 1, Q(x) = x > 1, and x has for domain the real numbers R. Then x R, (P (x) Q(x)) becomes for all x R, if x > 1 then x > 1. Attached to the conditional operator were several of its variations, its contrapositive, its inverse, and its negation. We can similarly define these for quantified statements. 91 x D, ( Q(x) P (x)) x D, (Q(x) P (x)) x D, ( P (x) Q(x)) contrapositive converse inverse See Exercise 5 for a proof that a conditional proposition is equivalent to its contrapositive. See also Exercise 6 to compute the negation of a conditional quantification, namely ( x, P (x) Q(x)).

16 9 CHAPTER 3. PREDICATE LOGIC Conditional Quantification (I) For all real number x, if x>1 then x >1 i.e., any real number greater than 1 has a square larger than 1 In symbolic form Let P(x) denote x>1 Let Q(x) denote x >1 Let R denote the domain, the collection of all real numbers x (P(x) Q(x)) Conditional Quantification (III) Given a conditional quantification Such as x A (P(x) Q(x)) Then we define contrapositive x A, Q(x) P(x) converse x A, Q(x) P(x) inverse x A, P(x) Q(x) Note: A conditional proposition is logically equivalent to its contrapositive

17 Our motivating Example 1 to start predicate logic was: Example 33. Consider the following two statements: (1) Every SCE student must study discrete mathematics. () Jackson is an SCE student. It looks logical to deduce that therefore, Jackson must study discrete mathematics. We will now develop inference rules, that will allow us to express this example (see Exercise 8). Consider a predicate variable x taking value in the domain D. Then x D, P (x); P (c) for any c D. As we did for propositional logic, we look at when the premises are true. When x, P (x) is true, P (x) is true for any choice of x in the domain D, in particular it is true for any choice of c, therefore P (c) is true for any c D. This rule says that if P (x) is true for any x in a domain, one is allowed to instantiate P (x) in x = c for any choice of c D. Example 34. Suppose we have the following premises: (1) No cat can catch Jerry; () Tom is a cat. We want to deduce that therefore Tom cannot catch Jerry. We define two predicates: Cat(x)= x is a cat, Catch(x)= x can catch Jerry. The second premise, Tom is a cat, is the easiest to write, it becomes: Cat(Tom)= Tom is a cat. Now for the first premise, suppose we want to say cats are catching Jerry, this would be if x is a cat, then x catches Jerry, that is Cat(x) Catch(x), keeping in mind that we have not yet assigned a quantifier to this statement. To say that cats are not catching Jerry, then this would be if x is cat, then x cannot catch Jerry, that is Cat(x) Catch(x), and finally, to say that no cat can catch Jerry, we add a universal quantifier: x(cat(x) Catch(x)). We then have the following premises in predicate logic: 1. x(cat(x) Catch(x));. Cat(Tom); We can then instantiate the first premise with x =Tom, to get (Cat(Tom) Catch(Tom)). 93

18 94 CHAPTER 3. PREDICATE LOGIC Universal Instantiation x P(x) P(c) where c is any element of the domain. Example: No cat can catch Jerry. Tom is a cat. Therefore Tom cannot catch Jerry. Cat(x): x is Cat, Catch(x): x can catch Jerry 1. x [Cat(x) Catch(x)] Hypothesis. Cat(Tom) Hypothesis 3. Cat(Tom) Catch(Tom) Universal Instantiation on 1 4. Catch(Tom) Modus ponens on and 3 William Hanna and Joseph Barbera Universal Generalization P(c) for any arbitrary c from the domain. x P(x) Example P(x) =``x is non-negative. P(c) for an arbitrary real c. Therefore P(x) for all x.

19 But since the second premise says that Cat(Tom); modus ponens (p q; p; q) tells us that therefore Catch(Tom). Consider a predicate variable x taking value in the domain D. Then P (c) for an arbitrary c D; x P (x) D. This just means that if a predicate is true for an arbitrary element c D, then it is true for all x D. Indeed, if whichever premise you look at is true, then the conclusion is true. This rule allows us to infer P (x) for all x D based on P (c) being true for an arbitrary instance c D. Example 35. Consider the premise for an arbitrary real number x, x 0. Therefore the square of any real number is non-negative. Set P (x) = x 0. In predicate logic, we have for any arbitrary c R, P (c). Therefore x P (x). In fact, we have already used this rule implicitly...in Exercise, we showed that if n is even, then n is even, for n an integer. The way we did it, is that we showed the result for one arbitrary n, and concluded this is true for all of them! See Exercise 9 for a more complicated example. Consider a predicate variable x taking value in the domain D. Then x D, P (x); P (c) for some c D. We look at when the premises are true. When x, P (x) is true, P (c) is true for at least one choice of c in the domain D. This rule allows to instantiate P (x) in some values of c for which P (c) is true. Example 36. The premises are: (1) if any student gets > 80 in the exam, then (s)he gets an A, () there are students who get > 80 in the exam, (3) Sam is such a student. We want to conclude that therefore Sam gets an A. Set the predicates A(x) = x gets an A, M(x) = x gets > 80 in the exam, the domain D is D = { students }. In predicate logic, (1) and () are respectively given by 1. x, M(x) A(x).. x, M(x). Now that Sam is such a student can be expressed using the above rule, to obtain M(Sam). Then we can instantiate 1. with Sam, to get M(Sam) A(Sam), which combined with M(Sam) leads to therefore A(Sam). 95

