# Induction. Mathematical Induction. Induction. Induction. Induction. Induction. 29 Sept 2015

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1 Mathematical If we have a propositional function P(n), and we want to prove that P(n) is true for any natural number n, we do the following: Show that P(0) is true. (basis step) We could also start at any other integer m, in which case we prove it for all n m. Show that if P(n) then P(n+1) for any n N. (inductive step) Then P(n) must be true for any n N. Sept. 29, 2015 CS Example: Show that n < 2 n for all positive integers n. Let P(n) be the proposition n < 2 n. 1. Show that P(1) is true. (basis step) P(1) is true, because 1 < 2 1 = 2. Sept. 29, 2015 CS Show that if P(n) is true, then P(n + 1) is true. (inductive step) 3. Assume that n < 2 n is true. We need to show that P(n + 1) is true, i.e. n + 1 < 2 n+1 We start from n < 2 n : n + 1 < 2 n n + 2 n = 2 n+1 Therefore, if n < 2 n then n + 1 < 2 n+1 3. Then P(n) must be true for any positive integer. n < 2 n is true for any positive integer. End of proof. Sept. 29, 2015 CS Sept. 29, 2015 CS Another Example ( Gauss ): n = n (n + 1)/2 1. Show that P(1) is true. (basis step) For n = 1 we get 1 = 1. True. 2. Show that if P(n) then P(n + 1) for any n 1. (inductive step) n = n (n + 1)/ n + (n + 1) = (n + 1) + n (n + 1)/2 = (n + 1)(1 + n/2) = (n + 1)((2 + n)/2) = (n + 1) ((n + 1) + 1)/2 Sept. 29, 2015 CS Sept. 29, 2015 CS

2 3. Then P(n) must be true for any n n = n (n + 1)/2 is true for all n 1 End of proof. Note that we ve already seen a proof for this not using induction. Often more than one proof is possible. Sept. 29, 2015 CS A variation on induction is the second principle of mathematical induction or strong induction. It is also used to prove that a propositional function P(n) is true for any natural number n. It s easy to check that strong induction follows from regular mathematical induction. Sept. 29, 2015 CS The second principle of mathematical induction: Show that P(0) is true. (basis step) Show that if P(0) and P(1) and P(2) and P(n), then P(n + 1) for any n N. (inductive step) Then P(n) must be true for any n N. Example: Show that every integer greater than 1 can be written as the product of primes. Show that P(2) is true. (basis step) 2 is the product of one prime: itself. Sept. 29, 2015 CS Sept. 29, 2015 CS Show that if P(2) and P(3) and and P(n), then P(n + 1) for any n N. (inductive step) Two possible cases: If (n + 1) is prime, then obviously P(n + 1) is true. If (n + 1) is composite, it can be written as the product of two integers a and b such that 2 a b < n + 1. By the induction hypothesis, both a and b can be written as the product of primes. Therefore, n + 1 = a b can be written as the product of primes. Sept. 29, 2015 CS Then P(n) must be true for any n N. End of proof. We have shown that every integer greater than 1 can be written as the product of primes. This proves the Fundamental Theorem of Arithmetic (p. 258) (p th ed), except for the uniqueness part. Sept. 29, 2015 CS

3 Well Ordering The Well Ordering Property of the Natural Numbers is: Every non-empty set of natural numbers has a least element. This is an axiom of the natural numbers, and is equivalent to mathematical induction. Sept. 29, 2015 CS Well Ordering Theorem: the following are equivalent 1. Mathematical is valid. 2. Strong induction is valid 3. Every non empty set of natural numbers has a least element Sept. 29, 2015 CS Proof: 1 2. Suppose regular induction is valid, and suppose (i) P(0) is true and (ii) P(0) P(1) P(n-1) P(n) for n 1. We need to prove P(n) for all n. Let Q(n) be P(0), P(1),, P(n-1) are true. Then Q(0) is true and by (ii), Q(n-1) Q(n). Thus by regular induction Q(n), and hence P(n), is true for all n. Sept. 29, 2015 CS Suppose strong induction is valid. Let A be a set of natural numbers without a least element. Let P(j) be j is not in A. Then P(0) is true, and if P(0), P(1),, P(n-1) are true then also P(n) is true, or n would be the least element of A. But then by strong induction P(n) is true for all n in A, and hence A is empty. Thus any non empty subset of N has a least element. Sept. 29, 2015 CS Suppose well ordering is valid and P(j) is a property such that (i) P(0) is true (ii) P(n-1) P(n) for all n > 0. Let A = {x N P(x) is false} We need to show A is empty. If A is not empty it has a least element u. But u is not 0 by (i). And if u > 0 then P(u-1) is true and P(u) is false, contradicting (ii). Hence P(n) is true for all n N. Recursive Definitions Recursion is a principle closely related to mathematical induction. In a recursive definition, an object is defined in terms of itself. We can recursively define sequences, functions and sets. Sept. 29, 2015 CS Sept. 29, 2015 CS

