Shawn Zhong

Shawn Zhong

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Shawn Zhong

钟万祥
  • Tutorials
  • Mathematics
    • Math 240
    • Math 375
    • Math 431
    • Math 514
    • Math 521
    • Math 541
    • Math 632
    • Abstract Algebra
    • Linear Algebra
    • Category Theory
  • Computer Sciences
    • CS/ECE 252
    • CS/ECE 352
    • Learn Haskell
  • AP Notes
    • AP Microecon
    • AP Macroecon
    • AP Statistics
    • AP Chemistry
    • AP Physics E&M
    • AP Physics Mech
    • CLEP Psycho

Math 240

Home / Mathematics / Notes / Math 240 / Page 4

6.1 The Basics of Counting

  • Apr 02, 2018
  • Shawn
  • Math 240
  • No comments yet
Basic Counting Principles: The Product Rule • The Product Rule ○ A procedure can be broken down into a sequence of two tasks. ○ There are n_1 ways to do the first task and n_2 ways to do the second task. ○ Then there are n_1∙n_2 ways to do the procedure. • Example 1 ○ How many bit strings of length seven are there? ○ Since each of the seven bits is either a 0 or a 1, the answer is 2^7 = 128. • Example 2 ○ How many different license plates can be made if ○ each plate contains a sequence of 3 uppercase English letters followed by 3 digits? ○ There are 26 ∙ 26 ∙ 26 ∙ 10 ∙ 10 ∙ 10 = 17,576,000 different possible license plates. • Example 3: Counting Functions ○ How many functions are there from a set with m elements to a set with n elements? ○ A function represents a choice of one of the n elements of the codomain for each of the m elements in the domain ○ The product rule tells us that there are n⋅n⋯n = n^m such functions. • Example 4: Counting One-to-One Functions ○ How many 1-to-1 functions are there from a set with m elements to one with n elements? ○ Suppose the elements in the domain are a_1,a_2,…,a_m. ○ There are n ways to choose the value of a_1 and n−1 ways to choose a_2, etc. ○ The product rule tells us that there are n(n−1)(n−2)⋯(n−m+1) functions. • Example 5: Counting Subsets of a Finite Set ○ Show that the number of different subsets of a finite set S is 2^|S| ○ When the elements of S are listed in an arbitrary order ○ There is a one-to-one correspondence between subsets of S and bit strings of length |S|. ○ When the ith element is in the subset, the bit string has a 1 in the ith position and a 0 otherwise. ○ By the product rule, there are 2^|S| such bit strings, and therefore 2^|S| subsets. • Example 6: Product Rule in Terms of Sets ○ Let A_1,A_2,…,A_m be finite sets ○ The number of elements in the Cartesian product of these sets is the product of the number of elements of each set. ○ The task of choosing an element in the Cartesian product A_1×A_2×…×A_m is done by ○ choosing an element in A_1, an element in A_2 , …, and an element in A_m. ○ By the product rule, it follows that: |A_1×A_2×…×A_m |=|A_1 |⋅|A_2 |⋯|A_m | • Example 7: DNA and Genomes ○ A gene is a segment of a DNA molecule that encodes a particular protein and the entirety of genetic information of an organism is called its genome. ○ DNA molecules consist of two strands of blocks known as nucleotides. ○ Each nucleotide is composed of bases: adenine(A), cytosine(C), guanine(G), thymine(T). ○ The DNA of bacteria has between 〖10〗^5 and 〖10〗^7 links (one of the four bases). ○ Mammals have between 〖10〗^8 and 〖10〗^10 links. ○ So, by the product rule there are at least 4^(〖10〗^5 ) different sequences of bases in the DNA of bacteria and 4^(〖10〗^8 ) different sequences of bases in the DNA of mammals. ○ The human genome includes approximately 23,000 genes, each with 1,000 or more links. Basic Counting Principles: The Sum Rule • The Sum Rule ○ If a task can be done either in one of n_1 ways or in one of n_2, ○ where none of the set of n_1 ways is the same as any of the n_2 ways, ○ then there are n_1+n_2 ways to do the task. • Example ○ The mathematics department must choose either a student or a faculty member as a representative for a university committee. ○ How many choices are there for this representative if there are 37 members of the mathematics faculty and 83 mathematics majors