Mathematical Induction

Save this PDF as:

Size: px
Start display at page:

Transcription

1 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 could we do this? One way: Show that ~( x)(n 1 x! 1 x) is inconsistent (obviously, you would need further premises). Another way: Certain infinite sets, such as N, have internal structure that we can use. his use constitutes the method of mathematical induction. he Principle of Mathematical Induction. Let S be any set of natural numbers. he principle says that I the following are true: (i) (ii) 1 " S, AND or any natural number x, if every number less than x is in S, then x " S, HEN it is also the case that: (iii) Every natural number is in S; i.e.: ( x)(n 1 x! S 1 x) i.e.: N " S EXAMPLES: he undamental heorem of Arithmetic entails that every natural number is prime or is the product of some collection of prime numbers. We can see that this is true at the beginning:

2 1 = 1 2 = 2 3 = 3 4 = 2*2 5 = 5 6 = 2*3 7 = 7 8 = 2*2*2 9 = 3*3 10 = 2*5 11 = = 2*2*3 But how can we show this is always so? heorem: Every natural number is prime or is the product of some collection of prime numbers. Proof. We will prove this claim by mathematical induction. Let [1] P = {x: x is prime or x is the product of some collection of primes}. (In other words, we are instantiating the S of the principle of induction to the set P. We want to show that every element in N is also an element of P.) Base: 1 " P. We have seen that this is true. So we have established step (i). Induction: We now want to show: [2] for any natural number x, if all the numbers less than x are in P, then x " P. We will show this by instantiating x to an arbitrary natural number n. (i.e., for all we know, n could be any natural number.) his means that we want to show: [3] if every number less than n is in P, then n " P. Obviously, if some numbers less than n are not in P, the conditional is true. So let's suppose that [IH] every number less than n is in P. (his is called the "Induction Hypothesis".) Now we must show that n is also in P.

3 We turn now to the details of the proof. here are two possibilities: n is prime or n is not prime. Case one: n is prime. In this case, n is automatically a member of P. Case two: n is not prime. In this case, there are numbers y and z such that (y*z) = n, where 1 < y & z < n. Note that both y and z must be less than n if they are to be numbers that "make" n composite (i.e. not prime). Since both y and z are less than n, by the induction hypothesis [IH], both y and z are in P. So each one of them is a prime of the product of a set of primes. So we have y = a 1 *a 2 *... *a j z = b 1 *b 2 *... *b k, where all the as and bs are prime numbers. (j and k are natural numbers, too; if y is prime, then j = 1 (and there is no a 2, etc.). Similarly, if z is prime, then k = 1.) But since y * z = n, it follows that a 1 *a 2 *... *a j *b 1 *b 2 *... *b k, = n So n is the product of some set of prime numbers. hus, we have shown that if [IH] is true, then [3] is true. But n was arbitrary (it could have been any number), so the strategy we used would work for every number, so we have shown step (ii). Since we have shown steps (i) and (ii) for our target set P, the principle of mathematical induction allows us to conclude that all every natural number is in P. In other words, we have just shown that every natural number is either prime or is the product of some collection of primes. heorem: he sum of the first n odd numbers is n 2. Proof. We'll use induction again. his time our target set will be the set Q: [4] Q = {x: the sum of the first x odd numbers is x 2 } We want to show that every number is in Q, and we will do so by proving that (i) and (ii) of the induction principle are true for Q. Base: 1 = 1 2, so 1 " Q. Induction: Now we want to show the induction step:

4 [5] for any natural number x, if every number less than x is in Q, then x " Q. We do this by instantiating x to some arbitrary number n, and proving: [6] if every number less than n is in Q, then n " Q. As before, if the antecedent is false for n, the conditional is trivially true for n. So we assume the antecedent, our induction hypothesis: [IH] every number less than n is in Q. Now we must show that n is in Q. Note that the nth odd number is 2n-1. So the (n-1)th odd number is 2n-3. hus, by our induction hypothesis [IH] we may assume that: ( (2n-5) + (2n-3)) = (n-1) 2. We want to show that this number plus 2n-1 (which is the nth odd number) is equal to n 2. hat is, we want to prove that: (n-1) 2 + 2n-1 = n 2 i.e., that 2n-1 = n 2 - (n-1) 2, As (n-1) 2 = n 2-2n + 1, We have: 2n-1 = n 2 - (n 2-2n + 1) 2n-1 = 2n-1 So for our arbitrary n, [6] is true, and since n was arbitrary, it follows that [5] is true, and so (ii) is true. Hence by the induction principle, it follows that all the numbers are in Q, which is to say that every number n is such that n 2 is the sum of the first n odd numbers. Mathematical Induction and PL. Mathematical induction is a powerful device for studying the properties of logical systems. We will practice using induction by proving a number of small theorems. We will then turn to a more interesting and slightly more involved theorem. Consider the silly sequence :

5 () P, (P & P), (P & (P & P)), (P & (P & (P & P))),... I.e, () is an infinite sequence that starts out with the statement P, and whenever α is the nth element in the sequence, (P & α) is the n+1th element in the sequence. heorem: Every statement in the sequence is equivalent to P. Proof. Let [7] E = {x: the xth element in the sequence is equivalent to P} We can now see that it will be enough to show that all numbers are in E. Base: We want to show that 1 " E, and to do this, we only need to show that the first element in is equivalent to P. But the first element in is P, and P is trivially equivalent to itself. Induction: As before, we want to show: [8] for any natural number x, if every number less than x is in E, then x " E. and so we pick an arbitrary number n and show [9] if every number less than n is in E, then n " E. And to show this, we get to assume the antecedent, our induction hypothesis: [IH] every number less than n is in E. Now we verify that n " E. or n to be in E, it must be that the nth element in is equivalent to P. Suppose that α is the (n-1)th element in. then we know that the nth element in is (P & α). Moreover, by our induction hypothesis, we know that α is equivalent to P (i.e., α is true iff P is true). Clearly P is equivalent to P, and (P & α) is true iff P and α are true, which they are iff P is true, so (P & α) is true iff P is true, so it is equivalent to P. Hence, n " E, so [9] is true, and since n was arbitrary, [10] is true. So the base and induction steps are both true, so by the induction principle, we may conclude that every number is in E, which is just to say that every element in is equivalent to P. Exercises: Consider the following sequence: (H) (P! P), (P! (P! P)), (P! (P! (P! P)))...

6 [i.e., the n+1th element in the sequence is (P! α), where α is the nth element.] 1. Show that every member of (H) is a sentence. 2. Show the every member of (H) is a tautology. Induction on the Complexity of Sentences. he set N has two important structural features that allow us to systematically prove things about all of its members: It has a beginning: 0 here is a step-by-step method for going from this beginning to any element in the set (and this method only requires a finite number of steps): finite applications of the successor function---s(n) = n+1---will take you from zero to any other number. It is no accident that formal languages have corresponding features, too. Let's look at PL: It has a beginning: the simple statements: A, B,... Z, A 1,..., Z 1,... A n,..., Z n,... here is a finite step-by-step method for going from this beginning to any other sentence: you can get from the simple statements to any other sentence by a finite number of applications of the rules for constructing negations, conjunctions, disjunctions, and conditionals. We can exploit this structure to use the induction principle to study PL, but first we need a couple definitions. DE: or any numbers x and y, MAX[x, y] = whichever of x and y is the largest number (or x if x = y). DE: he complexity of any given sentence α is given by the function Cx(α), where this function is defined as follows: Cx(α) = 1, if α is a statement letter; Cx(α) = x+1, if α = ~β, for some β, and Cx(β) = x. Cx(α) = x+1, if there are β and γ such that α is of the form: (γ & β), (γ # β), or (γ! β), and MAX[Cx(β), Cx(γ)] = x. Since every sentence of PL is finite in length, and is either a statement letter or is constructed from other sentences by one of the rules for forming negations, conjunctions,