4 Recursively Defined Sequences Example: The sequence {a n } of powers of 2 is given by a n = 2 n for n = 0, 1, 2,. The same sequence can also be defined recursively: a 0 = 1 a n+1 = 2a n for n = 0, 1, 2, Obviously, induction and recursion are similar principles. Recursively Defined Functions We can use the following method to define a function with the natural numbers as its domain: Specify the value of the function at zero. Give a rule for finding its value at any integer from its values at smaller integers. Such a definition is called recursive or inductive definition. Sept. 29, 2015 CS Sept. 29, 2015 CS Recursively Defined Functions Example: f(0) = 3 f(n + 1) = 2f(n) + 3 f(0) = 3 f(1) = 2f(0) + 3 = = 9 f(2) = 2f(1) + 3 = = 21 f(3) = 2f(2) + 3 = = 45 f(4) = 2f(3) + 3 = = 93 Recursively Defined Functions How can we recursively define the factorial function f(n) = n!? f(0) = 1 f(n + 1) = (n + 1)f(n) f(0) = 1 f(1) = 1f(0) = 1 1 = 1 f(2) = 2f(1) = 2 1 = 2 f(3) = 3f(2) = 3 2 = 6 f(4) = 4f(3) = 4 6 = 24 Sept. 29, 2015 CS Sept. 29, 2015 CS Recursively Defined Functions A famous example: The Fibonacci numbers f(0) = 0, f(1) = 1 f(n) = f(n 1) + f(n - 2) f(0) = 0 f(1) = 1 f(2) = f(1) + f(0) = = 1 f(3) = f(2) + f(1) = = 2 f(4) = f(3) + f(2) = = 3 f(5) = f(4) + f(3) = = 5 f(6) = f(5) + f(4) = = 8 If we want to recursively define a set A, we need to provide two things: an initial set of elements, rules for the construction of additional elements from elements in the set. Example: x 1,x 2, x n A R(x 1,..,x n ) A When we want to prove P(x) is true for all x in a recursively defined set A we must prove P(x) is true for each element of the initial set of A. For each rule generating new elements, if P(x1 ), P(x 2,),, P(x n ) are true then P(R(x 1,..,x n )) is true Sept. 29, 2015 CS Sept. 29, 2015 CS

5 Let P(n) be the statement 3n belongs to S. Basis step: P(1) is true, because 3 is in S. Inductive step: To show: If P(n) is true, then P(n + 1) is true. Assume 3n is in S. Since 3n is in S and 3 is in S, it follows from the recursive definition of S that 3n + 3 = 3(n + 1) is also in S.. Conclusion of Part I: A S. Sept. 29, 2015 CS Sept. 29, 2015 CS Part II: To show: S A. Basis step: To show: All initial elements of S are in A. 3 is in A. True. Inductive step: To show: (x + y) is in A whenever x and y are in S. If x and y are both in A, it follows that 3 x and 3 y. As we already know, it follows that 3 (x + y). Conclusion of Part II: S A. Overall conclusion: A = S. Sept. 29, 2015 CS Another example: The well-formed formulas of variables, numerals and operators from {+, -, *, /, ^} are defined by x is a well-formed formula if x is a numeral or variable. (f + g), (f g), (f * g), (f / g), (f ^ g) are wellformed formulas if f and g are. Sept. 29, 2015 CS With this definition, we can construct formulas such as: (x y) ((z / 3) y) ((z / 3) (6 + 5)) ((z / (2 * 4)) (6 + 5)) An algorithm is called recursive if it solves a problem by reducing it to an instance of the same problem with smaller input. Example I: Recursive Euclidean Algorithm procedure gcd(a, b: nonnegative integers with a < b) if a = 0 then gcd(a, b) := b else gcd(a, b) := gcd(b mod a, a) Sept. 29, 2015 CS Sept. 29, 2015 CS