20 96 CHAPTER 3. PREDICATE LOGIC Existential Instantiation x P(x) P(c) for some c in the domain. Example If any student gets >80 in the final exam, then (s)he gets an A. There are students who get >80 in the final exam, Sam is such a student. Therefore, Sam gets an A. Domain={all students}; A(x)= x gets an A M(x) = x gets 80 in the final exam 1. x [M(x) A(x)] Hypothesis. x M(x) Hypothesis 3. M(Sam) Hypothesis +Existential instantiation 4. M(Sam) A(Sam) Universal instantiation on 1 5. A(Sam) Modus ponens on 4 and 3 P(c) Existential Generalization x P(x) for c some specific element of domain. Domain={all people}, Sell(x) = x is selling stocks. x Sell(x) x Sell(x) 1. x Sell(x) Hypothesis. Sell(c) Universal instantiation 3. x Sell(x) Existential generalization Example If everyone is selling stocks, then someone is selling stocks. belongs to the cartoonist

21 97 Consider a predicate variable x taking value in the domain D. Then P (c) for some specific c D; x, P (x). We look at when the premises are true. When P (c) is true for some specific c D, x for which P (x) is true. This rule allows to go from one instantiation P (c) to deduce that there is at least one x for which P (x) is true. Example 37. Suppose we want to show formally that if everyone is selling stocks, there must be someone selling stocks. Consider the predicate Sell(x)= x is selling stocks. Then we want to show that if x Sell(x) is true, then x Sell(x) is true as well. From x Sell(x) is true, we instantiate it in one c in the domain (here the domain is { people }). This gives Sell(c). Now using the above rule, we know there must exist at least an x for which Sell(x) is true, as desired. These 4 rules may look either obvious or contrived, but they are needed to write down things formally, in particular whenever formal methods are involved, e.g., if you need to program formal verifications! We now come to the last part of predicate logic, namely, we will see how to apply the logic rules we have seen to justify different proof techniques. We will discuss three proof technique: direct proof, induction, proof by contradiction. Direct Proof. As the name suggests, these are proofs that are perfomed without particular techniques. Some claim has to be shown, and there is a specific way to do so in this particular context. Example 38. Suppose we want to show that n i=0 i = n(n+1), n N. Write down the following array: n 1 n n n 1 n... 1 If you sum up the entries of the first row, you get n i=0 i, and if you sum up the entries of the second row, you also get n i=0 i, Thus if you sum up all entries in this array, you get n i=0 i. Now if we sum up the first column, we get n + 1, if we sum up the second column we get n + 1,..., and for the nth column we also get n + 1, so the total is n + 1 times n columns.

22 98 CHAPTER 3. PREDICATE LOGIC Proof Techniques A valid proof is a valid argument, i.e. the conclusion follows from the given assumptions. Three techniques: Direct proof Proof by induction Proof by contradiction. Proof by example: The author gives only the case n = and suggests that it contains most of the ideas of the general proof. Proof by intimidation: 'Trivial.' Proof by cumbersome notation: Best done with access to at least four alphabets and special symbols. Proof by exhaustion: An issue or two of a journal devoted to your proof is useful. Proof by omission: 'The reader may easily supply the details.' 'The other 53 cases are analogous.' '... Proof by obfuscation: A long plotless sequence of true and/or meaningless syntactically related statements. Proof by wishful citation: The author cites the negation, converse, or generalization of a theorem from literature to support his claims. PROOF TECHNIQUES by A. H. Zemanian, The Physics Teacher, May Proof Technique: Direct Proof Prove that Define Note: Sum up: Thus: S i n n Z, i n i 0 n S i i 0 i 0 n ( n 1) n -1 n n+1 terms n n S n n... n n n n ( n 1) S n+1 terms S (n 1) n Leonhard Euler ( )

23 If we sum up the elements horizontally, we got n i=0 i, while if we sum up the elements vertically, we got n(n + 1). But the sum does not change when we count differently, thus: 99 n i = i=0 n(n + 1). The legend attributes this proof technique to young Euler, who apparently was punished for not behaving in the classroom. His teacher would have asked him to compute the sum of integers from 1 to 100, and Euler would have, or so the legend says, came up with this technique so as to have to compute all the additions! Mathematical Induction. This is a proof technique to show statements of the form n, P (n). We first explain the technique, then give a proof of why this proof technique is valid, and finally provide an example. A proof by mathematical induction follows two steps: 1. Basis step: You need to show that P (1) is true.. Inductive step: You assume that P (k) is true, and have to prove that P (k + 1) is then true. When both steps are complete, we have proved that n, P (n) is true. Why is that the case? From the inductive step, we have that P (k) P (k + 1) for any k, therefore this is true for when we instantiate in k = 1, that is P (1) P (). But from the basis step, we know that P (1) is true, thus combining (P (1) P ()); P (1); we get that therefore P () holds. We can repeat this process with k = to deduce that P (3) holds, and so on and so forth.