7 disjunctions, or conditionals, it follows that every sentence of PL has some number as its complexity. In quantificational terms: P 1 : is a sentence of PL N 1 : is a number ( α)(p 1 α! ( x)(n 1 x & Cx(α) = x)) 1 Let's now show that for any sentence of PL, there is exactly one way to "build up" that sentence from the simple statements of the language, using the rules for constructing sentences of PL. (cf. pp of the book for the rules) irst let's get clear what we mean by "building up" a sentence DE: A construction tree for a sentence α is denoted by the function (α), where this function is inductively defined as follows. If α is a simple statement, then (α) = α. Now let β and γ be any sentences of PL. If α = β, then (α) = ~β Τ(β) If α = (β & γ), then (α) = (β & γ) (β) (γ) If α = (β! γ), then (α) = (β! γ) (β) (γ) 1 We could also express this statement in "pure" RPL. Let C 2 : the complexity of is. hen we have: ( y)(p 1 y! ( x)(n 1 x & C 2 yx))

8 [he case is similar for when α = (β # γ), except that we replace "!" with "#".] Nothing else is a construction tree. Exercises: Create some construction trees of your own. or instance, what are the construction trees for: 1. (A! (B & ~(C & ~D))) 2. (A & (B & (C & D))) 3. ((A & B) & (C & D)) 4. ((( & ~K) # ~(B & C))! N) We can now show that there is only one way to build up a given sentence of PL. heorem: Every sentence has exactly one construction tree. Proof. o show this fact, we will use the (extremely common) method of using mathematical induction on the complexity of sentences. So let [10] C = {x: every sentence of complexity x has exactly one construction tree} Base: We want to show that every sentence with complexity 1 has exactly one construction tree. o prove this universal claim, let α be any sentence such that Cx(α) = 1. So α is a statement letter (with or without subscript). here are infinitely many such sentences, but regardless of this fact, we know that (α) = α. So α has exactly one construction tree. So our Base step has been established. Induction: We now want to show [11] for any natural number x, if every number less than x is in C, then x " C. (By now this part should be very obvious!) So we pick an arbitrary n and show [12] if every number less than n is in C, then n " C. o show this, we assume that the antecedent is true, and this gives us our IH: [IH] Every number less than n is in C. Now we want to establish that n " C. Showing that n " C is tantamount to showing that: [13] every sentence with complexity n has exactly one construction tree.

9 o prove [13], a universal claim, we will show that it holds for an arbitrary instance. So let α be any sentence such that Cx(α) = n. Now we only want to show: [14] α has exactly one construction tree. Obviously, if n = 1, then by our Base step, [14] is trivially true. So, without loss of generality, let's assume that n > 1. Since n > 1, we know, by our definition of complexity, that α must have one of the following forms, for some sentences β and γ: ~β, (β & γ), (β # γ), or (β! γ). Let us consider each of these cases in turn. Case i: α = ~β. Now, since Cx(α) = n, it follows that Cx(β) = n-1. But then by [IH], it follows that β has exactly one construction tree. By the definition of a construction tree, it follows that there is only one way to construct ~β, so α has exactly one construction tree. Case ii: α = (β & γ). Since Cx(α) = n, it follows that MAX[β, γ] = n-1. So both β and γ have complexity less than n. his means, by [IH], that there is exactly one construction tree for β and exactly one for γ, too. Since there is only one way to construct β and γ respectively, there is only one way to construct (β & γ), and hence only one way to construct α. Case iii: α = (β! γ). Since Cx(α) = n, it follows that MAX[β, γ] = n-1. So both β and γ have complexity less than n. his means, by [IH], that there is exactly one construction tree for β and exactly one for γ, too. Since there is only one way to construct β and γ respectively, there is only one way to construct (β! γ), and hence only one way to construct α. he case for α = (β # γ) is similar. We've done all the work to prove this theorem, but let's take stock and note all that we did. Since these four cases exhaust the possible ways any sentence could be built up out of smaller sentences, regardless of what sentence of complexity n α is, it follows (given [IH], of course) that there is exactly one construction tree for α. In other words, we have shown [14]. α was of course an arbitrary sentence of complexity n, so it follows that every sentence of complexity n has exactly one construction tree, so, assuming that [IH] is true, we have shown [13]. We used [IH] in proving [13], so another way to say what we have shown is that if [IH] is true, [13] is true, and this is just to say that we have proven [12]. But our number n was

