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COMPSCI 102. Introduction to Discrete Mathematics. X 1. X 2. +. +. X 3. Counting III. Lecture 8 (September 21, 2007). How many ways to rearrange the letters in the word “SYSTEMS” ?. SYSTEMS.

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slide1

COMPSCI 102

Introduction to Discrete Mathematics

slide2

X1

  • X2
  • +
  • +
  • X3

Counting III

Lecture 8 (September 21, 2007)

slide4

SYSTEMS

7 places to put the Y, 6 places to put the T, 5 places to put the E, 4 places to put the M, and the S’s are forced

7 X 6 X 5 X 4 = 840

slide5

7!

= 840

3!

SYSTEMS

Let’s pretend that the S’s are distinct:

S1YS2TEMS3

There are 7! permutations of S1YS2TEMS3

But when we stop pretending we see that we have counted each arrangement of SYSTEMS 3! times, once for each of 3! rearrangements of S1S2S3

slide6

n-r1

n - r1 - r2 - … - rk-1

r2

rk

n!

=

(n-r1)!r1!

n

r1

n!

=

r1!r2! … rk!

Arrange n symbols: r1 of type 1, r2 of type 2, …, rk of type k

(n-r1)!

(n-r1-r2)!r2!

slide7

CARNEGIEMELLON

14!

= 3,632,428,800

2!3!2!

slide8

Remember:

The number of ways to arrange n symbols with r1 of type 1, r2 of type 2, …, rk of type k is:

n!

r1!r2! … rk!

slide9

5 distinct pirates want to divide 20 identical, indivisible bars of gold. How many different ways can they divide up the loot?

slide10

Sequences with 20 G’s and 4 /’s

GG/G//GGGGGGGGGGGGGGGGG/

represents the following division among the pirates: 2, 1, 0, 17, 0

In general, the ith pirate gets the number of G’s after the (i-1)st/ and before the ith/

This gives a correspondence (bijection) between divisions of the gold and sequences with 20 G’s and 4 /’s

slide11

24

4

How many different ways to divide up the loot?

Sequences with 20 G’s and 4 /’s

slide12

n + k - 1

n + k - 1

n - 1

k

How many different ways can n distinct pirates divide k identical, indivisible bars of gold?

=

slide13

24

4

How many integer solutions to the following equations?

x1 + x2 + x3 + x4 + x5 = 20

x1, x2, x3, x4, x5 ≥ 0

Think of xk are being the number of gold bars that are allotted to pirate k

slide14

n + k - 1

n + k - 1

n - 1

k

How many integer solutions to the following equations?

x1 + x2 + x3 + … + xn = k

x1, x2, x3, …, xn ≥ 0

=

slide15

12

7

Identical/Distinct Dice

Suppose that we roll seven dice

How many different outcomes are there, if order matters?

67

What if order doesn’t matter?(E.g., Yahtzee)

(Corresponds to 6 pirates and 7 bars of gold)

slide16

Multisets

A multiset is a set of elements, each of which has a multiplicity

The size of the multiset is the sum of the multiplicities of all the elements

Example:

{X, Y, Z} with m(X)=0 m(Y)=3, m(Z)=2

Unary visualization: {Y, Y, Y, Z, Z}

slide17

n + k - 1

n + k - 1

n - 1

k

Counting Multisets

There number of ways

to choose a multiset of

size k from n types of elements is:

=

slide18

Polynomials Express Choices and Outcomes

Products of Sum = Sums of Products

(

)

(

) =

+

+

+

+

+

+

+

+

slide19

b3

b1

b2

t2

t1

t2

t1

t2

t1

b1t1

b1t2

b2t1

b2t2

b3t1

b3t2

(b1+b2+b3)(t1+t2) =

b1t1

+

b1t2

+

b2t1

+

b2t2

+

b3t1

+

b3t2

slide20

There is a correspondence between paths in a choice tree and the cross terms of the product of polynomials!

slide21

Choice Tree for Terms of (1+X)3

1

X

1

X

1

X

1

X

1

X

1

X

1

X

X2

X2

X3

X

X

X2

X

1

Combine like terms to get 1 + 3X + 3X2 + X3

slide22

n

ck =

k

What is a Closed Form Expression For ck?

