# It is not immediately obvious that this should even give an integer. Since 1 < 1 5

Save this PDF as:

Size: px
Start display at page:

Download "It is not immediately obvious that this should even give an integer. Since 1 < 1 5"

## Transcription

1 Math Introductory Seminar Lehigh University Spring 8 Notes on Fibonacci numbers, binomial coefficients and mathematical induction These are mostly notes from a previous class and thus include some material not covered in Math 163 For completeness this extra material is left in the notes Observe that these notes are somewhat informal Not all terms are defined and not all proofs are complete The material in these notes can be found with more detail in many cases in many different textbooks Fibonacci Numbers What are the Fibonacci numbers? The Fibonacci numbers are defined by the simple recurrence relation F n = F n 1 F n for n with F =, F 1 = 1 This gives the sequence F, F 1, F, =, 1, 1,, 3,, 8, 13, 1, 34,, 89, 144, 33, Each number in the sequence is the sum of the previous two numbers We read F as F naught These numbers show up in many areas of mathematics and in nature For example, the numbers of seeds in the outermost rows of sunflowers tend to be Fibonacci numbers A large sunflower will have and 89 seeds in the outer two rows Can we easily calculate large Fibonacci numbers without first calculating all smaller values using the recursion? Surprisingly, there is a simple and non-obvious formula for the Fibonacci numbers It is: F n = 1 1 n 1 1 n It is not immediately obvious that this should even give an integer Since 1 < 1 < it is approximately 618 the second term approaches as n gets large Thus the first term is a good approximation of the Fibonacci numbers In fact The Golden Ratio F n is the integer closest to 1 1 The number 1 shows up in many places and is called the Golden ratio or the Golden mean For one example, consider a rectangle with height 1 and width x If a vertical line is drawn in the middle so that the left side is a square and the right side is a smaller rectangle proportional to the original then x is the golden ratio To see this, note that for the rectangles to be proportional, the ratios of the longer sides to the smaller are equal That is x/1 = 1/x 1 So xx 1 = 1 x x 1 Using 1 n

2 the quadratic formula x must be 1 / or 1 / Since the second is negative, x is the first term, the golden ratio This proportion for rectangles shows up for example in the dimensions of the Parthenon and in art There is no evidence that the ancient Greek used the golden ratio in planning the parthenon Rather the shape seems to be aesthetically pleasing Da Vinci did sketches of the golden ration in relation to the human body There is debate as to whether or not he incorporated these ideas into his paintings Dali explicitly used the golden ratio in some of his paintings Other examples of recurrences Consider two other examples of simple recurrences If A n = A n 1 A n for n with A = and A 1 = we get the sequence,,, 4, 8, 6,, 16, 3, 3,, A number in this sequence is twice the previous number minus twice the number preceding the previous number The general expression for A n is even more surprising than that for Fibonacci numbers: A n = 1 i n 1 i n where i = 1 A simpler example that will be useful for illustration is B n = B n 1 6B n for n with B = 1 and B 1 = 8 This recurrence gives the sequence 1, 8, 14, 6, 146, 18, The general formula is B n = 3 n 1 n Mathematical Induction Later we will see how to easily obtain the formulas that we have given for F n, A n, B n For now we will use them to illustrate the method of mathematical induction We can prove these formulas correct once they are given to us even if we would not know how to discover the formulas First we give a proof using the idea of contradiction and that of a minimal counterexample This is essentially the same as what we will do with induction but using slightly different language Proposition: If B n = B n 1 6B n for n with B = 1 and B 1 = 8 then B n = 3 n 1 n Proof using the method of minimal counterexamples: We prove that the formula is correct by contradiction Assume that the formula is false Then there is some smallest value of n for which it is false Calling this value k we are assuming that the formula fails for k but holds for all smaller values That is, we assume that B k 3 k 1 k but that B k 1 = 3 k 1 1 k 1, B k = 3 k 1 k,, B = 3 1 Note first that substituting n = and n = 1 into the formula we get B = 3 1 = 1 and B 1 = = 8 So the formula works for n =, 1 Thus k and we can apply the recursion to B k Then using the recursion and the assumptions of the correctness

3 of the formulas for B k 1 and B k we get B k = B k 1 6B k 1 = [ 3 k 1 1 k 1] 6 [ 3 k 1 k ] = k 1 6 k 3 = 3 3 k 1 k 4 = 3 k 1 k Thus the formula holds for k, contradicting the assumption that k is the smallest number for which the formula fails So there can be no such number: the formula holds for all n =, 1,, The key to the proof was showing that if the formula is correct for B k and B k 1 then it is correct for B k These are the displayed computations above Those computations work for any k So we could use them along with the easily checked fact that the formula is correct for B and B 1 to show that it is correct for B Then since it is correct for B 1 and B we use the computations above to see that the formula is correct for B 3 Continuing this way we see that we can build up to showing that the formula is correct for any B n We display this as follows: Formula holds for B and B 1 using equations 1- formula holds for B Formula holds for B 1 and B using equations 1- formula holds for B 3 Formula holds for B and B 3 using equations 1- formula holds for B 4 Formula holds for B k and B k 1 using equations 1- formula holds for B k It is apparent that this shows that this shows that the formula works for all n =, 1,, The language used by mathematicians to describe and present this process is called mathematical induction This provides a succinct way of stating the ideas described above Technically it is equivalent to the minimal counterexample proof we gave above but in many situations the presentation is cleaner and shorter We present the same proof using the terminology of mathematical induction Proposition: If B n = B n 1 6B n for n with B = 1 and B 1 = 8 then B n = 3 n 1 n Proof using mathematical induction: We prove that the formula is correct using mathematical induction Since B = 3 1 = 1 and B 1 = = 8 the formula holds for n = and n = 1 For n, by induction B n = B n 1 6B n = [ 3 n 1 1 n 1] 6 [ 3 n 1 n ] = n 1 6 n = 3 3 n 1 n = 3 n 1 n 3