10 arbitrary: we never made any special assumptions about it. So whatever we prove about n holds for every number, so our proof of [12] is really a proof of [11]. And [11] is our Induction step. We previously proved the Base step, so by the principle of mathematical induction, we can conclude that every number is in C, and this is to say no more and no less than for any complexity level, every sentence of that level has exactly one construction tree. Since every sentence has some complexity level (which follows immediately from the definitions of sentences and complexity), it follows that every sentence has exactly one construction tree. Exercises: 1. Prove that every sentence of PL has an even number of parentheses. 2. Suppose that every sentence letter is assigned the value rue. Prove that in this case, every sentence that contains no occurrences of the negation symbol is rue. [Hint: Prove that every sentence either contains a negation symbol or is rue.] We will now raise a question about PL that is not obvious. After raising this question, we will make it mathematically precise. We will then supply a rigorous proof of the answer. Suppose that instead of developing PL, we used a much weaker language, SL. SL is just like PL, except that SL does not contain the symbols! and ~. So the only sentences of SL are simple sentences, and conjunctions and disjunctions (and conjunctions of disjunctions and conjunctions, and disjunctions of conjunctions, etc.) Nevertheless, there are infinitely many sentences of SL of every complexity. (If you need another exercise, prove this last claim.) Intuitively speaking, is anything missing from SL? Yes, of course: SL cannot express negation. hat is, we can't express what e.g., PL expresses by ~P. [he proof of this is an exercise; cf. below.] So we see that PL is, intuitively speaking, stronger, or more expressive, than SL. But now we should ask: Could there be languages of propositional logic that are stronger than PL, in the way that PL is stronger than SL? PL is stronger than SL because there is no sentence α of SL (of any complexity) with the truth table: P α Our question is: Is there some truth table for which there is no sentence α such that (the main connective of) α yields the correct values of the truth table for each row? P 1 P 2... P n α X 1 X 2

11 X 2^n If there is, then maybe we should expand PL to include, say, some new n-ary logical connective that has exactly the truth conditions given above, which no sentence of PL expresses. Part of answering the intuitive question about whether PL is as strong as can be is turning the intutive question into a mathematical question. We have made some progress towards this, but we need to get a bit clearer. ruth tables for individual sentences represent functions: Put in an n-ary sequence of s and s (i.e. a row of a truth table with n distinct types of sentence letters), and the truth table tells you what the output of the function is (i.e., the truth value under the main connective of the sentence). he most expressive language of propositional logic would express all these functions. In other words, for every such function (i.e., the truth table schema above minus the Ps and α), there would be a sentence α of that language with exactly n distinct types of sentence letters that yielded the values of the function. Let's make these ideas "official" with some definitions. DE: or any number n, an n-ary boolean function is a collection of all the n-ary sequences of s and s (all 2 n of them), such that each such sequence is followed by either a or an. (hese boldfaced s and s are the output of the function given the initial n-ary sequence of s and s.) DE: A sentence α represents a given n-ary boolean function f iff the truth table for α perfectly describes f. In other words, α represents f iff α contains exactly n distinct types of statement letters, and at any given row of the truth table for α, where the input is X 1,..., X n (where each X is either a or an ), α is true iff f(x 1,..., X n ) = Yet a third way to say that α represents f is to say that f is defined as some truth table where there are n distinct statement letters, and some column of s and s. In this case α represents f iff this is the truth table for α. So for instance, can you find some α that has the following truth table (i.e., represents the following 3-ary boolean function? P 1 P 2 P 3 α