and no one is both a faculty member and a student. ○ There are 37 + 83 = 120 possible ways to pick a representative. • The Sum Rule in terms of sets ○ The sum rule can be phrased in terms of sets. ○ |A ∪ B|= |A| + |B| as long as A and B are disjoint sets. ○ Or more generally, § |A_1∪A_2∪…∪A_m |=|A_1 |+|A_2 |+…+|A_m |, when A_i∩A_j=∅ for all i,j Combining the Sum and Product Rule • Example 1 ○ Suppose statement labels in a programming language can be either a single letter or a letter followed by a digit. ○ Find the number of possible labels. ○ Use the product rule: 26 + 26 ∙ 10 = 286 • Example 2: Counting Passwords ○ Each user on a computer system has a password ○ which is 6 to 8 characters long, where each character is an uppercase letter or a digit. ○ Each password must contain at least one digit. ○ How many possible passwords are there? ○ Let P be the total number of passwords ○ Let P_6, P_7, and P_8 be the passwords of length 6, 7, and 8. ○ By the sum rule P=P_6+P_7+P_8. ○ To find each of P_6, P_7, and P_8, we find the number of passwords of the specified length composed of letters and digits and subtract the number composed only of letters. ○ We find that: § P_6 = 〖36〗^6 − 〖26〗^6 = 2,176,782,336 − 308,915,776 =1,867,866,560 § P_7 = 〖36〗^7 − 〖26〗^7 = 78,364,164,096 − 8,031,810,176 = 70,332,353,920 § P_8 = 〖36〗^8 − 〖26〗^8 = 2,821,109,907,456 − 208,827,064,576 = 2,612,282,842,880 ○ Consequently, P=P_6+P_7+P_8 = 2,684,483,063,360. Basic Counting Principles: Subtraction Rule • The Subtraction Rule ○ If a task can be done either in one of n_1 ways or in one of n_2 ways ○ Then the total number of ways to do the task is n_1 + n_2 minus the number of ways to do the task that are common to the two different ways. ○ Also known as, the principle of inclusion-exclusion: ○ |A∪B|=|A|+|B|−|A∩B| • Example: Counting Bit Strings ○ How many bit strings of length eight either start with a 1 bit or end with the two bits 00? ○ Use the subtraction rule. ○ Number of bit strings of length eight that start with a 1 bit: 2^7 = 128 ○ Number of bit strings of length eight that end with bits 00: 2^6 = 64 ○ Number of bit strings of length eight that start with a 1 bit and end with bits 00: 2^5 = 32 ○ Hence, the number is 128 + 64 − 32 = 160. Basic Counting Principles: Division Rule • Division Rule ○ There are n/d ways to do a task if ○ it can be done using a procedure that can be carried out in n ways ○ and for every way w, exactly d of the n ways correspond to way w. • In terms of sets ○ If the finite set A is the union of n pairwise disjoint subsets each with d elements ○ then n = |A|/d. • In terms of functions ○ If f is a function from A to B, where both are finite sets, and for every value y ∈ B there are exactly d values x ∈ A such that f(x)=y, then |B|=|A|/d. • Example ○ How many ways are there to seat four people around a circular table, where two seatings are considered the same when each person has the same left and right neighbor? ○ Number the seats around the table from 1 to 4 proceeding clockwise. ○ There are four ways to select the person for seat 1, 3 for seat 2, 2, for seat 3, and one way for seat 4. ○ Thus there are 4! = 24 ways to order the four people. ○ But since two seatings are the same when each person has the same left and right neighbor, for every choice for seat 1, we get the same seating. ○ Therefore, by the division rule, there are 24/4 = 6 different seating arrangements. Tree Diagrams • Tree Diagrams ○ We can solve many counting problems through the use of tree diagrams, where a branch represents a possible choice and the leaves represent possible outcomes. • Example ○ Suppose that “I Love Discrete Math” T-shirts come in five different sizes: S, M, L, XL, XXL. ○ Each size comes in four colors (white, red, green, and black), except XL, which comes only in red, green, and black, and XXL, which comes only in green and black. ○ What is the minimum number of shirts that the campus book store needs to stock to have one of each size and color available? ○ Draw the tree diagram. The store must stock 17 T-shirts.