4 Hence the formula holds for all n =, 1, The words by induction sometimes by the induction hypothesis is used are shorthand for the idea described above that we have already proved the statement for smaller values of n and are presenting the method to get to the next value and that this can be repeated to show the statement for all n Induction and the well ordering principle Formal descriptions of the induction process can appear at first very abstract and hide the simplicity of the idea For completeness we give a version of a formal description of mathematical induction and also that of the well ordering principle on which the minimal counterexample proof was based The two are equivalent in that if either is assumed as a basic axiom then the other can be shown to follow from the axiom Well ordering principle - Every non-empty subset of integers contains a smallest element Principle of mathematical induction - If S n is a statement about the positive integer n such that S 1 is true and S k is true whenever S k 1 is true then S n is true for all positive integers Solving linear recurrences The example above with the Golden ratio and rectangles involved the quadratic x x 1 = We now explain how this same equation appears in finding a formula for the Fibonacci numbers This method works in general for the sorts of recurrences we gave in the examples These are linear: Terms like F n 1 appear by themselves and not together or in a function So for example F n 1 F n or Fn 1 13 or sinf n 1 are not allowed in a linear recurrence These are also homogeneous: there is no term like n or n not involving some F k Consider what would happen if we had a solution to the Fibonacci recurrence of the form F n = cα n where c is some constant and α Substituting into the recurrence we get cα n = cα n 1 cα n α = α1 Hence α α 1 = That is, α is a root of the quadratic x x 1 Multiples and sums of functions that satisfy the recurrence will also satisfy it So for Fibonacci numbers, both roots of x x 1 = satisfy the recurrence Multiplying each 1 by a constant and adding we get that λ n 1 1 n λ satisfies the recurrence If we can pick λ 1 and λ so that we get the correct values when n = and n = 1 then in fact 1 it will be the solution correct for all values Solving F = = λ 1 1 λ 1 and F 1 = 1 = λ λ we get λ1 = 1 and λ = 1 and hence the formula F n = 1 1 n 1 n 1 The Lucas numbers satisfy the same recursion as the Fibonacci numbers but have different initial conditions They are given by L n = L n 1 L n for n with L = and L 1 = 1 So the sequence is, 1, 3, 4, 7, 11, 18, 9, 47, From the discussion above, as with the Fibonacci numbers they will be of the form L n = λ 1 1 n λ 1 of λ 1 and λ For the Lucas numbers λ 1 = λ = 1 and we have L n = n but with different values 1 n 1 n For the recurrences A n = A n 1 A n and B n = B n 1 6B n we get that solutions will 4

5 be roots of x x = and x x 6 = respectively The roots of the first are 1 i and 1 i and the roots of the second are 3 and and these are what we see multiplied by a constant in the expressions we gave previously Using tools from linear algebra it can be shown that if the corresponding polynomial has degree d and its roots are distinct then d initial conditions will suffice to give a formula in a manner analogous to that described above When there are repeated roots things are only slightly more complicated As an example C n = C n 1 16C n C n 3 for n 3 with C = 1, C 1 = 1, C = 4 has the corresponding polynomial x 3 x 16x = x x So the roots are which is repeated and The general formula is C n = 1 n n n n Combinatorial description of Fibonacci numbers We have already noted that Fibonacci numbers show up both in nature and in many areas of mathematics The following five closely related objects are counted by Fibonacci numbers The Fibonacci numbers F n count each of the following: a the number of subsets of {1,,, n } with no consecutive elements That is, subsets that do not contain i and i 1 for any i b the number of binary string of length n with no consecutive 1 s That is, lists of s and 1 s such no two 1 s appear next to each other c the number of lists of 1 s and s with sum n 1 The lists can have any length as long as the sum of the entries is n 1 d the number of lists of odd positive integers with sum n e the number of lists of integers greater than 1 with sum n 1 We illustrate with n = 6, where F 6 = 8 a b c d e {1} {} {3} {4} {1,3} {1,4} {,4} So column a has all subsets of {1,, 3, 4} with no consecutive elements, column b has binary strings of length 4 with no consecutive 1 s, column c has lists of 1 s and s with sum, column d has lists of odd integers with sum 6 and column e has lists of integers greater than 1 with sum 7 There are several ways to show that the Fibonacci numbers give each of these counts Two methods are: Recursion - show that the objects being counted satisfy the same recurrence relation and initial conditions as the Fibonacci numbers Bijection - show a bijection, that is, a one to one correspondence, between the object and some other object known to be counted by the Fibonacci numbers

6 We will illustrate these methods with a few of the examples above but first we discuss bijections Bijections A bijection between two sets is a one-to-one pairing of all of the elements in one set with all of the elements in the other set If there is a bijection between two sets then they have the same cardinality There are a few technical remarks: formally a bijection is a function from one set to another that is both onto also called surjective and one-to-one also called injective So everything in each set is paired to something in the other set We have used the word cardinality rather than size to include infinite sets For infinite sets the cardinality is in some sense a measure of how infinite it is There is a bijection between the natural numbers and the rational numbers so both have the same cardinality even though it might seem at first glance that there should be more rational numbers These are both countable sets as we can list or count them It can be shown that there is no bijection between the natural numbers and the real numbers so the real numbers have a different cardinality They are called uncountable Infinite sets with the same cardinality as the natural numbers are countable those that do not have the same cardinality are uncountable So, while the reals are uncountable, there are other uncountable sets with different cardinality than the reals The continuum hypothesis states that there is no set with cardinality between that of the natural numbers and the reals Proving this was posed by Hilbert in 19 as one of a set of famous problems In 1963 Paul Cohen proved that the continuum hypothesis could neither be proved or disproved using a standard set of basic axioms That is, it is undecidable in this setting Proofs for combinatorial description of Fibonacci numbers We can think of subsets of {1,,, n} in two ways We can list the elements of the set or we can give a binary string of length n with 1 s in locations corresponding to the elements of the set and s elsewhere This binary list is called the characteristic vector For example {, 3,, 7} as a subset of {1,,, 8} is represented by 1111 Technically we have just described a bijection between binary lists of length n and subsets of {1,,, n} To formally prove that this is a bijection we would need to say a bit more but because this pairing is so clear we do not do so Similarly, there is a bijection between subsets of {1,,, n } with no consecutive elements and binary string of length n with no consecutive 1 s Again we do not prove this formally Note that by the bijection if one of these is counted by the Fibonacci numbers then so is the other To show that there are indeed F n binary string of length n with no consecutive 1 s we could either show a bijection between these string and something else we know to be counted by the Fibonacci numbers or show that they satisfy the Fibonacci recurrence and have the same initial conditions We will leave these as exercises To show that there are F n lists of 1 s and s with sum n 1 we will use the recurrence relation Proposition: The number of lists of 1 s and s with sum n 1 is the Fibonacci number F n Proof: Let R n for n 1 denote the set of all lists of 1 s and s with sum n 1 Our aim 6