12 DE: A language L is adequate iff for every n-ary boolean function f, there is some sentence α of L which represents f. We have seen that SL is not adequate, because there is a 1-ary boolean function that it does not represent, namely the one described in the first truth table above. If every n-ary boolean function could be represented, for every n, then the propositional language is as expressive as can be. (Of course, we could change the game by adding further truth values, or including infinite sequences of s and s, but those are different stories!) Let us now show that PL is indeed as strong as can be. heorem: PL is adequate. Proof. As you might suspect, we will prove this claim by mathematical induction, but we will not do an induction on the complexity of sentences of PL. Rather, our induction will will be on the n of the n-ary boolean functions. So for starters let's define the set B: B = {x: for every x-ary boolean function, there is some sentence of PL that represents it} (Remember x and n are just place-holders for numbers, so switching them in these contexts is okay.) Base: Now we want to show that every 1-ary boolean function is represented by some sentence of PL. What 1-ary functions are there? A bit of thought will show that there are four of them, described by the following truth tables: P α 1 P α 2

13 P α 3 P α 4 [A fact, which you can prove, although it takes some thought, is that for every n, there are exactly 2 (2^n) n-ary boolean functions.] Given that these are are all the 1-ary boolean functions, can we find sentences that represent them? Sure. Let: α 1 = (P # ~P) α 2 = P (or (P & P) if you like) α 3 = ~P α 4 = (P & ~P) You know by now that each of these sentences has the corresponding truth table given above. So every 1-ary boolean function is represented in PL. So 1 " B. Induction: his should be familiar. We want to show [15] below, and we will do this by choosing an arbitrary instance of x, and showing [16]: [15] for any natural number x, if all the numbers less than x are in B, then x " B. [16] if all the numbers less than n are in B, then n " B. Of course, to show [16], we assume the antecedent: [IH] All the numbers less than n are in B. Now we want to show that n " B. In other words, we want to show that no matter what values ( or ) we assign to the various Xs in the truth table schema below, there is sure to be some sentence α that fits that truth table: (Input side) P 1 P 2... P n (Output side) α... X 1... X 2

14 ... X 2^n [Remember also that at this point we are assuming some definite value for n; however, in order for n to be at the same time arbitrary, we can't know what that definite value is.] At this point, pick some definite assignment of s and s to all the Xs. he real trick to showing that we can find some α, regardless of what values the Xs are given, lies in observing the structure of the input side (the n-ary sequences of s and s) of the truth table. If n = 1, then by our Base step, we are done, so without loss of generality, let's assume n > 1. his means that we created the input side of the truth table by forming a copy of all the (n-1)-ary sequences, and then placing a at the end of them. hen below that we form another copy of all the (n-1)-ary sequences, and then we place an after each one of those. Let's start thinking of the truth table as cut in half, the top half, where every (n-1)-ary sequence is followed by a, and the bottom half, where every (n-1)-ary sequence is followed by a. Now let's think about the top half of the truth table. It has 2 (n-1) rows, and after each row, under the α, there is a or an. If we ignore the that follows each of the (n-1)-ary sequences in the top half, then what we have is a description of an (n-1)-ary boolean function. (hat is, ignore the last at the end of every input sequence in the top half. If you do this, then the top half describes an (n-1)-ary boolean function.) By [IH], we know that (regardless of what values were chosen for the Xs), there is some sentence β of PL that represents that (n-1)-ary boolean function. Notice, too, that the same thing holds over on the bottom half. If we ignore the that follows each (n-1)-ary sequence on the input side, we get a description of some (n-1)-ary boolean function. Similarly, by [IH], we know that there is some sentence γ of PL that represents that (n-1)-ary boolean function. Notice, too, that in representing their respective (n-1)-ary boolean functions, neither β nor γ makes any use of the final sentence letter, P n. Rather it only uses P 1,... P n-1. So what we would like to do is construct a sentence that says: "If P n is true, then I will behave like β, and if P n is false, then I will behave like γ." But of course we can easily say that. hus, our sentence α in this case will be: [17] ((P n! β) & (~P n! γ))