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5.4 Recursive Algorithms

  • Mar 23, 2018
  • Shawn
  • Math 240
  • No comments yet
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5.3 Recursive Definitions and Structural Induction

  • Mar 19, 2018
  • Shawn
  • Math 240
  • No comments yet
Recursively Defined Functions • Definition ○ A recursive or inductive definition of a function consists of two steps. ○ Basis Step § Specify the value of the function at zero. ○ Recursive Step § Give a rule for finding the at an integer from its values at smaller integers ○ A function f(n) is the same as a sequence a_0,a_1,… where f(i)=a_i ○ This was done using recurrence relations in Section 2.4 • Example 1 ○ Suppose f is defined by § f(0)=3 § f(n+1)=2f(n)+3 ○ Find f(1), f(2), f(3), f(4) § f(1)=2f(0)+3=2∙3+3=9 § f(2)=2f(1)+3=2∙9+3=21 § f(3)=2f(2)+3=2∙21+3=45 § f(4)=2f(3)+3=2∙45+3=93 • Example 2 ○ Give a recursive definition of the factorial function n! ○ f(0) = 1 ○ f(n + 1) = (n + 1)∙ f(n) • Example ○ Give a recursive definition of ∑_(k=0)^n▒a_k ○ The first part of the definition is § ∑_(k=0)^0▒a_k =a_0 ○ The second part is § ∑_(k=0)^(n+1)▒a_k =(∑_(k=0)^n▒a_k )+a_(n+1) Fibonacci Numbers • The Fibonacci numbers are defined as follows: ○ f_0=0 ○ f_1=1 ○ f_n=f_(n−1)+f_(n−2) • Find f_2,f_3,f_4,f_5 ○ f_2=f_1+f_0=1+0=1 ○ f_3=f_2+f_1=1+1=2 ○ f_4=f_3+f_2=2+1=3 ○ f_5=f_4+f_3=3+2=5 • Show that whenever n ≥ 3, f_n   α^(n−2), where α=(1+√5)/2 ○ Let P(n) be the statement f_n   α^(n−2) . ○ Use strong induction to show that P(n) is true whenever n≥3. ○ Basis step § P(3) holds since α   2 = f_3 § P(4) holds since α^2 = (3+√5)/2   3 = f_4 ○ Inductive step § Assume that P(j) holds § i.e., f_j   α^(j−2) for all integers j with 3 ≤ j ≤ k, where k ≥ 4. § Show that P(k+1) holds, i.e., f_(k+1) α^(k−1). § Since α^2= α + 1 (because α is a solution of x^2−x−1=0), § α^(k−1)=α^2⋅α^(k−3)=(α+1)⋅α^(k−3)=α⋅α^(k−3)+1⋅α^(k−3)=α^(k−2)+α^(k−3) § By the inductive hypothesis, because k ≥ 4 we have □ f_(k−1) α^(k−3) □ f_k α^(k−3) § Therefore, it follows that □ f_(k+1)=f_k+f_(k−1) α^(k−2)+α^(k−3)=α^(k−1) § Hence, P(k+1) is true. Lamé’s Theorem • Lamé’s Theorem ○ Let a and b be positive integers with a≥b. ○ Then the number of divisions used by the Euclidian algorithm to find gcd(a,b) ○ is less than or equal to five times the number of decimal digits in b. • Proof ○ When we use the Euclidian algorithm to find gcd(a,b) with a≥b, ○ n divisions are used to obtain (with a=r_0,b=r_1) § r_0=r_1 q_1+r_2, 0 r_2 r_1 § r_1=r_2 q_2+r_3, 0 r_3 r_2 § ⋮ § r_(n−2)=r_(n−1) q_(n−1)+r_n, 0 r_n r_(n−1) § r_(n−1)=r_n q_n ○ Since each quotient q_1,q_2,…,q_(n−1) is at least 1 and q_n≥2 § r_n≥1=f_2 § r_(n−1)≥2r_n≥2f_2=f_3 § r_(n−2)≥r_(r−1)+r_n≥f_3+f_2=f_4 § ⋮ § r_2 r_3+r_4≥f_(n−1)+f_(n−2)=f_n § b=r_1≥r_2+r_3≥f_n+f_(n−1)=f_(n+1) ○ If n divisions are used to find gcd(a,b) with a≥b, then b ≥ f_(n+1) ○ By Example 4, f_(n+1) α^(n−1), for n   2, where α=(1+√5)/2. ○ Therefore, b   α^(n−1). ○ Because log_10⁡α ≈ 0.208   1/5, log_10⁡b   (n−1) log_10⁡α   (n−1)/5 ○ Hence, n−1 5⋅log_10⁡b ○ Suppose that b has k decimal digits. Then b   10k and log_10⁡b k. ○ It follows that n−1 5k and since k is an integer, n≤5k. ○ Therefore, O(log b) divisions are used to find gcd(a,b) whenever a   b. ○ The