7 is to show that R n = F n Note that there is one list in R 1, the empty list and one list in R, the list with a single 1 So R 1 = 1 = F 1 and R = 1 = F Now, consider R n and partition it into R 1 n and R n where R 1 n a contains those lists that end in a 1 and R n contains those lists that end in a This is a partition as each list in R n ends in exactly one of 1 or So R n = R 1 n R n Now, note that each element of R 1 n is a 1, list with sum n 1 ending in 1 Dropping the last 1 results in a 1, list with sum n Similarly, adding a 1 to the end of a 1, list with sum n results in a list with sum n 1 This establishes a bijection between 1, lists with sum n 1 ending in 1 and 1, lists with sum n Thus R 1 n = R n 1 Similarly, dropping last from lists in R n pairs them with lists in R n and we have R n = R n Thus we get that R n = R 1 n R n = R n 1 R n Thus the R n satisfy the Fibonacci recurrence and have the same initial conditions so R n = F n To show that the number of lists of odd positive integers with sum n and the number of lists of integers greater than 1 with sum n1 are each F n we could in a similar manner show that they satisfy the Fibonacci recurrence In the first case we would partition the lists based on whether the last term is 1 or not If it is 1 we drop the 1 to get a list with sum n 1 If it is not 1 it is at least 3 and we subtract from the last term to get a list with sum n We will not give a formal proof which would also need to show that the process described above is indeed a bijection, ie, that it can be reversed For the second case we would partition the lists based on whether the last term is or not, dropping it if it is a and subtracting 1 if it is not Again we skip the details Having shown that the number of 1, lists is F n we can also establish that the number of lists of odd positive integers with sum n and the number of lists of integers greater than 1 with sum n 1 are each F n by establishing a bijections Consider again these lists when n = 6: We need to find a way to pair the first column with each of the other two columns in a manner that will work in general for any n For the first two columns start first by adding a 1 to the front of each list in the first column so that each sum is now 6 Every consecutive string of s is now preceded by a 1 Replace each consecutive string of s and the preceding 1 by the sum of these terms So, for example we would replace 11 with 71 and replace 11 with 31 In the example for n = 6 this pairing put together entries in the same row It is not hard to see that the sum is preserved and that we only get odd numbers so we do indeed get lists of odd positive integers with sum n Also it is not hard to see that the process can be reversed, odd positive integers can 7

8 be replaced with a list consisting of a 1 followed by some number of s We will give a more formal description of this below For the first and third columns first start by adding a to the front of each list in the first column so that each sum is now 7 Now each consecutive string of 1 s is preceded by a Replace each consecutive string of 1 s and the preceding by the sum of these terms So, for example in 1 we replace the middle 1 with a 3 to get 3 and in 111 we replace the beginning 1 with 3 and the ending 11 with 4 to get 34 In the example for n = 6 this pairing put together entries in the same row Again we can show that this produces a bijection in general but we will omit the details Now we give a more formal version of the proof for the first two columns: Proposition: The number of lists of 1 s and s with sum n 1 is the same as the number of lists of odd positive integers with sum n Proof using a bijection: Let S n be the set of lists of 1 s and s with sum n 1 and T n be the set of lists of odd positive integers with sum n We will give a bijection between S n and T n to establish the result Given a list in S n, add a 1 to the start of the list The new list has sum n and each consecutive string of s appearing in the new list is preceded by a 1 Replace each consecutive string of s and the preceding 1 by the sum of the entries The sum must be odd and the only terms that are not changed are 1 s that are followed by another 1 Thus the result is a list of odd positive integers with sum n Reversing the process we can replace each odd integer k 1 3 in a list in T n with a string 1 having k s to get a list of 1 s and s starting with a 1 having sum n Dropping the first 1 gives a list in S n Each list in S n then clearly yields a list in T n and the reverse process takes the list in T n back to the original list in S n Thus there is a bijection between S n and T n Binomial coefficients In order to see another example in mathematics where Fibonacci numbers appear we first need to review binomial coefficients Use the symbol n k to represent the number of k element subsets of an n element set We read this as n choose k Although we do not need this fact now it is not hard to show that n k = n! where n! read as n factorial is the product of k!n k! all positive integers less than or equal to n That is, n! = n n 1 n 3 1 As a notational convenience we set! to be 1 The following table, which we will call the binomial triangle gives some small values of n k : 8