24 100 CHAPTER 3. PREDICATE LOGIC Mathematical Induction Prove propositions of the form: n P(n) The proof consists of two steps: Basis Step: The proposition P(1) is shown to be true Inductive Step: Assume P(k) is true (when n=k), then, prove P(k+1) is true (when n=k+1). When both steps are complete, we have proved that " n P(n) is true Why Does it Work? From step : P(1) P() by Universal Instantiation. From step 1: P(1) Applying modus ponen: P(). Repeat the process to get P(3),P(4), P(5), etc. So, all P(k) are true! i.e., k P(k) Analogy with climbing Ladders. Inductive step [P(1) k(p(k) P(k+1))] np(n) Basis Hypothesis (valid argument)

25 Example 39. We want to prove n i = i=0 n(n + 1), n N using mathematical induction. Then set n n(n + 1) P (n) = i =, n N. i=0 Basis step: P (1) = 1 = 1(1+1). 101 Inductive step: suppose that P (k) is true, that is k i=0 i = k(k+1) is true for all k. Now we need to show that P (k + 1) holds. k+1 i = i=0 = = There P (n) is true for all n. k i + (k + 1) i=0 k(k + 1) + (k + 1) k(k + 1) + (k + 1) = (k + 1)(k + ). Proof by Contradiction. We want to prove that P (n) Q(n) is true. In a proof by contradiction, we assume by contradiction that P (n) Q(n) is false, that is, that (P (n) Q(n)) is true. The only way this might happen, is if P (n) is true and Q(n) is false. Thus we start with P (n) true and Q(n) false. If from there we deduce a contradiction, that is a statement of the form C C, which is always false, what we have proven is (P (n) Q(n)) C C is true. This is equivalent to P (n) Q(n). To see that, set S(n) = P (n) Q(n), and look at the truth table: S C S C C ( S) (C C) T T F F T T F F F T F T T F F F F T F F

26 10 CHAPTER 3. PREDICATE LOGIC Therefore, to prove P (n) Q(n) (or any other statement), it suffices to instead prove the conditional statement (P (n) Q(n)) C C, which is done by direct proof, by assuming (P (n) Q(n)) and deduce C C. One difficulty is to figure out what is C given the proof to effectuate. Example 40. Suppose we want to prove that: if n is even, then n is even, for n integer. Set P (n)= n is even, and Q(n)= n is even. We want to prove that P (n) Q(n), which is equivalent to (P (n) Q(n)) C C. Suppose (P (n) Q(n)), that means P (n) is true and Q(n) is false: n is even, and n is not even (equivalently n is odd). Now if n is odd, then n = k + 1, and n = (k + 1) = 4k + 4k + 1 = (k + k) + 1, that is n is odd. Thus C = n is even, and we have just shown that n is odd, that is C C, a contradiction! We may alternatively use that P (n) Q(n) is equivalent to Q(n) P (n). This would be a proof using contrapositive. Example 41. Suppose we want to prove that: if n is even, then n is even, for n integer. Set P (n)= n is even, and Q(n)= n is even. We want to prove that P (n) Q(n), which is equivalent to Q(n) P (n). Suppose that Q(n), that is: n is not even, or n is odd. Now if n is odd, then n = k + 1, and n = (k + 1) = 4k + 4k + 1 = (k + k) + 1, that is n is odd, which is equivalent to P (n), which concludes the proof.

27 103 Example: Mathematical Induction n Prove that n ( n 1) n N, i i 0 n n ( n 1) Let P(n) denote i i 0 Inductive step. Assume P(k) true, k>0: Prove P(k+1) true : k 1 k k( k 1) i i ( k 1) ( k 1) i ( k 1)( k ) ( k 1)[( k 1) 1] i 0 0 Basis step: P(1) is true 1(1 1) 1 k k( k 1) i i 0 So, P(n) is true for n=k+1 and thus true for all n: n P(n) is true Proof Technique: Contradiction Prove that: If n is even, then n is even, for n integer. Lets assume: n is even but n is not even (assume the negation of the statement is true). n is not even n is odd, i.e., n = k+1, k integer. Then n = (k+1) = 4k + 4k + 1 = (k + k ) + 1 (odd) This is in contradiction with the assumption. Hence, the assumption is false, thus the negation of the assumption is true.