15 Let us verify that this works. Suppose we pick at random any row of the n-ary truth table given above (once we have fixed values for the Xs). he last element in the n-ary sequence of s and s (i.e. the element corresponding to P n ) on the input side of the truth table will be either a or an. if it is a, then the second conjunct of [17] will be trivially true, because the second conjunct is (~P n! γ), and so the antecedent of this conditional will be false, and so the conditional will be true. Similarly, at every row where P n is false, the first conjunct of [17] will be true. or definiteness, let's suppose that we've picked some row where P n is true. (a corresponding account will work in the case where P n is false). Assuming we've picked a row where P n is, the second conjunct of [17] is true, and [17] itself will be true if and only if (P n! β) is also true. Since we are assuming P n is true, the sentence will be true if and only if β is true (you can convince yourself of these last two claims by consulting the truth table for "!"). But what we know about β is that at any given row in the top half, the truth value for β is identical to the truth value given at that row for that choice of value for the X at that row. hat is, the truth values of β exactly mirror the truth values of the given n-ary function (i.e., the given choice of values of X). So in our present case, β will be true at that row iff the n-ary boolean function yields the value true for that input. his means that (P n! β) is true in the top half iff the n-ary boolean function yields the value true for that input. And this means that [17] is true iff the function yields true for that input. he choice of rows was of course random, so we know that the result holds for every row in the top half. A very similar argument using the other conjunct (~P n! γ) will show that the corresponding result holds for every row in the bottom half. So for every input, our sentence in [17] is true at that (corresponding) row iff the value of the function is. So sentence [17] represents the given n-ary boolean function. he choice of values for the Xs (which distinguishes each n-ary boolean function from every other n-ary boolean function) was obviously arbitrary. So for any n-ary boolean function, we can find some sentence α that represents it. And thus we have shown that, given the [IH], it follows that n " B. Hence, have shown [16], and since our choice of n was arbitrary, we have shown [15]. [15] was the induction step, and we also proved the base step, so by the principle of mathematical induction, we can conclude that every number is in B. So for any number n, it follows that for any n-ary boolean function, there is some sentence α of PL that represents it. Hence, by definition, PL is adequate. Exercises:

16 1. Prove that negation cannot be expressed in SL. I.e., prove that there is no sentence of SL that is equivalent to ~P. 2. Let SSL be just like SL except that the conjunction symbol is not a part of SSL. So the only complex statements of SSL are disjunctions. Prove that for any sentence α of SSL, if α contains n tokens of simple sentences, then there are exactly n open branches at the bottom of the tree for α. (You may assume there is exactly one tree for α.) 3. Prove that in SL, every truth tree is open. 4. Prove that every truth table for any sentence of PL has an even number of rows (you don't need to appeal to the complexity of sentences. Rather run the induction on the number of simple sentence types present in the matrix of the truth table). Kent Johnson, 2000

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

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

2. Propositional Equivalences

2. PROPOSITIONAL EQUIVALENCES 33 2. Propositional Equivalences 2.1. Tautology/Contradiction/Contingency. Definition 2.1.1. A tautology is a proposition that is always true. Example 2.1.1. p p Definition

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

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

Fundamentals of Mathematics Lecture 6: Propositional Logic

Fundamentals of Mathematics Lecture 6: Propositional Logic Guan-Shieng Huang National Chi Nan University, Taiwan Spring, 2008 1 / 39 Connectives Propositional Connectives I 1 Negation: (not A) A A T F

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

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

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

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

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,

Prime Numbers. Chapter Primes and Composites

Chapter 2 Prime Numbers The term factoring or factorization refers to the process of expressing an integer as the product of two or more integers in a nontrivial way, e.g., 42 = 6 7. Prime numbers are

Chapter 6 Finite sets and infinite sets. Copyright 2013, 2005, 2001 Pearson Education, Inc. Section 3.1, Slide 1

Chapter 6 Finite sets and infinite sets Copyright 013, 005, 001 Pearson Education, Inc. Section 3.1, Slide 1 Section 6. PROPERTIES OF THE NATURE NUMBERS 013 Pearson Education, Inc.1 Slide Recall that denotes

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

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

Invalidity in Predicate Logic

Invalidity in Predicate Logic So far we ve got a method for establishing that a predicate logic argument is valid: do a derivation. But we ve got no method for establishing invalidity. In propositional

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

MATH20302 Propositional Logic. Mike Prest School of Mathematics Alan Turing Building Room

MATH20302 Propositional Logic Mike Prest School of Mathematics Alan Turing Building Room 1.120 mprest@manchester.ac.uk April 10, 2015 Contents I Propositional Logic 3 1 Propositional languages 4 1.1 Propositional

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

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

a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.

Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given

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

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

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)

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.

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

1 Gaussian Elimination

Contents 1 Gaussian Elimination 1.1 Elementary Row Operations 1.2 Some matrices whose associated system of equations are easy to solve 1.3 Gaussian Elimination 1.4 Gauss-Jordan reduction and the Reduced

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,

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

CHAPTER 1. Logic, Proofs Propositions

CHAPTER 1 Logic, Proofs 1.1. Propositions A proposition is a declarative sentence that is either true or false (but not both). For instance, the following are propositions: Paris is in France (true), London

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

WUCT121. Discrete Mathematics. Logic

WUCT121 Discrete Mathematics Logic 1. Logic 2. Predicate Logic 3. Proofs 4. Set Theory 5. Relations and Functions WUCT121 Logic 1 Section 1. Logic 1.1. Introduction. In developing a mathematical theory,

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

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

It is time to prove some theorems. There are various strategies for doing

CHAPTER 4 Direct Proof It is time to prove some theorems. There are various strategies for doing this; we now examine the most straightforward approach, a technique called direct proof. As we begin, it

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

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

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

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

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

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

5544 = 2 2772 = 2 2 1386 = 2 2 2 693. Now we have to find a divisor of 693. We can try 3, and 693 = 3 231,and we keep dividing by 3 to get: 1

MATH 13150: Freshman Seminar Unit 8 1. Prime numbers 1.1. Primes. A number bigger than 1 is called prime if its only divisors are 1 and itself. For example, 3 is prime because the only numbers dividing

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

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called

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,

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

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

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 2. Logic. 1. Logic Definitions. Notation: Variables are used to represent propositions. The most common variables used are p, q, and r.

CHAPTER 2 Logic 1. Logic Definitions 1.1. Propositions. Definition 1.1.1. A proposition is a declarative sentence that is either true (denoted either T or 1) or false (denoted either F or 0). Notation:

ORDERS OF ELEMENTS IN A GROUP

ORDERS OF ELEMENTS IN A GROUP KEITH CONRAD 1. Introduction Let G be a group and g G. We say g has finite order if g n = e for some positive integer n. For example, 1 and i have finite order in C, since

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

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.

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

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

Introduction to mathematical arguments

Introduction to mathematical arguments (background handout for courses requiring proofs) by Michael Hutchings A mathematical proof is an argument which convinces other people that something is true. Math

LESSON 1 PRIME NUMBERS AND FACTORISATION

LESSON 1 PRIME NUMBERS AND FACTORISATION 1.1 FACTORS: The natural numbers are the numbers 1,, 3, 4,. The integers are the naturals numbers together with 0 and the negative integers. That is the integers

1. LINEAR EQUATIONS. A linear equation in n unknowns x 1, x 2,, x n is an equation of the form

1. LINEAR EQUATIONS A linear equation in n unknowns x 1, x 2,, x n is an equation of the form a 1 x 1 + a 2 x 2 + + a n x n = b, where a 1, a 2,..., a n, b are given real numbers. For example, with x and

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

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

Introduction. Appendix D Mathematical Induction D1

Appendix D Mathematical Induction D D Mathematical Induction Use mathematical induction to prove a formula. Find a sum of powers of integers. Find a formula for a finite sum. Use finite differences to

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

1 Propositional Logic

1 Propositional Logic Propositions 1.1 Definition A declarative sentence is a sentence that declares a fact or facts. Example 1 The earth is spherical. 7 + 1 = 6 + 2 x 2 > 0 for all real numbers x. 1 =

8 Primes and Modular Arithmetic

8 Primes and Modular Arithmetic 8.1 Primes and Factors Over two millennia ago already, people all over the world were considering the properties of numbers. One of the simplest concepts is prime numbers.

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

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

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

Propositional Logic. Definition: A proposition or statement is a sentence which is either true or false.