9 k n So, for example the entry in row 6 column 3 is 6 3 = The number of different size 3 subsets of a 6 element set is Note that we start counting at so the row labeled 6 is actually the 7 th row in the table The binomial coefficients satisfy a simple recurrence relation that is useful for making the binomial triangle As a concrete example consider a class with 18 students where the instructor decides that exactly 7 students will get A s and the rest will get F s The number of different groups subsets of 7 students that can be selected to get the A s is 18 choose 7, the binomial coefficient 18 7 To get a sense of how large these number are, this is 18 7 = 18! 7!11! = 31, 84 Imagine that the instructor has a favorite student The size 7 subsets can be partitioned into two collections, those that contain the favorite student and those that do not For those that contain the favorite, the instructor needs only to pick 6 students from the other 17 students, in 17 6 ways For those that do not contain the favorite, the instructor picks all 7 students from the remaining 17 students in 17 7 ways So we get 18 7 = In general we get the following which we provide with a slightly more formal proof that follows the idea of previous paragraph n n 1 n 1 Proposition: = k k 1 k Proof: Partition the k subsets of {1,,, n} into two parts, the subsets containing n and the subsets not containing n Each k subset of {1,,, n} containing n can be written as S {n} for some k 1 subset of {1,,, n 1} and hence the number of these is n 1 k 1 The k subsets of {1,,, n} not containing n are the k subsets of {1,,, n 1} and hence the number of these is n 1 k The result then follows This is sometimes called Pascal s identity and can be used to quickly determine entries in the binomial triangle The right diagonal and first column entries are 1 Each other entry is the sum of the entry directly above it plus the entry above and one column to the left The binomial triangle is often presented as an isosceles triagle with the 1 s forming the outer edges It is often called Pascal s triangle in western countries, Yang Hui s triangle in China and Kayyam s triangle in Persia Pascal s Triangle was discovered by Pingala around AD in India, by Kayyam in Persia around 1 AD and by Yang Hui in China around 1 AD all long before Pascal s discovery in 16 The binomial coefficients play an important 9

10 role in various areas of mathematics For example the rows give the coefficients in the expansion of x y n This result plays a role, as one example, in determining probabilities related to sequences of coin flips The binomial triangle and Fibonacci numbers Below we highlight two diagonal rows of the binomial triangle with italic and with bold numerals k n The sum of the italic diagonal is = 13 = F 7, the sum of the bold diagonal is = 34 = F 9 and the sum of the diagonal between these two is = 1 = F 8 That these are Fibonacci numbers is no accident The sums of the diagonals give the Fibonacci numbers In order to show this fact, we first need to get correct notation to express the observation of the previous paragraph and then we need to prove that it is correct Writing the binomial coefficients the examples above are F 7 = , F9 = and F 8 = We see that if we start in row n 1 we get Fn so we guess that the Fibonacci-binomial identity is n 1 n 1 n 3 n 1 n 1 if n is odd F n = n 1 n 1 n 3 n if n is even n 1 n n 3 n 1 i This is F n = where we understand 1 i that the sum stops when we get a term a b with a < b Proofs of the Fibonacci-binomial identity We will give two different proofs of the Fibonacci- binomial identity described above in order to illustrate to different methods One method, mathematical induction we have already encountered The second method, a combinatorial proof we have also already encountered The proof of the identity n k = n 1 k 1 n 1 k used an argument that involved counting size k subsets of n in two different ways Equating the expression gave the identity Using arguments that involve counting is the key to combinatorial proofs Indeed, what we called a bijective proof above can also be considered a special type of combinatorial proof 1 n

11 Proof using mathematical induction: It is straightforward to check that the identity holds for n = 1 and n = : F 1 = = 1 and F = 1 = 1 By induction, we assume that the identity holds for values less than n and show that it holds for n When n is even we get F n = F n 1 = n 1 1 F n = n 1 n 1 n 1 1 n 1 n 1 n 1 i 1 i 1 n 1 3 n 1 1 i i n 3 n 1 i i n n n 1 1 n 1 1 n The equalities in the first two rows follow by induction The bottom row is the sum of the top two rows and establishes the identity for F n The result on the left in the bottom follows by the Fibonacci recurrence and on the right from applying the binomial identity m r = m 1 r 1 m 1 r to each pair of terms and noting that for the first term n = 1 = n 1 Similarly the identity can be shown for F n when n is odd as follows: F n = F n 1 = n 1 1 F n = n 1 n 1 n 1 1 n 1 n 1 i 1 i 1 n 1 1 i i n 1 i i n 1 n 3 n 1 n 1 n1 n 3 n n 1 n 1 n 1 n 1 The equalities in the first two rows follow by induction The bottom row is the sum of the top two rows and establishes the identity for F n The result on the left in the bottom follows by the Fibonacci recurrence and on the right from applying the binomial identity m r = m 1 r 1 m 1 r to each pair of terms and noting that for the first term n = 1 = n 1 n 1 = 1 = n 1 and for the last term n 3 n 3 Thus by induction the identity holds for all n Proof using combinatorial methods: The number of lists of 1 s and s with sum n 1 is F n Partition these lists into S, S 1, S, where S i consists of those lists with i terms that are s and hence n 1 i terms that are 1 s Since the S i partition the lists ie, the S i are disjoint and their union is all of the lists we have that F n = S S 1 The lists in S i have length n 1 i i = n 1 i of which i are twos As there are n 1 i ways to the locations of the s we get that S i = n 1 i i n 1 n 1 n 3 n k k 1 establishing the identity Another Fibonacci identity Another identity involving Fibonacci numbers is i Hence Fn = S S 1 = 1 F F 1 F F n = F n 11

12 We will outline a proof by mathematical induction, a combinatorial proof and also discuss using notation Mathematical induction Learning mathematical induction may be a bit like learning to ride a bike It seems almost impossible at first and you fall and scrape your knees a few times but eventually you get it and it turns out that it isn t hard and you can really do a lot once you figure it out To get to the ideas behind a mathematical induction proof we describe the process in detail The final proof will hide all of this and be very short Substituting n = into the equation we get 1 F = F Since F = and F = 1 this is 1 = 1 and clearly true When n = 1 we get 1 F F 1 = F 3 With F = F 1 = 1 and F 3 = we have 1 1 = and the statement is true Using the recurrence for Fibonacci numbers we have F 3 = F F 1 and substituting F = 1 F which we have just established we get F 3 = F F 1 = 1 F F 1 as needed For n = we need to establish F 4 = 1 F F 1 F Using the Fibonacci recurrence and F 3 = 1F F 1 which we have just shown to be true we get F 4 = F 3 F = 1F F 1 F and the identity holds for n = Now imagine that we keep doing this and have shown the identity for n =, 1,,, 11 and we want to show it for n = 1 That is, we already know the following: F = 1 F F 3 = 1 F F 1 F 4 = 1 F F 1 F F 13 = 1 F F 1 F 11 We want to know show that F 14 = 1 F F 1 F 11 F 1 Substituting the last line above which we have already established to be true and the recurrence we get F 14 = F 13 F 1 = 1 F F 1 F 11 F 1 = 1 F F 1 F 1 So using the fact that the identity is known to be true for n =, 1,, 11 we have shown that it is true for n = 1 In fact we really only needed the fact that the identity is known to be true for n = 11 That it is true for n =, 1,, 1 is in this case extra unused information In some induction proofs we will need the truth of the statement for all smaller values There was nothing to special about n = 11 so just use the symbol n That is, we imagine that we have shown the identity true for 1,,, n 1 and we want to show that it is true for n So we assume that we already know the following: F = 1 F F 3 = 1 F F 1 F 4 = 1 F F 1 F F n 1 = 1 F F 1 F n 1 1