28 104 CHAPTER 3. PREDICATE LOGIC Exercises for Chapter 3 Exercise 0. Consider the predicates M(x, y) = x has sent an to y, and T (x, y) = x has called y. The predicate variables x, y take values in the domain D = {students in the class}. Express these statements using symbolic logic. 1. There are at least two students in the class such that one student has sent the other an , and the second student has called the first student.. There are some students in the class who have ed everyone. Exercise 1. Consider the predicate C(x, y) = x is enrolled in the class y, where x takes values in the domain S = {students}, and y takes values in the domain D = {courses}. Express each statement by an English sentence. 1. x S, C(x, MH181).. y D, C(Carol, y). 3. x S, (C(x, MH181) C(x, CZ00)). 4. x S, x S, y D, ((x x ) (C(x, y) C(x, y))). Exercise. Consider the predicate P (x, y, z) = xyz = 1, for x, y, z R, x, y, z > 0. What are the truth values of these statements? Justify your answer. 1. x, y, z, P (x, y, z).. x, y, z, P (x, y, z). 3. x, y, z, P (x, y, z). 4. x, y, z, P (x, y, z). Exercise Express ( x, y, P (x, y)) in terms of existential quantification.

29 105. Express ( x, y, P (x, y)) in terms of universal quantification. Exercise 4. Consider the predicate C(x, y) = x is enrolled in the class y, where x takes values in the domain S = {students}, and y takes values in the domain C = {courses}. Form the negation of these statements: 1. x, (C(x, MH181) C(x, CZ00)).. x y, z, ((x y) (C(x, z) C(y, z))). Exercise 5. Show that x D, P (x) Q(x) is equivalent to its contrapositive. Exercise 6. Show that ( x, P (x) Q(x)) x, P (x) Q(x). Exercise 7. Let y, z be positive integers. y, z, (y = z (y is prime)). What is the truth value of Exercise 8. Write in symbolic logic Every SCE student studies discrete mathematics. Jackson is an SCE student. Therefore Jackson studies discrete mathematics. Exercise 9. Here is an optional exercise about universal generalization. Consider the following two premises: (1) for any number x, if x > 1 then x 1 > 0, () every number in D is greated than 1. Show that therefore, for every number x in D, x 1 > 0. Exercise 30. Let q be a positive real number. Prove or disprove the following statement: if q is irrational, then q is irrational. Exercise 31. Prove using mathematical induction that the sum of the first n odd positive integers is n. Exercise 3. Prove using mathematical induction that n 3 n is divisible by 3 whenever n is a positive integer.

30 106 CHAPTER 3. PREDICATE LOGIC

### 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 W W L CHEN

DISCRETE MATHEMATICS W W L CHEN c W W L Chen, 1982, 2008. This chapter originates from material used by the author at Imperial College, University of London, between 1981 and 1990. It is available free

### Proof: A logical argument establishing the truth of the theorem given the truth of the axioms and any previously proven theorems.

Math 232 - Discrete Math 2.1 Direct Proofs and Counterexamples Notes Axiom: Proposition that is assumed to be true. Proof: A logical argument establishing the truth of the theorem given the truth of the

### Chapter I Logic and Proofs

MATH 1130 1 Discrete Structures Chapter I Logic and Proofs Propositions A proposition is a statement that is either true (T) or false (F), but or both. s Propositions: 1. I am a man.. I am taller than

### 31 is a prime number is a mathematical statement (which happens to be true).

Chapter 1 Mathematical Logic In its most basic form, Mathematics is the practice of assigning truth to welldefined statements. In this course, we will develop the skills to use known true statements to

Section 5.1 Climbing an Infinite Ladder Suppose we have an infinite ladder and the following capabilities: 1. We can reach the first rung of the ladder. 2. If we can reach a particular rung of the ladder,

### CS 2336 Discrete Mathematics

CS 2336 Discrete Mathematics Lecture 2 Logic: Predicate Calculus 1 Outline Predicates Quantifiers Binding Applications Logical Equivalences 2 Predicates In mathematics arguments, we will often see sentences

### Predicate Logic. Example: All men are mortal. Socrates is a man. Socrates is mortal.

Predicate Logic Example: All men are mortal. Socrates is a man. Socrates is mortal. Note: We need logic laws that work for statements involving quantities like some and all. In English, the predicate is

### 1.4/1.5 Predicates and Quantifiers

1.4/1.5 Predicates and Quantifiers Can we express the following statement in propositional logic? Every computer has a CPU No Outline: Predicates Quantifiers Domain of Predicate Translation: Natural Languauge

### DISCRETE MATH: LECTURE 4

DISCRETE MATH: LECTURE 4 DR. DANIEL FREEMAN 1. Chapter 3.1 Predicates and Quantified Statements I A predicate is a sentence that contains a finite number of variables and becomes a statement when specific

### INTRODUCTION TO PROOFS: HOMEWORK SOLUTIONS

INTRODUCTION TO PROOFS: HOMEWORK SOLUTIONS STEVEN HEILMAN Contents 1. Homework 1 1 2. Homework 2 6 3. Homework 3 10 4. Homework 4 16 5. Homework 5 19 6. Homework 6 21 7. Homework 7 25 8. Homework 8 28