(1+X)n = c0 + c1X + c2X2 + … + cnXn

(1+X)(1+X)(1+X)(1+X)…(1+X)

After multiplying things out, but before combining like terms, we get 2n cross terms, each corresponding to a path in the choice tree

ck, the coefficient of Xk, is the number of paths with exactly k X’s

slide23

Binomial Coefficients

n

n

n

n

1

0

The Binomial Formula

(1+X)n =

X0

+

X1

+…+

Xn

binomial expression

slide24

The Binomial Formula

(1+X)0 =

1

(1+X)1 =

1 + 1X

(1+X)2 =

1 + 2X + 1X2

(1+X)3 =

1 + 3X + 3X2 + 1X3

(1+X)4 =

1 + 4X + 6X2 + 4X3 + 1X4

slide26

What is the coefficient of EMS3TY in the expansion of

(E + M + S + T + Y)7?

The number of ways to rearrange the letters in the word SYSTEMS

slide27

What is the coefficient of BA3N2 in the expansion of

(B + A + N)6?

The number of ways to rearrange the letters in the word BANANA

slide28

What is the coefficient of (X1r1X2r2…Xkrk)

in the expansion of

(X1+X2+X3+…+Xk)n?

n!

r1!r2!...rk!

slide30

Xk

=

k = 0

= 0

n

k

n

n

k

k

Power Series Representation

n

(1+X)n =

Xk

k = 0

For k>n,

“Product form” or

“Generating form”

“Power Series” or “Taylor Series” Expansion

slide31

n

2n =

k = 0

n

n

k

k

By playing these two representations against each other we obtain a new representation of a previous insight:

n

(1+X)n =

Xk

k = 0

Let x = 1,

The number of subsets of an n-element set

slide32

n

(-1)k

0 =

k = 0

n

n

n

n

n

n

=

k

k

k

k

k even

k odd

By varying x, we can discover new identities:

n

(1+X)n =

Xk

k = 0

Let x = -1,

Equivalently,

slide34

n

k

n

(1+X)n =

Xk

k = 0

Proofs that work by manipulating algebraic forms are called “algebraic” arguments. Proofs that build a bijection are called “combinatorial” arguments

slide35

n

n

n

n

=

k

k

k even

k odd

Let On be the set of binary strings of length n with an odd number of ones.

Let En be the set of binary strings of length n with an even number of ones.

We gave an algebraic proof that

On  =  En

slide36

A Combinatorial Proof

Let On be the set of binary strings of length n with an odd number of ones

Let En be the set of binary strings of length n with an even number of ones

A combinatorial proof must construct a bijection between On and En

slide37

An Attempt at a Bijection

Let fn be the function that takes an

n-bit string and flips all its bits

fn is clearly a one-to-one and onto function

for odd n. E.g. in f7 we have:

...but do even n work? In f6 we have

0010011  1101100

1001101  0110010

110011  001100

101010  010101

Uh oh. Complementing maps evens to evens!

slide38

A Correspondence That Works for all n

Let fn be the function that takes an n-bit string and flips only the first bit. For example,

0010011  1010011

1001101  0001101

110011  010011

101010  001010

slide39

n

k

n

(1+X)n =

Xk

k = 0

The binomial coefficients have so many representations that many fundamental mathematical identities emerge…

slide40

The Binomial Formula

(1+X)0 =

1

(1+X)1 =

1 + 1X

(1+X)2 =

1 + 2X + 1X2

(1+X)3 =

1 + 3X + 3X2 + 1X3

(1+X)4 =

1 + 4X + 6X2 + 4X3 + 1X4

Pascal’s Triangle: kth row are coefficients of (1+X)k

Inductive definition of kth entry of nth row:Pascal(n,0) = Pascal (n,n) = 1; Pascal(n,k) = Pascal(n-1,k-1) + Pascal(n-1,k)

slide41

0

3

1

3

1

3

2

3

2

2

= 1

= 1

= 1

= 1

= 1

= 1

= 3

= 3

= 2

= 1

0

1

2

0

1

1

0

2

0

3

“Pascal’s Triangle”

  • Al-Karaji, Baghdad 953-1029
  • Chu Shin-Chieh 1303
  • Blaise Pascal 1654
slide42

Pascal’s Triangle

“It is extraordinary

how fertile in

properties the

triangle is.

Everyone can

try his

hand”

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

slide43

n

2n =

k = 0

n

k

Summing the Rows

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

= 1

+

+ +

+ + +

+ + + +

+ + + + +

+ + + + + +

= 2

= 4

= 8

= 16

= 32

= 64

slide44

Odds and Evens

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

1 + 15 + 15 + 1

=

6 + 20 + 6

slide45

n

i

1

i = 1

n

i

n+1

i = 1

2

Summing on 1st Avenue

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

=

=

slide46

n

i

k

i = k

n+1

k+1

Summing on kth Avenue

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

=

slide47

Fibonacci Numbers

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

= 2

= 3

= 5

= 8

= 13

slide48

Sums of Squares

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

2

2

2

2

2

2

2

slide49

Al-Karaji Squares

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

= 1

+2

= 4

+2

= 9

+2

= 16

+2

= 25

+2

= 36

slide51

All these properties can be proved inductively and algebraically. We will give combinatorial proofs using the Manhattan block walking representation of binomial coefficients

slide52

10

5

How many shortest routes from A to B?