13 We want to use these fact and anything else we know in this case the Fibonacci recurrence to show the identity for n That is we need to show F n = 1 F F 1 F n using the assumptions above We can do this fairly easily by substituting the last line of the values assumed to be true, where the left side is F n 1 = F n1 and the recurrence to get F n = F n1 F n = 1 F F n 1 F n = 1 F F n 1 F n Observe that the second equality is simply using the associative property We didn t really need to do this but include it to make sure things are clear So we have shown that the identity holds at n when it is assumed to be true for 1,,, n 1 This we can imagine a process of building up values for which we know it is true, n = then n = 1 then n = then then n = 11 then n = 1 Clearly we can do this to get all numbers, 1,, and this established that the identity is true for all n This is mathematical induction The wording of a proof using mathematical induction is a way of describing the process we went through above without writing so much If we all understand that this is what is meant by induction then we can present the proof ideas very succinctly Here is how we do this using induction: Proof that 1 F F 1 F F n = F n by mathematical induction version 1 Since F = and F = 1 we have F = 1 = 1 = 1 F So the identity holds for n = For n 1 by the Fibonacci recurrence we have F n = F n1 F n and by induction we can assume F n1 = 1 F F n 1 Then F n = F n1 F n = 1 F F n 1 F n and the identity holds for n Thus by mathematical induction 1 F F 1 F F n = F n for n =, 1,, notation We next introduce some more notation for the proof above In this case the notation does not do too much to make the proof above shorter or more clear but in many instances it does so it is good practice to use the notation here We use n as shorthand for sums So means that we sum all of the expressions i= F i that we get substituting values i = 1,,, n into the expression after the That is, n F i = F F 1 F n i= Now we give the proof above again using this notation We also phrase it even slightly more succinctly to see how induction proofs are sometimes written By the basis we are referring to the small case n = that is checked directly Proof that 1 n i= F i = F n by mathematical induction version 13

14 The basis is F = 1 = 1 = 1 F For n 1 we have n 1 F n = F n1 F n = 1 F i F n = 1 i= n i= F i where the first equality is from the Fibonacci recurrence, the second by induction and the last by including F n in the sum So, by induction the identity holds for all n Combinatorial proof We also want to show 1 F F 1 F F n = F n using a counting argument This is what is known as a combinatorial proof In this instance the combinatorial proof is at least as long as the induction proof However, combinatorial proofs are in general a powerful technique that sometimes are much shorter than other methods They also are nice in that they do not require as much overhead such as, for example, having a good grasp on the technique of mathematical induction We recall that the Fibonacci numbers count the number of lists of 1 s and s with sum n 1 When n = and so n = 7 we have the following F 7 = 13 lists of 1 s and s with sum 6 We will soon see why they are arranged in the columns as done here This is almost too small to see a pattern but we can see something Observe that the lists in the last column all end in, those in the second to last column end in 1 etc That is, we have partition the lists based on how many 1 s follow the last The first column has the only list with seven 1 s and no s The second column has no lists since there are no lists with sum 6 ending with a followed by six 1 s the sum would be too big, the third column ends with a followed by five 1 s etc Consider the column with those lists that end in 1 The beginning of the list, preceding the end 1 has sum 6 3 = 3 and we know that there are F 4 = 3 such lists So this column should have size 3 For a given n the right side of the identity is F n which counts lists of 1 s and s with sum n 1 = n 1 The goal will be to partition the set of all lists with sum n 1 into subsets of the lists with sizes corresponding to the sizes given by Fibonacci numbers on the left side of the identity For any n we can partition the set of F n lists of 1 s and s with sum n 1 based on the number of 1 s following the last Observe that lists with sum n 1 that end with 11 1 where there are k terms that are 1 can be obtained by taking lists with sum n1 k = n k 1 and appending 11 1, with sum k So there are F n k such lists It seems that this partition the gives the count of F n as a sum of smaller Fibonacci numbers, plus 1 for the single list with all 1 s 14

15 What we need to do now is take the observation of the previous paragraph and make it into a proof that works for all n We give two versions of the same proof using slightly different notation and level of formality Combinatorial proof that 1 F F 1 F F n = F n version 1 Let S be the set of lists of 1 s and s with sum n 1 This has size F n Partition S into T T 1 T n 1 U where U consists of the single list with n 1 terms that are 1 and T k consists of all lists for which the last is followed by exactly k terms that are 1 s This is a partition Each list in S is in exactly one of these sets as any list with sum n 1 that contains a has at most n 1 terms that are 1 So S = T T 1 T n 1 U Given a list in T k, deleting the last and the 1 s that follow it results in a list with sum n 1 k = n k 1 Reversing this, adding a followed by k terms that are 1 s to a list with sum n k 1 yields a list in T k Thus there is a bijection between T k and lists of 1 s and s with sum n k 1 So T k = F n k Along with U by its definition and using F = we get F n = S and the identity is shown = U T n 1 T n T = 1 F F n n 1 F n n F n = 1 F F 1 F F n Combinatorial proof that 1 n i= F i = F n version There are F n lists of 1 s and s with sum n 1 There is one list of all 1 s The remaining lists each contain at least one There are F n k lists in which the last is followed by k terms that are 1 because such lists are determined by the part of the list preceding the last, which has sum n 1 k = n k 1 here k can be, 1,, n 1 Thus n 1 F n = 1 F n k = 1 k= n F i = 1 i=1 n i=1 F i where the last equality follows by adding F = and the second to last from the change of indices i = n k So the identity is shown 1