### 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

### LOGICAL INFERENCE & PROOFs. Debdeep Mukhopadhyay Dept of CSE, IIT Madras

LOGICAL INFERENCE & PROOFs Debdeep Mukhopadhyay Dept of CSE, IIT Madras Defn A theorem is a mathematical assertion which can be shown to be true. A proof is an argument which establishes the truth of a

### CSI 2101 / Rules of Inference ( 1.5)

CSI 2101 / Rules of Inference ( 1.5) Introduction what is a proof? Valid arguments in Propositional Logic equivalence of quantified expressions Rules of Inference in Propositional Logic the rules using

### 2.4 Mathematical Induction

2.4 Mathematical Induction What Is (Weak) Induction? The Principle of Mathematical Induction works like this: What Is (Weak) Induction? The Principle of Mathematical Induction works like this: We want

### 1.3 Induction and Other Proof Techniques

4CHAPTER 1. INTRODUCTORY MATERIAL: SETS, FUNCTIONS AND MATHEMATICAL INDU 1.3 Induction and Other Proof Techniques The purpose of this section is to study the proof technique known as mathematical induction.

### Section 1. Statements and Truth Tables. Definition 1.1: A mathematical statement is a declarative sentence that is true or false, but not both.

M3210 Supplemental Notes: Basic Logic Concepts In this course we will examine statements about mathematical concepts and relationships between these concepts (definitions, theorems). We will also consider

### Math 55: Discrete Mathematics

Math 55: Discrete Mathematics UC Berkeley, Fall 2011 Homework # 1, due Wedneday, January 25 1.1.10 Let p and q be the propositions The election is decided and The votes have been counted, respectively.

### CS 441 Discrete Mathematics for CS Lecture 5. Predicate logic. CS 441 Discrete mathematics for CS. Negation of quantifiers

CS 441 Discrete Mathematics for CS Lecture 5 Predicate logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Negation of quantifiers English statement: Nothing is perfect. Translation: x Perfect(x)

### Example. Statements in Predicate Logic. Predicate Logic. Predicate Logic. (Rosen, Chapter ) P(x,y) TOPICS. Predicate Logic

Predicate Logic Predicate Logic (Rosen, Chapter 1.4-1.6) TOPICS Predicate Logic Quantifiers Logical Equivalence Predicate Proofs n Some statements cannot be expressed in propositional logic, such as: n

### Even Number: An integer n is said to be even if it has the form n = 2k for some integer k. That is, n is even if and only if n divisible by 2.

MATH 337 Proofs Dr. Neal, WKU This entire course requires you to write proper mathematical proofs. All proofs should be written elegantly in a formal mathematical style. Complete sentences of explanation

### Inference Rules and Proof Methods

Inference Rules and Proof Methods Winter 2010 Introduction Rules of Inference and Formal Proofs Proofs in mathematics are valid arguments that establish the truth of mathematical statements. An argument

### MATH 55: HOMEWORK #2 SOLUTIONS

MATH 55: HOMEWORK # SOLUTIONS ERIC PETERSON * 1. SECTION 1.5: NESTED QUANTIFIERS 1.1. Problem 1.5.8. Determine the truth value of each of these statements if the domain of each variable consists of all

### Review Name Rule of Inference

CS311H: Discrete Mathematics Review Name Rule of Inference Modus ponens φ 2 φ 2 Modus tollens φ 2 φ 2 Inference Rules for Quantifiers Işıl Dillig Hypothetical syllogism Or introduction Or elimination And

### 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

### Elementary Number Theory and Methods of Proof. CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.

Elementary Number Theory and Methods of Proof CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.edu/~cse215 1 Number theory Properties: 2 Properties of integers (whole

### 1.4 Predicates and Quantifiers

Can we express the following statement in propositional logic? Every computer has a CPU No 1 Can we express the following statement in propositional logic? No Every computer has a CPU Let's see the new

### What is logic? Propositional Logic. Negation. Propositions. This is a contentious question! We will play it safe, and stick to:

Propositional Logic This lecture marks the start of a new section of the course. In the last few lectures, we have had to reason formally about concepts. This lecture introduces the mathematical language

### Mathematical Induction

Mathematical Induction MAT30 Discrete Mathematics Fall 016 MAT30 (Discrete Math) Mathematical Induction Fall 016 1 / 19 Outline 1 Mathematical Induction Strong Mathematical Induction MAT30 (Discrete Math)

### 1.5 Methods of Proof INTRODUCTION

1.5 Methods of Proof INTRODUCTION Icon 0049 Two important questions that arise in the study of mathematics are: (1) When is a mathematical argument correct? (2) What methods can be used to construct mathematical

### A set is a Many that allows itself to be thought of as a One. (Georg Cantor)

Chapter 4 Set Theory A set is a Many that allows itself to be thought of as a One. (Georg Cantor) In the previous chapters, we have often encountered sets, for example, prime numbers form a set, domains

### 3.3. INFERENCE 105. Table 3.5: Another look at implication. p q p q T T T T F F F T T F F T

3.3. INFERENCE 105 3.3 Inference Direct Inference (Modus Ponens) and Proofs We concluded our last section with a proof that the sum of two even numbers is even. That proof contained several crucial ingredients.