number of divisions needed is less than or equal to 5⋅(log_10⁡b+1) ○ Since the number of decimal digits in b is less than or equal to log_10⁡b+1 Recursively Defined Sets and Structures • Recursive definitions of sets have two parts: ○ The basis step specifies an initial collection of elements. ○ The recursive step gives the rules for forming new elements in the set from those already known to be in the set. • Sometimes the recursive definition has an exclusion rule, which specifies that ○ the set contains nothing other than those elements specified in the basis step ○ and generated by applications of the rules in the recursive step. • We always assume that the exclusion rule holds, even if it is not explicitly mentioned. • Example: Subset of Integers S ○ Basis step: 3 ∊ S. ○ Recursive step: If x ∊ S and y ∊ S, then x+y is in S. ○ Initially 3 is in S, then 3 + 3 = 6, then 3 + 6 = 9, etc. • Example: The natural numbers N. ○ Basis step: 0 ∊ N. ○ Recursive step: If n is in N, then n + 1 is in N. ○ Initially 0 is in N, then 0 + 1 = 1, then 1 + 1 = 2, etc. Strings • Definition: The set Σ* of strings over the alphabet Σ: ○ Basis step: λ ∊ Σ* (λ is the empty string) ○ Recursive step: If w is in Σ* and x is in Σ, wx∈Σ^∗. • Example ○ If Σ = {0,1} ○ The strings in in Σ* are the set of all bit strings, λ, 0, 1, 00,01,10, 11, etc. • Example ○ If Σ = {a,b}, show that aab is in Σ*. ○ Since λ ∊ Σ* and a ∊ Σ, a ∊ Σ*. ○ Since a ∊ Σ* and a ∊ Σ, aa ∊ Σ*. ○ Since aa ∊ Σ* and b ∊ Σ, aab ∊ Σ*. String Concatenation • Two strings can be combined via the operation of concatenation. • Let Σ be a set of symbols and Σ* be the set of strings formed from the symbols in Σ. • We can define the concatenation of two strings, denoted by ∙, recursively as follows: ○ Basis step § If w∈Σ^∗, then w⋅λ=w ○ Recursive step § If w_1∈Σ^∗ and w_2∈Σ^∗ and x∈Σ^∗ then w_1⋅(w_2 x)=(w_1⋅w_2 )x • Often w_1⋅w_2 is written as w_1 w_2. • If w_1 = abra and w_2 = cadabra, the concatenation w_1 w_2 = abracadabra. Length of a String • Give a recursive definition of l(w), the length of the string w. • The length of a string can be recursively defined by: ○ l(λ)=0 ○ l(wx) = l(w) + 1 if w∊Σ* and x∈Σ Rooted Trees • The set of rooted trees, where a rooted tree consists of ○ a set of vertices containing a distinguished vertex called the root ○ edges connecting these vertices • can be defined recursively by these steps • Basis step ○ A single vertex r is a rooted tree. • Recursive step ○ Suppose that T_1,…,T_n are disjoint rooted trees with roots r_1,…,r_n respectively. ○ Then the graph formed by § start with a root r, which is not in any of the rooted trees T_1,…,T_n § add an edge from r to each of the vertices r_1,…,r_n ○ is also a rooted tree. Full Binary Trees • The set of full binary trees can be defined recursively by these steps. ○ Basis step § There is a full binary tree consisting of only a single vertex r. ○ Recursive step § If T_1 and T_2 are disjoint full binary trees § There is a full binary tree, denoted by T_1∙T_2, consisting of □ a root r □ edges connecting the root to each of the roots of T_1 and T_2 • The height h(T) of a full binary tree T is defined recursively as follows: ○ Basis step § The height of a full binary tree T consisting of only a root r is h(T)=0 ○ Recursive step § If T_1 and T_2 are full binary trees § Then the full binary tree T=T_1⋅T_2 has height § h(T)=1+max⁡(hT_1 ),hT_2 )) • The number of vertices n(T) of a full binary tree T is defined recursively as follows ○ Basis step § n(T)=1 for full binary tree T consisting of only a root r ○ Recursive step § If T_1 and T_2 are full binary trees § Then the full binary tree T=T_1⋅T_2 has the number of vertices § n(T)=1+n(T_1 )+n(T_2 ) Structural Induction • To prove a property of the elements of a recursively defined set, we use structural induction • Basis step ○ Show that The result holds for all elements specified in the basis step • Recursive step ○ Suppose the statement is true for each of the elements used to construct new elements in the recursive step of the definition ○ Show that the result holds for these new elements. • The validity of structural induction can be shown to follow from the principle of mathematical induction. Structural Induction and Binary Trees • If T is a full binary tree, then n(T)≤2^(hT)+1)−1 • Proof: Use structural induction • Basis step ○ The result holds for a full binary tree consisting only of a root ○ n(T) = 1 and h(T) = 0 ○ Hence, n(T) = 1 ≤ 2^(0+1)−1 = 1 • Recursive step ○ Assume n(T_1 )≤2^(hT_1 )+1)−1 and n(T_2 )≤2^(hT_2 )+1)−1 for full binary trees T_1,T_2 ○ n(T)=1+n(T_1 )+n(T_2 ) ○ ≤1+(2^(hT_1 )+1)−1)+(2^(hT_2 )+1)−1) ○ ≤2⋅max⁡(2^(hT_1 )+1),2^(hT_2 )+1) )−1 ○ =2⋅2^max⁡〖(h(T_1 ),hT_2 ))+1〗 −1 ○ =2⋅2^ht) −1 ○ =2^(ht)+1)−1
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5.2 Strong Induction and Well-Ordering

  • Mar 14, 2018
  • Shawn
  • Math 240
  • No comments yet
Strong Induction • To prove that P(n) is true for all positive integers n • where P(n) is a propositional function, complete two steps: • Basis Step ○ Verify that the proposition P(1) is true. • Inductive Step ○ Show the conditional statement § [P(1)∧P(2)∧…∧P(k)]→ P(k+1) ○ holds for all positive integers k. • Strong Induction is sometimes called ○ the second principle of mathematical induction ○ complete induction Proof using Strong Induction • Prove that every natural number n 7 can be written as 3p+5q • where p and q are natural numbers • Prove this result using strong induction • Basis Step ○ 8=3×1+5×1 • Inductive Step ○ Inductive hypothesis: The statement is true for any n for k≥n≥8 ○ In particular it is true for k+1−3 (assuming k+1−3≥8) ○ So k+1−3=3p+5q and so k+1=3(p+1)+5q • What happens if k+1=9 or k+1=10? ○ Add those cases into the basis step ○ 9=3×3+5×0 ○ 10=3×0+5×2 Which Form of Induction Should Be Used? • We can always use strong induction instead of mathematical induction. • But there is no reason to use it if it is simpler to use mathematical induction • In fact, the principles of mathematical induction, strong induction, and the well-ordering property are all equivalent. • Sometimes it is clear how to proceed using one of the three methods, but not the other two. Proof of the Fundamental Theorem of Arithmetic • Show that if n is an integer greater than 1 • Then n can be written as the product of primes. • Let P(n) be the proposition that n can be written as a product of primes. • Basis Step ○ P(2) is true since 2 itself is prime. • Inductive Step ○ The inductive hypothesis is P(j) is true for