### CARDINALITY, COUNTABLE AND UNCOUNTABLE SETS PART ONE

CARDINALITY, COUNTABLE AND UNCOUNTABLE SETS PART ONE With the notion of bijection at hand, it is easy to formalize the idea that two finite sets have the same number of elements: we just need to verify

### 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

### MATH 289 PROBLEM SET 1: INDUCTION. 1. The induction Principle The following property of the natural numbers is intuitively clear:

MATH 89 PROBLEM SET : INDUCTION The induction Principle The following property of the natural numbers is intuitively clear: Axiom Every nonempty subset of the set of nonnegative integers Z 0 = {0,,, 3,

### Finite Sets. Theorem 5.1. Two non-empty finite sets have the same cardinality if and only if they are equivalent.

MATH 337 Cardinality Dr. Neal, WKU We now shall prove that the rational numbers are a countable set while R is uncountable. This result shows that there are two different magnitudes of infinity. But we

### Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 20

CS 70 Discrete Mathematics and Probability Theory Fall 009 Satish Rao, David Tse Note 0 Infinity and Countability Consider a function (or mapping) f that maps elements of a set A (called the domain of

### x if x 0, x if x < 0.

Chapter 3 Sequences In this chapter, we discuss sequences. We say what it means for a sequence to converge, and define the limit of a convergent sequence. We begin with some preliminary results about the

### Sets and Subsets. Countable and Uncountable

Sets and Subsets Countable and Uncountable Reading Appendix A Section A.6.8 Pages 788-792 BIG IDEAS Themes 1. There exist functions that cannot be computed in Java or any other computer language. 2. There

### MATHEMATICAL INDUCTION AND DIFFERENCE EQUATIONS

1 CHAPTER 6. MATHEMATICAL INDUCTION AND DIFFERENCE EQUATIONS 1 INSTITIÚID TEICNEOLAÍOCHTA CHEATHARLACH INSTITUTE OF TECHNOLOGY CARLOW MATHEMATICAL INDUCTION AND DIFFERENCE EQUATIONS 1 Introduction Recurrence

### This chapter describes set theory, a mathematical theory that underlies all of modern mathematics.

Appendix A Set Theory This chapter describes set theory, a mathematical theory that underlies all of modern mathematics. A.1 Basic Definitions Definition A.1.1. A set is an unordered collection of elements.

### 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,

### 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)

### 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

### 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

### 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

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

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

### Fibonacci Numbers. This is certainly the most famous

Fibonacci Numbers The Fibonacci sequence {u n } starts with 0, then each term is obtained as the sum of the previous two: un = un + un The first fifty terms are tabulated at the right. Our objective here

### 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.

### Finite and Infinite Sets

Chapter 9 Finite and Infinite Sets 9. Finite Sets Preview Activity (Equivalent Sets, Part ). Let A and B be sets and let f be a function from A to B..f W A! B/. Carefully complete each of the following

### vertex, 369 disjoint pairwise, 395 disjoint sets, 236 disjunction, 33, 36 distributive laws

Index absolute value, 135 141 additive identity, 254 additive inverse, 254 aleph, 466 algebra of sets, 245, 278 antisymmetric relation, 387 arcsine function, 349 arithmetic sequence, 208 arrow diagram,

### 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

### 3. Recurrence Recursive Definitions. To construct a recursively defined function:

3. RECURRENCE 10 3. Recurrence 3.1. Recursive Definitions. To construct a recursively defined function: 1. Initial Condition(s) (or basis): Prescribe initial value(s) of the function.. Recursion: Use a

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

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

### A Concrete Introduction. to the Abstract Concepts. of Integers and Algebra using Algebra Tiles

A Concrete Introduction to the Abstract Concepts of Integers and Algebra using Algebra Tiles Table of Contents Introduction... 1 page Integers 1: Introduction to Integers... 3 2: Working with Algebra Tiles...

### Math 4310 Handout - Quotient Vector Spaces

Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable

### Additional Topics in Linear Algebra Supplementary Material for Math 540. Joseph H. Silverman

Additional Topics in Linear Algebra Supplementary Material for Math 540 Joseph H Silverman E-mail address: jhs@mathbrownedu Mathematics Department, Box 1917 Brown University, Providence, RI 02912 USA Contents

### This chapter is all about cardinality of sets. At first this looks like a

CHAPTER Cardinality of Sets This chapter is all about cardinality of sets At first this looks like a very simple concept To find the cardinality of a set, just count its elements If A = { a, b, c, d },

### ON THE FIBONACCI NUMBERS

ON THE FIBONACCI NUMBERS Prepared by Kei Nakamura The Fibonacci numbers are terms of the sequence defined in a quite simple recursive fashion. However, despite its simplicity, they have some curious properties

### 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

### Georg Cantor (1845-1918):

Georg Cantor (845-98): The man who tamed infinity lecture by Eric Schechter Associate Professor of Mathematics Vanderbilt University http://www.math.vanderbilt.edu/ schectex/ In papers of 873 and 874,

### 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

### In mathematics you don t understand things. You just get used to them. (Attributed to John von Neumann)

Chapter 1 Sets and Functions We understand a set to be any collection M of certain distinct objects of our thought or intuition (called the elements of M) into a whole. (Georg Cantor, 1895) In mathematics

### Sets and Cardinality Notes for C. F. Miller

Sets and Cardinality Notes for 620-111 C. F. Miller Semester 1, 2000 Abstract These lecture notes were compiled in the Department of Mathematics and Statistics in the University of Melbourne for the use

### Notes on counting finite sets

Notes on counting finite sets Murray Eisenberg February 26, 2009 Contents 0 Introduction 2 1 What is a finite set? 2 2 Counting unions and cartesian products 4 2.1 Sum rules......................................