### Predicate Logic. Lucia Moura. Winter Predicates and Quantifiers Nested Quantifiers Using Predicate Calculus

Predicate Logic Winter 2010 Predicates A Predicate is a declarative sentence whose true/false value depends on one or more variables. The statement x is greater than 3 has two parts: the subject: x is

### 3.3 Proofs Involving Quantifiers

3.3 Proofs Involving Quantifiers 1. In exercise 6 of Section 2.2 you use logical equivalences to show that x(p (x) Q(x)) is equivalent to xp (x) xq(x). Now use the methods of this section to prove that

Mathematics 0N1 Solutions 1 1. Write the following sets in list form. 1(i) The set of letters in the word banana. {a, b, n}. 1(ii) {x : x 2 + 3x 10 = 0}. 3(iv) C A. True 3(v) B = {e, e, f, c}. True 3(vi)

### Chapter 1, Part II: Predicate Logic. With Question/Answer Animations

Chapter 1, Part II: Predicate Logic With Question/Answer Animations Summary Predicate Logic (First-Order Logic (FOL) The Language of Quantifiers Logical Equivalences Nested Quantifiers Translation from

### Midterm Examination 1 with Solutions - Math 574, Frank Thorne Thursday, February 9, 2012

Midterm Examination 1 with Solutions - Math 574, Frank Thorne (thorne@math.sc.edu) Thursday, February 9, 2012 1. (3 points each) For each sentence below, say whether it is logically equivalent to the sentence

### 1.5 Rules of Inference

1.5 Rules of Inference (Inference: decision/conclusion by evidence/reasoning) Introduction Proofs are valid arguments that establish the truth of statements. An argument is a sequence of statements that

### 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

### Mathematical Induction

Mathematical Induction Victor Adamchik Fall of 2005 Lecture 2 (out of three) Plan 1. Strong Induction 2. Faulty Inductions 3. Induction and the Least Element Principal Strong Induction Fibonacci Numbers

### 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

### Predicate in English. Predicates and Quantifiers. Propositional Functions: Prelude. Predicate in Logic. Propositional Function

Predicates and Quantifiers By Chuck Cusack Predicate in English In English, a sentence has 2 parts: the subject and the predicate. The predicate is the part of the sentence that states something about

### Mathematical induction. Niloufar Shafiei

Mathematical induction Niloufar Shafiei Mathematical induction Mathematical induction is an extremely important proof technique. Mathematical induction can be used to prove results about complexity of

### We now explore a third method of proof: proof by contradiction.

CHAPTER 6 Proof by Contradiction We now explore a third method of proof: proof by contradiction. This method is not limited to proving just conditional statements it can be used to prove any kind of statement

### Propositional Logic. A proposition is a declarative sentence (a sentence that declares a fact) that is either true or false, but not both.

irst Order Logic Propositional Logic A proposition is a declarative sentence (a sentence that declares a fact) that is either true or false, but not both. Are the following sentences propositions? oronto

### Undergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics. Introductory Notes in Discrete Mathematics

Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics Introductory Notes in Discrete Mathematics Marcel B. Finan c All Rights Reserved Last Updated April 6, 2016 Preface

### 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)

### Predicates and Quantifiers. Niloufar Shafiei

Predicates and Quantifiers Niloufar Shafiei Review Proposition: 1. It is a sentence that declares a fact. 2. It is either true or false, but not both. Examples: 2 + 1 = 3. True Proposition Toronto is the

### Exam 1 Answers: Logic and Proof

Q250 FALL 2012, INDIANA UNIVERSITY Exam 1 Answers: Logic and Proof September 17, 2012 Instructions: Please answer each question completely, and show all of your work. Partial credit will be awarded where

### Discrete Mathematics, Chapter 5: Induction and Recursion

Discrete Mathematics, Chapter 5: Induction and Recursion Richard Mayr University of Edinburgh, UK Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 5 1 / 20 Outline 1 Well-founded

### Discrete Mathematics, Chapter : Predicate Logic

Discrete Mathematics, Chapter 1.4-1.5: Predicate Logic Richard Mayr University of Edinburgh, UK Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 1.4-1.5 1 / 23 Outline 1 Predicates

### n logical not (negation) n logical or (disjunction) n logical and (conjunction) n logical exclusive or n logical implication (conditional)

Discrete Math Review Discrete Math Review (Rosen, Chapter 1.1 1.6) TOPICS Propositional Logic Logical Operators Truth Tables Implication Logical Equivalence Inference Rules What you should know about propositional

### MAT2400 Analysis I. A brief introduction to proofs, sets, and functions

MAT2400 Analysis I A brief introduction to proofs, sets, and functions In Analysis I there is a lot of manipulations with sets and functions. It is probably also the first course where you have to take