j∈Z with 2≤j≤k ○ To show that P(k+1) must be true under this assumption ○ Two cases need to be considered: ○ If k+1 is prime, then P(k+1) is true. ○ Otherwise, k+1 is composite ○ And it can be written as the product of two positive integers ○ a and b with 2≤a≤b≤k+1 ○ By inductive hypothesis a and b can be written as product of primes ○ Therefore k + 1 can also be written as the product of those primes. • Hence, every integer greater than 1 can be written as product of primes Well-Ordering Property • Well-ordering property ○ Every nonempty set of nonnegative integers has a least element. • The well-ordering property is one of the axioms of the positive integers • The well-ordering property can be used directly in proofs. • The well-ordering property can be generalized. • Definition: A set is well ordered if every subset has a least element. ○ N is well ordered under ≤. ○ The set of finite strings over an alphabet using lexicographic ordering is well ordered. Proof of The Division Algorithm • Use the well-ordering property to prove the division algorithm ○ If a is an integer and d is a positive integer, then ○ there are unique integers q and r with 0≤r d, such that ○ a=dq+4 • Let S be the set of nonnegative integers of the form a=dq, q∈Z. • The set is nonempty since −dq can be made as large as needed • By the well-ordering property, S has a least element r=a−dq_0 • The integer r is nonnegative. • It also must be the case that r d • If it were not, then there would be a smaller nonnegative element in S ○ a−d(q_0+1)=a−dq_0−d=r−d 0 • Therefore, there are integers q and r with 0≤r d
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5.1 Mathematical Induction

  • Mar 14, 2018
  • Shawn
  • Math 240
  • No comments yet
Climbing an Infinite Ladder • Suppose we have an infinite ladder: ○ We can reach the first rung of the ladder. ○ If we can reach a particular rung of the ladder ○ then we can reach the next rung. • From (1), we can reach the first rung. • Then by applying (2), we can reach the second rung. • Applying (2) again, the third rung. And so on. • We can apply (2) any number of times to reach any particular rung, no matter how high up. Principle of Mathematical Induction • To prove that P(n) is true for all positive integers n, we complete these steps: ○ Basis Step: Show that P(1) is true. ○ Inductive Step: Show that P(k)→P(k+1) is true for all positive integers k • To complete the inductive step ○ assuming the inductive hypothesis that P(k) holds for an arbitrary integer k ○ show that must P(k+1) be true. • Climbing an Infinite Ladder Example: ○ Basis Step § By (1), we can reach rung 1. ○ Inductive Step § Assume the inductive hypothesis that we can reach rung k § Then by (2), we can reach rung k+1. ○ Hence, P(k)→P(k+1) is true for all positive integers k ○ We can reach every rung on the ladder. Important Points About Using Mathematical Induction • Mathematical induction can be expressed as the rule of inference ○ (P(1)∧∀k(P(k)→P(k+1)))→∀n P(n) ○ where the domain is the set of positive integers. • In mathematical induction, we don’t assume that P(k) is true for all positive integers! • We show that if we assume that P(k) is true, then P(k+1) must also be true. • Proofs by mathematical induction do not always