### Chapter 3. Distribution Problems. 3.1 The idea of a distribution. 3.1.1 The twenty-fold way

Chapter 3 Distribution Problems 3.1 The idea of a distribution Many of the problems we solved in Chapter 1 may be thought of as problems of distributing objects (such as pieces of fruit or ping-pong balls)

### Answer Key for California State Standards: Algebra I

Algebra I: Symbolic reasoning and calculations with symbols are central in algebra. Through the study of algebra, a student develops an understanding of the symbolic language of mathematics and the sciences.

### CHAPTER 3. Sequences. 1. Basic Properties

CHAPTER 3 Sequences We begin our study of analysis with sequences. There are several reasons for starting here. First, sequences are the simplest way to introduce limits, the central idea of calculus.

### 0 ( x) 2 = ( x)( x) = (( 1)x)(( 1)x) = ((( 1)x))( 1))x = ((( 1)(x( 1)))x = ((( 1)( 1))x)x = (1x)x = xx = x 2.

SOLUTION SET FOR THE HOMEWORK PROBLEMS Page 5. Problem 8. Prove that if x and y are real numbers, then xy x + y. Proof. First we prove that if x is a real number, then x 0. The product of two positive

### 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

### 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

### Interpolating Polynomials Handout March 7, 2012

Interpolating Polynomials Handout March 7, 212 Again we work over our favorite field F (such as R, Q, C or F p ) We wish to find a polynomial y = f(x) passing through n specified data points (x 1,y 1 ),

### 1. R In this and the next section we are going to study the properties of sequences of real numbers.

+a 1. R In this and the next section we are going to study the properties of sequences of real numbers. Definition 1.1. (Sequence) A sequence is a function with domain N. Example 1.2. A sequence of real

### Equivalence relations

Equivalence relations A motivating example for equivalence relations is the problem of constructing the rational numbers. A rational number is the same thing as a fraction a/b, a, b Z and b 0, and hence

### 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)

### 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.

### List of Standard Counting Problems

List of Standard Counting Problems Advanced Problem Solving This list is stolen from Daniel Marcus Combinatorics a Problem Oriented Approach (MAA, 998). This is not a mathematical standard list, just a

### 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

### 6.3 Conditional Probability and Independence

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

### MATH Fundamental Mathematics II.

MATH 10032 Fundamental Mathematics II http://www.math.kent.edu/ebooks/10032/fun-math-2.pdf Department of Mathematical Sciences Kent State University December 29, 2008 2 Contents 1 Fundamental Mathematics

### 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

### Pythagorean Triples. Chapter 2. a 2 + b 2 = c 2

Chapter Pythagorean Triples The Pythagorean Theorem, that beloved formula of all high school geometry students, says that the sum of the squares of the sides of a right triangle equals the square of the

### LINEAR RECURSIVE SEQUENCES. The numbers in the sequence are called its terms. The general form of a sequence is. a 1, a 2, a 3,...

LINEAR RECURSIVE SEQUENCES BJORN POONEN 1. Sequences A sequence is an infinite list of numbers, like 1) 1, 2, 4, 8, 16, 32,.... The numbers in the sequence are called its terms. The general form of a sequence

### Module 6: Basic Counting

Module 6: Basic Counting Theme 1: Basic Counting Principle We start with two basic counting principles, namely, the sum rule and the multiplication rule. The Sum Rule: If there are n 1 different objects

### 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

### CHAPTER 2. Inequalities

CHAPTER 2 Inequalities In this section we add the axioms describe the behavior of inequalities (the order axioms) to the list of axioms begun in Chapter 1. A thorough mastery of this section is essential

### Continued fractions and good approximations.

Continued fractions and good approximations We will study how to find good approximations for important real life constants A good approximation must be both accurate and easy to use For instance, our

### MTH6109: Combinatorics. Professor R. A. Wilson

MTH6109: Combinatorics Professor R. A. Wilson Autumn Semester 2011 2 Lecture 1, 26/09/11 Combinatorics is the branch of mathematics that looks at combining objects according to certain rules (for example:

### 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

### God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)

Chapter 2 Numbers God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) God created the integers and the rest is the work

### TREES IN SET THEORY SPENCER UNGER

TREES IN SET THEORY SPENCER UNGER 1. Introduction Although I was unaware of it while writing the first two talks, my talks in the graduate student seminar have formed a coherent series. This talk can be

### 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

### Mathematics for Computer Science

Mathematics for Computer Science Lecture 2: Functions and equinumerous sets Areces, Blackburn and Figueira TALARIS team INRIA Nancy Grand Est Contact: patrick.blackburn@loria.fr Course website: http://www.loria.fr/~blackbur/courses/math

### NOTES ON COUNTING KARL PETERSEN

NOTES ON COUNTING KARL PETERSEN It is important to be able to count exactly the number of elements in any finite set. We will see many applications of counting as we proceed (number of Enigma plugboard

### Assignment 5 - Due Friday March 6

Assignment 5 - Due Friday March 6 (1) Discovering Fibonacci Relationships By experimenting with numerous examples in search of a pattern, determine a simple formula for (F n+1 ) 2 + (F n ) 2 that is, a

### f(x) is a singleton set for all x A. If f is a function and f(x) = {y}, we normally write

Math 525 Chapter 1 Stuff If A and B are sets, then A B = {(x,y) x A, y B} denotes the product set. If S A B, then S is called a relation from A to B or a relation between A and B. If B = A, S A A is called

### Notes on Linear Recurrence Sequences

Notes on Linear Recurrence Sequences April 8, 005 As far as preparing for the final exam, I only hold you responsible for knowing sections,,, 6 and 7 Definitions and Basic Examples An example of a linear

### 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.

### 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,

### Topics in Number Theory

Chapter 8 Topics in Number Theory 8.1 The Greatest Common Divisor Preview Activity 1 (The Greatest Common Divisor) 1. Explain what it means to say that a nonzero integer m divides an integer n. Recall

### The Fibonacci sequence satisfies a number of identities among it members. The Fibonacci numbers have some remarkable divisibility properties.