### Logic, Sets, and Proofs

Logic, Sets, and Proofs David A. Cox and Catherine C. McGeoch Amherst College 1 Logic Logical Statements. A logical statement is a mathematical statement that is either true or false. Here we denote logical

### Mathematical Induction. Rosen Chapter 4.1 (6 th edition) Rosen Ch. 5.1 (7 th edition)

Mathematical Induction Rosen Chapter 4.1 (6 th edition) Rosen Ch. 5.1 (7 th edition) Mathmatical Induction Mathmatical induction can be used to prove statements that assert that P(n) is true for all positive

### 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

### Problems on Discrete Mathematics 1

Problems on Discrete Mathematics 1 Chung-Chih Li 2 Kishan Mehrotra 3 L A TEX at July 18, 2007 1 No part of this book can be reproduced without permission from the authors 2 Illinois State University, Normal,

### Chapter 1 LOGIC AND PROOF

Chapter 1 LOGIC AND PROOF To be able to understand mathematics and mathematical arguments, it is necessary to have a solid understanding of logic and the way in which known facts can be combined to prove

### 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

### Discrete Mathematics Lecture 3 Elementary Number Theory and Methods of Proof. Harper Langston New York University

Discrete Mathematics Lecture 3 Elementary Number Theory and Methods of Proof Harper Langston New York University Proof and Counterexample Discovery and proof Even and odd numbers number n from Z is called

### def: An axiom is a statement that is assumed to be true, or in the case of a mathematical system, is used to specify the system.

Section 1.5 Methods of Proof 1.5.1 1.5 METHODS OF PROOF Some forms of argument ( valid ) never lead from correct statements to an incorrect. Some other forms of argument ( fallacies ) can lead from true

### Math 3000 Section 003 Intro to Abstract Math Homework 2

Math 3000 Section 003 Intro to Abstract Math Homework 2 Department of Mathematical and Statistical Sciences University of Colorado Denver, Spring 2012 Solutions (February 13, 2012) Please note that these

### This section demonstrates some different techniques of proving some general statements.

Section 4. Number Theory 4.. Introduction This section demonstrates some different techniques of proving some general statements. Examples: Prove that the sum of any two odd numbers is even. Firstly you

### If f is a 1-1 correspondence between A and B then it has an inverse, and f 1 isa 1-1 correspondence between B and A.

Chapter 5 Cardinality of sets 51 1-1 Correspondences A 1-1 correspondence between sets A and B is another name for a function f : A B that is 1-1 and onto If f is a 1-1 correspondence between A and B,

### Problems on Discrete Mathematics 1

Problems on Discrete Mathematics 1 Chung-Chih Li Kishan Mehrotra 3 L A TEX at July 18, 007 1 No part of this book can be reproduced without permission from the authors Illinois State University, Normal,

### 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

### 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

### Mathematical induction & Recursion

CS 441 Discrete Mathematics for CS Lecture 15 Mathematical induction & Recursion Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Proofs Basic proof methods: Direct, Indirect, Contradiction, By Cases,

### Discrete Mathematics Lecture 1 Logic of Compound Statements. Harper Langston New York University

Discrete Mathematics Lecture 1 Logic of Compound Statements Harper Langston New York University Administration Class Web Site http://cs.nyu.edu/courses/summer05/g22.2340-001/ Mailing List Subscribe at

### Foundations of Computing Discrete Mathematics Solutions to exercises for week 2

Foundations of Computing Discrete Mathematics Solutions to exercises for week 2 Agata Murawska (agmu@itu.dk) September 16, 2013 Note. The solutions presented here are usually one of many possiblities.

### Chapter 1. Use the following to answer questions 1-5: In the questions below determine whether the proposition is TRUE or FALSE

Use the following to answer questions 1-5: Chapter 1 In the questions below determine whether the proposition is TRUE or FALSE 1. 1 + 1 = 3 if and only if 2 + 2 = 3. 2. If it is raining, then it is raining.

### 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

Lecture Notes in Discrete Mathematics Marcel B. Finan Arkansas Tech University c All Rights Reserved 2 Preface This book is designed for a one semester course in discrete mathematics for sophomore or junior

### Students in their first advanced mathematics classes are often surprised

CHAPTER 8 Proofs Involving Sets Students in their first advanced mathematics classes are often surprised by the extensive role that sets play and by the fact that most of the proofs they encounter are

### Mathematical Induction

Chapter 2 Mathematical Induction 2.1 First Examples Suppose we want to find a simple formula for the sum of the first n odd numbers: 1 + 3 + 5 +... + (2n 1) = n (2k 1). How might we proceed? The most natural

### Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 1

CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 1 Course Outline CS70 is a course on Discrete Mathematics and Probability for EECS Students. The purpose of the course

### CHAPTER 1 The Foundations: Logic and Proof, Sets, and Functions

Section 1.5 Methods of Proof 1 CHAPTER 1 The Foundations: Logic and Proof, Sets, and Functions SECTION 1.5 Methods of Proof Learning to construct good mathematical proofs takes years. There is no algorithm