6 Example II: Recursive Fibonacci Algorithm procedure fibo(n: nonnegative integer) if n = 0 then fibo(0) := 0 else if n = 1 then fibo(1) := 1 else fibo(n) := fibo(n 1) + fibo(n 2) Recursive Fibonacci Evaluation: f(4) f(3) f(2) f(2) f(1) f(1) f(0) f(1) f(0) Sept. 29, 2015 CS Sept. 29, 2015 CS procedure iterative_fibo(n: nonnegative integer) if n = 0 then y := 0 else begin x := 0 y := 1 for i := 1 to n-1 begin z := x + y x : = y y := z end end {y is the n-th Fibonacci number} For every recursive algorithm, there is an equivalent iterative algorithm. Recursive algorithms are often shorter, more elegant, and easier to understand than their iterative counterparts. However, iterative algorithms are usually more efficient in their use of space and time. Sept. 29, 2015 CS Sept. 29, 2015 CS Program Verification Proof that a program works correctly is difficult. One approach is to attach statements about the state of the program (values of the variables) and prove thereby that sequences of statements will do what you expect. See section 5.5, p 372 (4.5, p 322 in 6 th edition) Sept 29, 2015 CS Partial Correctness Def. A program segment S is partially correct with respect to initial assertion p and final assertion q, if whenever p is true and S is executed and terminates then q will be true. In this case we write: p{s}q Sept 29, 2015 CS

7 Composition Rule p{s 1 }q q{s 2 }r p{s 1 ; S 2 }r This means that we can combine the assertions about S 1 and S 2 to get an assertion about what happens when we execute first S 1 and then S 2. Example Suppose p is T, q is x > 0, r is y > 0 Then p{x := 4}q, and q{y := 2*x}r are correct, and thus so is p{x := 4; y := 2*x}r. Sept 29, 2015 CS Sept 29, 2015 CS Conditionals 1 (p condition) {S} q (p condition) q p { if condition then S} q Conditionals 2 (p condition) {S 1 } q (p condition) {S 2 }q p { if condition then S 1 else S 2 } q Here we get a correctness condition on execution of S giving us a correctness condition for the conditional. Sept 29, 2015 CS Similar thing, for if-then-else. Sept 29, 2015 CS Loop Invariants (p condition) {S} p p {while condition S} ( condition p) Here p is called a loop invariant because it remains true on each pass through the loop. We usually pick a loop invariant carefully to establish some fact we want. Sept 29, 2015 CS Example of loop invariants We can use loop invariants to prove that binary search is correct. (searching for x in an ordered sequence a 1,, a n ) i := 1; k:= n; while (i < k) { m := (i+k)/2 ; if (x > a m ) then i := m+1; // i post = m+1, k post = k pre else k := m; // i post = i pre, k post = m } if (x = a i ) then location := i; else location := 0; // A loop invariant that works: // p: (x = a j for some j) (a i x a k ) (i k) Sept 29, 2015 CS

8 To see this loop invariant works: 1. Check that i k is invariant. 2. m = (i+k)/2 = k - (k-i)/2 = k - (k-i)/2, so m < k (if i < k). 3. m = (i+k)/2 = i+(k-i)/2 = i+ (k-i)/2, so m i. Now check that a i x a k is invariant on each pass through the loop. if (x > a m ) then i post := m+1; // so a m+1 x a k, if x is one of the a j else k post := m; // so a i x a m Thus in each case we also have a i x a k 4. Thus i m < k on each pass after each pass through the loop. through the loop and i < m < k unless i+1=k. (i, k are i pre,k pre) Sept 29, 2015 CS Sept 29, 2015 CS

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