QuestionofDay Question of the Day What mathematical identities do you know? Do you know any identities or relationships that are satisfied by special sets of integers, such as the sequence of odd numbers

### Cartesian Products and Relations

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

### 2. 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

### Markov Chains, part I

Markov Chains, part I December 8, 2010 1 Introduction A Markov Chain is a sequence of random variables X 0, X 1,, where each X i S, such that P(X i+1 = s i+1 X i = s i, X i 1 = s i 1,, X 0 = s 0 ) = P(X

### Chapter Three. Functions. In this section, we study what is undoubtedly the most fundamental type of relation used in mathematics.

Chapter Three Functions 3.1 INTRODUCTION In this section, we study what is undoubtedly the most fundamental type of relation used in mathematics. Definition 3.1: Given sets X and Y, a function from X to

### Algebra I Notes Review Real Numbers and Closure Unit 00a

Big Idea(s): Operations on sets of numbers are performed according to properties or rules. An operation works to change numbers. There are six operations in arithmetic that "work on" numbers: addition,

### Fibonacci Numbers Modulo p

Fibonacci Numbers Modulo p Brian Lawrence April 14, 2014 Abstract The Fibonacci numbers are ubiquitous in nature and a basic object of study in number theory. A fundamental question about the Fibonacci

### Lecture 1 (Review of High School Math: Functions and Models) Introduction: Numbers and their properties

Lecture 1 (Review of High School Math: Functions and Models) Introduction: Numbers and their properties Addition: (1) (Associative law) If a, b, and c are any numbers, then ( ) ( ) (2) (Existence of an

### Reading 7 : Program Correctness

CS/Math 240: Introduction to Discrete Mathematics Fall 2015 Instructors: Beck Hasti, Gautam Prakriya Reading 7 : Program Correctness 7.1 Program Correctness Showing that a program is correct means that

### Discrete Structures for Computer Science: Counting, Recursion, and Probability

Discrete Structures for Computer Science: Counting, Recursion, and Probability Michiel Smid School of Computer Science Carleton University Ottawa, Ontario Canada michiel@scs.carleton.ca December 15, 2016

### Discrete Mathematics & Mathematical Reasoning Chapter 6: Counting

Discrete Mathematics & Mathematical Reasoning Chapter 6: Counting Colin Stirling Informatics Slides originally by Kousha Etessami Colin Stirling (Informatics) Discrete Mathematics (Chapter 6) Today 1 /

### Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

### 1 Algebra - Partial Fractions

1 Algebra - Partial Fractions Definition 1.1 A polynomial P (x) in x is a function that may be written in the form P (x) a n x n + a n 1 x n 1 +... + a 1 x + a 0. a n 0 and n is the degree of the polynomial,

### 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

### MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

### 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

### RELATIONS AND FUNCTIONS

Chapter 1 RELATIONS AND FUNCTIONS There is no permanent place in the world for ugly mathematics.... It may be very hard to define mathematical beauty but that is just as true of beauty of any kind, we

### Betweenness of Points

Math 444/445 Geometry for Teachers Summer 2008 Supplement : Rays, ngles, and etweenness This handout is meant to be read in place of Sections 5.6 5.7 in Venema s text [V]. You should read these pages after

### Chapter 1 Basic Number Concepts

Draft of September 2014 Chapter 1 Basic Number Concepts 1.1. Introduction No problems in this section. 1.2. Factors and Multiples 1. Determine whether the following numbers are divisible by 3, 9, and 11:

### Math 55: Discrete Mathematics

Math 55: Discrete Mathematics UC Berkeley, Spring 2012 Homework # 9, due Wednesday, April 11 8.1.5 How many ways are there to pay a bill of 17 pesos using a currency with coins of values of 1 peso, 2 pesos,

### The program also provides supplemental modules on topics in geometry and probability and statistics.

Algebra 1 Course Overview Students develop algebraic fluency by learning the skills needed to solve equations and perform important manipulations with numbers, variables, equations, and inequalities. Students

### For the purposes of this course, the natural numbers are the positive integers. We denote by N, the set of natural numbers.

Lecture 1: Induction and the Natural numbers Math 1a is a somewhat unusual course. It is a proof-based treatment of Calculus, for all of you who have already demonstrated a strong grounding in Calculus

### 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

### Section 6-2 Mathematical Induction

6- Mathematical Induction 457 In calculus, it can be shown that e x k0 x k k! x x x3!! 3!... xn n! where the larger n is, the better the approximation. Problems 6 and 6 refer to this series. Note that

### Pythagorean Triples, Complex Numbers, Abelian Groups and Prime Numbers

Pythagorean Triples, Complex Numbers, Abelian Groups and Prime Numbers Amnon Yekutieli Department of Mathematics Ben Gurion University email: amyekut@math.bgu.ac.il Notes available at http://www.math.bgu.ac.il/~amyekut/lectures

### Announcements. CompSci 230 Discrete Math for Computer Science. Test 1

CompSci 230 Discrete Math for Computer Science Sep 26, 2013 Announcements Exam 1 is Tuesday, Oct. 1 No class, Oct 3, No recitation Oct 4-7 Prof. Rodger is out Sep 30-Oct 4 There is Recitation: Sept 27-30.

### 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

### LEARNING OBJECTIVES FOR THIS CHAPTER

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

### SECTION 10-2 Mathematical Induction

73 0 Sequences and Series 6. Approximate e 0. using the first five terms of the series. Compare this approximation with your calculator evaluation of e 0.. 6. Approximate e 0.5 using the first five terms

### page 1 Golden Ratio Project

Golden Ratio Project When to use this project: Golden Ratio artwork can be used with the study of Ratios, Patterns, Fibonacci, or Second degree equation solutions and with pattern practice, notions of