### Oh Yeah? Well, Prove It.

Oh Yeah? Well, Prove It. MT 43A - Abstract Algebra Fall 009 A large part of mathematics consists of building up a theoretical framework that allows us to solve problems. This theoretical framework is built

### Direct Proofs. CS 19: Discrete Mathematics. Direct Proof: Example. Indirect Proof: Example. Proofs by Contradiction and by Mathematical Induction

Direct Proofs CS 19: Discrete Mathematics Amit Chakrabarti Proofs by Contradiction and by Mathematical Induction At this point, we have seen a few examples of mathematical proofs. These have the following

### Lecture 3. Mathematical Induction

Lecture 3 Mathematical Induction Induction is a fundamental reasoning process in which general conclusion is based on particular cases It contrasts with deduction, the reasoning process in which conclusion

### The last three chapters introduced three major proof techniques: direct,

CHAPTER 7 Proving Non-Conditional Statements The last three chapters introduced three major proof techniques: direct, contrapositive and contradiction. These three techniques are used to prove statements

### Logical Inference and Mathematical Proof

Logical Inference and Mathematical Proof CSE 191, Class Note 03: Logical Inference and Mathematical Proof Computer Sci & Eng Dept SUNY Buffalo c Xin He (University at Buffalo) CSE 191 Discrete Structures

### Mathematical Induction

Chapter 4 Mathematical Induction 4.1 The Principle of Mathematical Induction Preview Activity 1 (Exploring Statements of the Form.8n N/.P.n//) One of the most fundamental sets in mathematics is the set

### 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

### CSE 191, Class Note 01 Propositional Logic Computer Sci & Eng Dept SUNY Buffalo

Propositional Logic CSE 191, Class Note 01 Propositional Logic Computer Sci & Eng Dept SUNY Buffalo c Xin He (University at Buffalo) CSE 191 Discrete Structures 1 / 37 Discrete Mathematics What is Discrete

### Proofs Crash Course. Winter 2011

Proofs Crash Course Winter 2011 Today s Topics O Why are Proofs so Hard? O Proof by Deduction O Proof by Contrapositive O Proof by Contradiction O Proof by Induction Why are Proofs so Hard? If it is a

### Announcements. CS243: Discrete Structures. More on Cryptography and Mathematical Induction. Agenda for Today. Cryptography

Announcements CS43: Discrete Structures More on Cryptography and Mathematical Induction Işıl Dillig Class canceled next Thursday I am out of town Homework 4 due Oct instead of next Thursday (Oct 18) Işıl

### 1 Proposition, Logical connectives and compound statements

Discrete Mathematics: Lecture 4 Introduction to Logic Instructor: Arijit Bishnu Date: July 27, 2009 1 Proposition, Logical connectives and compound statements Logic is the discipline that deals with the

### Predicate Calculus. There are certain arguments that seem to be perfectly logical, yet they cannot be expressed by using propositional calculus.

Predicate Calculus (Alternative names: predicate logic, first order logic, elementary logic, restricted predicate calculus, restricted functional calculus, relational calculus, theory of quantification,

### Logic and Proofs. Chapter 1

Section 1.0 1.0.1 Chapter 1 Logic and Proofs 1.1 Propositional Logic 1.2 Propositional Equivalences 1.3 Predicates and Quantifiers 1.4 Nested Quantifiers 1.5 Rules of Inference 1.6 Introduction to Proofs

### Logic and Proof Solutions. Question 1

Logic and Proof Solutions Question 1 Which of the following are true, and which are false? For the ones which are true, give a proof. For the ones which are false, give a counterexample. (a) x is an odd

### 2.1.1 Examples of Sets and their Elements

Chapter 2 Set Theory 2.1 Sets The most basic object in Mathematics is called a set. As rudimentary as it is, the exact, formal definition of a set is highly complex. For our purposes, we will simply define

### Definition 10. A proposition is a statement either true or false, but not both.

Chapter 2 Propositional Logic Contrariwise, continued Tweedledee, if it was so, it might be; and if it were so, it would be; but as it isn t, it ain t. That s logic. (Lewis Carroll, Alice s Adventures

### The set consisting of all natural numbers that are in A and are in B is the set f1; 3; 5g;

Chapter 5 Set Theory 5.1 Sets and Operations on Sets Preview Activity 1 (Set Operations) Before beginning this section, it would be a good idea to review sets and set notation, including the roster method

### 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

### Review for Final Exam

Review for Final Exam Note: Warning, this is probably not exhaustive and probably does contain typos (which I d like to hear about), but represents a review of most of the material covered in Chapters

### Section 3 Sequences and Limits

Section 3 Sequences and Limits Definition A sequence of real numbers is an infinite ordered list a, a 2, a 3, a 4,... where, for each n N, a n is a real number. We call a n the n-th term of the sequence.

### 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