start at the integer 1. • In such a case, the basis step begins at a starting point b where b is an integer. • We will see examples of this soon. Proving a Summation Formula by Mathematical Induction • Example ○ Show that:∑_(i=1)^n▒i=n(n+1)/2 • Solution ○ Basis Step § P(1) is true since 1(1+1)/2=1 ○ Inductive Step § Assume true for P(k) § The inductive hypothesis is ∑_(i=1)^k▒i=k(k+1)/2 § Under this assumption § 1+2+…+k+(k+1)=k(k+1)/2+(k+1)=(k+1)(k+2)/2 Validity of Mathematical Induction • Mathematical induction is valid because of the well ordering property, which states that • every nonempty subset of the set of positive integers has a least element • Suppose that P(1) holds and P(k)→P(k+1) is true for all positive integers k. • Assume there is at least one positive integer n for which P(n) is false. • Then the set S of positive integers for which P(n) is false is nonempty. • By the well-ordering property, S has a least element, say m. • We know that m cannot be 1 since P(1) holds. • Since m is positive and greater than 1, m−1 must be a positive integer. • Since m−1 m, it is not in S, so P(m−1) must be true. • But then, since the conditional P(k)→P(k+1) for every positive integer k holds, • P(m) must also be true. This contradicts P(m) being false. • Hence, P(n) must be true for every positive integer n. Conjecturing and Proving Correct a Summation Formula • Example ○ Conjecture and prove correct a formula for the sum of the first n positive odd integers ○ Then prove your conjecture • Solution ○ We have § 1= 1 § 1 + 3 = 4 § 1 + 3 + 5 = 9 § 1 + 3 + 5 + 7 = 16 § 1 + 3 + 5 + 7 + 9 = 25. ○ We can conjecture that the sum of the first n positive odd integers is n^2, § 1+3+5+…+(2n−1)+(2n+1)=n^2 ○ We prove the conjecture is proved correct with mathematical induction. ○ Basis Step § P(1) is true since 1^2 = 1. ○ Inductive Step: P(k)→P(k+1) for every positive integer k. § Inductive Hypothesis: 1+3+5+…+(2k−1)=k^2 § So, assuming P(k), it follows that: § 1+3+5+…+(2k−1)+(2k+1) § =[1+3+5+…+(2k−1)]+(2k+1) § =k^2+(2k+1) § =(k+1)^2 ○ Hence, we have shown that P(k+1) follows from P(k). ○ Therefore the sum of the first n positive odd integers is n^2 Proving Inequalities • Example ○ Use mathematical induction to prove that n 2^n for all positive integers n. • Solution ○ Let P(n) be the proposition that n 2^n. ○ Basis Step § P(1) is true since 1   2^1 = 2. ○ Inductive Step § Assume P(k) holds, i.e., k 2^k, for an arbitrary positive integer k. § Must show that P(k + 1) holds. § Since by the inductive hypothesis, k 2^k, it follows that: § k+1 2^k+1≤2^k+2^k=2⋅2^k=2^(k+1) § Therefore n 2^n holds for all positive integers n. • Example ○ Use mathematical induction to prove that 2^n n!, for every integer n≥4 • Solution ○ Let P(n) be the proposition that 2^n n! ○ Basis Step § P(4) is true since 24 = 16   4! = 24 ○ Inductive Step § Assume P(k) holds, i.e., 2^k k! for an arbitrary integer k ≥ 4. § To show that P(k+1) holds § 2^(k+1)=2∙2^k 2⋅2^k (k+1)k!