Propositional Logic Definition: A proposition or statement is a sentence which is either true or false. Definition:If a proposition is true, then we say its truth value is true, and if a proposition is

(Refer Slide Time: 1:41)

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

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

Induction and the Division Theorem 2.1 The Method of Induction

2 Induction and the Division Theorem 2.1 The Method of Induction In the Introduction we discussed a mathematical problem whose solution required the verification of an infinite family of statements. We

Chapter 1: The binomial asset pricing model

Chapter 1: The binomial asset pricing model Simone Calogero April 17, 2015 Contents 1 The binomial model 1 2 1+1 dimensional stock markets 4 3 Arbitrage portfolio 8 4 Implementation of the binomial model

Discrete Mathematics, Chapter : Propositional Logic

Discrete Mathematics, Chapter 1.1.-1.3: Propositional Logic Richard Mayr University of Edinburgh, UK Richard Mayr (University of Edinburgh, UK) Discrete Mathematics. Chapter 1.1-1.3 1 / 21 Outline 1 Propositions

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

Chapter 1. Logic and Proof

Chapter 1. Logic and Proof 1.1 Remark: A little over 100 years ago, it was found that some mathematical proofs contained paradoxes, and these paradoxes could be used to prove statements that were known

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

CS 103X: Discrete Structures Homework Assignment 3 Solutions

CS 103X: Discrete Structures Homework Assignment 3 s Exercise 1 (20 points). On well-ordering and induction: (a) Prove the induction principle from the well-ordering principle. (b) Prove the well-ordering

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

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

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

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

Sets and functions. {x R : x > 0}.

Sets and functions 1 Sets The language of sets and functions pervades mathematics, and most of the important operations in mathematics turn out to be functions or to be expressible in terms of functions.

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system

1. Systems of linear equations We are interested in the solutions to systems of linear equations. A linear equation is of the form 3x 5y + 2z + w = 3. The key thing is that we don t multiply the variables

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

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

CS510 Software Engineering

CS510 Software Engineering Propositional Logic Asst. Prof. Mathias Payer Department of Computer Science Purdue University TA: Scott A. Carr Slides inspired by Xiangyu Zhang http://nebelwelt.net/teaching/15-cs510-se

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

P (A) = lim P (A) = N(A)/N,

1.1 Probability, Relative Frequency and Classical Definition. Probability is the study of random or non-deterministic experiments. Suppose an experiment can be repeated any number of times, so that we

Induction. Margaret M. Fleck. 10 October These notes cover mathematical induction and recursive definition

Induction Margaret M. Fleck 10 October 011 These notes cover mathematical induction and recursive definition 1 Introduction to induction At the start of the term, we saw the following formula for computing

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,

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

Section 3 Sequences and Limits, Continued.

Section 3 Sequences and Limits, Continued. Lemma 3.6 Let {a n } n N be a convergent sequence for which a n 0 for all n N and it α 0. Then there exists N N such that for all n N. α a n 3 α In particular

Course notes on Number Theory

Course notes on Number Theory In Number Theory, we make the decision to work entirely with whole numbers. There are many reasons for this besides just mathematical interest, not the least of which is that

8.7 Mathematical Induction

8.7. MATHEMATICAL INDUCTION 8-135 8.7 Mathematical Induction Objective Prove a statement by mathematical induction Many mathematical facts are established by first observing a pattern, then making a conjecture

Consistency, completeness of undecidable preposition of Principia Mathematica. Tanmay Jaipurkar

Consistency, completeness of undecidable preposition of Principia Mathematica Tanmay Jaipurkar October 21, 2013 Abstract The fallowing paper discusses the inconsistency and undecidable preposition of Principia

Full and Complete Binary Trees

Full and Complete Binary Trees Binary Tree Theorems 1 Here are two important types of binary trees. Note that the definitions, while similar, are logically independent. Definition: a binary tree T is full

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

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

Induction and Recursion

Induction and Recursion Jan van Eijck June 3, 2003 Abstract A very important proof method that is not covered by the recipes from Chapter 3 is the method of proof by Mathematical Induction. Roughly speaking,