A

B

slide53

j+k

k

Manhattan

0

0

1

1

jth street

kth avenue

2

2

3

3

4

4

There are

shortest routes from (0,0) to (j,k)

slide54

n

k

Manhattan

0

0

1

Level n

kth avenue

1

2

2

3

3

4

4

There are

shortest routes from (0,0) to (n-k,k)

slide55

n

k

Manhattan

0

0

1

Level n

kth avenue

1

2

2

3

3

4

4

shortest routes from (0,0) to

There are

level n and kth avenue

slide56

0

0

1

Level n

kth avenue

1

1

2

1

1

2

3

1

1

2

3

4

1

3

1

3

4

4

4

1

1

6

1

5

1

5

10

10

6

15

6

20

15

slide57

n-1

n-1

n

k-1

k

k

0

Level n

kth avenue

1

1

1

1

2

1

1

2

3

1

3

1

3

4

4

4

1

1

6

1

5

1

5

10

10

+

6

15

6

20

15

=

+

slide58

n

2

2n

n

n

k

k = 0

0

0

1

Level n

kth avenue

1

2

2

3

3

4

4

=

slide59

n

n+1

i

=

k+1

k

i = k

0

0

1

Level n

kth avenue

1

2

2

3

3

4

4

slide60

Vector Programs

Let’s define a (parallel) programming language called VECTOR that operates on possibly infinite vectors of numbers. Each variable V! can be thought of as:

< * , * , * , * , *, *, … >

slide61

Vector Programs

Let k stand for a scalar constant

<k> will stand for the vector <k,0,0,0,…>

<0> = <0,0,0,0,…>

<1> = <1,0,0,0,…>

V! + T! means to add the vectors position-wise

<4,2,3,…> + <5,1,1,….> = <9,3,4,…>

slide62

Vector Programs

RIGHT(V!) means to shift every number in V! one position to the right and to place a 0 in position 0

RIGHT( <1,2,3, …> ) = <0,1,2,3,…>

slide63

Vector Programs

Example:

Store:

V! := <6>;

V! := RIGHT(V!) + <42>;

V! := RIGHT(V!) + <2>;

V! := RIGHT(V!) + <13>;

V! = <6,0,0,0,…>

V! = <42,6,0,0,…>

V! = <2,42,6,0,…>

V!= <13,2,42,6,…>

V! = < 13, 2, 42, 6, 0, 0, 0, … >

slide64

Vector Programs

Example:

Store:

V! := <1>;

Loop n times

V! := V! + RIGHT(V!);

V! = <1,0,0,0,…>

V! = <1,1,0,0,…>

V! = <1,2,1,0,…>

V!= <1,3,3,1,…>

V! = nth row of Pascal’s triangle

slide65

X1

  • X2
  • +
  • +
  • X3

Vector programs can be implemented by polynomials!

slide66

PV =

aiXi

i = 0

Programs  Polynomials

The vector V! = < a0, a1, a2, . . . > will be represented by the polynomial:

slide67

PV =

aiXi

i = 0

Formal Power Series

The vector V! = < a0, a1, a2, . . . > will be represented by the formal power series:

slide68

PV =

aiXi

i = 0

V! = < a0, a1, a2, . . . >

<0> is represented by

0

<k> is represented by

k

V! + T! is represented by

(PV + PT)

RIGHT(V!) is represented by

(PV X)

slide69

Vector Programs

Example:

V! := <1>;

Loop n times

V! := V! + RIGHT(V!);

PV := 1;

PV := PV + PV X;

V! = nth row of Pascal’s triangle

slide70

Vector Programs

Example:

V! := <1>;

Loop n times

V! := V! + RIGHT(V!);

PV := 1;

PV := PV(1+X);

V! = nth row of Pascal’s triangle

slide71

Vector Programs

Example:

V! := <1>;

Loop n times

V! := V! + RIGHT(V!);

PV = (1+ X)n

V! = nth row of Pascal’s triangle

slide72

Polynomials count

  • Binomial formula
  • Combinatorial proofs of binomial identities
  • Vector programs

Here’s What You Need to Know…