=(k+1)! § Therefore, 2^n n! holds, for every integer n ≥ 4 Proving Divisibility Results • Example ○ Use mathematical induction to prove that n^3−n is divisible by 3 ○ for every positive integer n. • Solution ○ Let P(n) be the proposition that n^3−n is divisible by 3. ○ Basis Step § P(1) is true since 13 − 1 = 0, which is divisible by 3 ○ Inductive Step § Assume P(k) holds, i.e., k^3−k is divisible by 3, for an arbitrary positive integer k § To show that P(k + 1) follows § (k+1)^3−(k+1)=(k^3+3k^2+3k+1)−(k+1)=(k^3−k)+3(k^2+k) § By the inductive hypothesis, the first term (k^3−k) is divisible by 3 § and the second term is divisible by 3 since it is an integer multiplied by 3. § So by part (i) of Theorem 1 in Section 4.1, (k+1)^3−(k+1) is divisible by 3 ○ Therefore, n^3−n is divisible by 3, for every integer positive integer n. Number of Subsets of a Finite Set • Example ○ Use mathematical induction to show that if S is a finite set with n elements ○ where n is a nonnegative integer, then S has 2^n subsets. ○ (Chapter 6 uses combinatorial methods to prove this result.) • Solution ○ Let P(n) be the proposition that a set with n elements has 2^n subsets. ○ Basis Step § P(0) is true, because the empty set has only itself as a subset and 2^0 = 1. ○ Inductive Step § Assume P(k) is true for an arbitrary nonnegative integer k. § Inductive Hypothesis § For an arbitrary nonnegative integer k, every set with k elements has 2^k subsets § Let T be a set with k+1 elements § Then T=S∪{a}, where a∈T and S=T−{a}. Hence |S| = k. § For each subset X of S, there are exactly two subsets of T, i.e., X and X∪{a}. § By the inductive hypothesis S has 2^k subsets. § Since there are two subsets of T for each subset of S, § the number of subsets of T is 2⋅2^k=2^(k+1) An Incorrect “Proof” by Mathematical Induction • P(n)≔every set of n lines in the plane, no two of which are parallel, meet in a point • Here is a “proof” that P(n) is true for all positive integers n≥2. • Basis Step ○ The statement P(2) is true ○ because any two lines in the plane that are not parallel meet in a common point. • Inductive hypothesis ○ P(k) is true for the positive integer k ≥ 2 ○ every set of k lines in the plane, no two of which are parallel, meet in a common point. • Inductive Step ○ We must show that if P(k) holds, then P(k+1) holds ○ If every set of k lines in the plane, no two of which are parallel, k ≥ 2, meet in a point ○ Then every set of k+1 lines in the plane, no two of which are parallel, meet in a point. ○ Consider a set of k + 1 distinct lines in the plane, no two parallel. ○ By the inductive hypothesis, the first k of these lines must meet in a common point p_1. ○ By the inductive hypothesis, the last k of these lines meet in a common point p_2. ○ If p_1 and p_2 are different points, all lines containing both of them must be the same line since two points determine a line. ○ This contradicts the assumption that the lines are distinct. ○ Hence, p_1=p_2 lies on all k + 1 distinct lines, and therefore P(k+1) holds. ○ Assuming that k ≥ 2, distinct lines meet in a common point ○ Then every k + 1 lines meet in a common point. • There must be an error in this proof since the conclusion is absurd. But where is the error? ○ P(k)→P(k+1) only holds for k≥3 ○ It is not the case that P(2) implies P(3) ○ The first two lines must meet in a common point p_1 and the second two must meet in a common point p_2 ○ They do not have to be the same point since only the second line is common to both sets of lines.
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