COMPSCI 230
Discrete Mathematics for
Computer Science
Counting III
Lecture 8
1
X+
2
X+
3
X
How many ways to rearrange the
letters in the word
“CARNEGIEMELLON”?
14
2
positions of the Ns
12
3
positions of the Es
9
2
positions of the Ls
7!
ways to order the rest
Arrange n symbols: r1 of type 1,
r2 of type 2, …, rk of type k
n
r1
n-r1
…
r2
=
=
n - r1 - r2 - … - rk-1
rk
n!
(n-r1)!
(n-r1)!r1! (n-r1-r2)!r2!
n!
r1!r2! … rk!
…
CARNEGIEMELLON
14!
= 3,632,428,800
2!3!2!
5 distinct pirates want to divide
20 identical, indivisible bars of
gold. How many different ways
can they divide up the loot?
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
How many different ways to
divide up the loot?
Sequences with 20 G’s and 4 /’s
24
4
How many different ways can n
distinct pirates divide k identical,
indivisible bars of gold?
n+k-1
n-1
=
n+k-1
k
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)
12
7
(Corresponds to 6 pirates and 7 bars of gold)
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}
Counting Multisets
There number of ways
to choose a multiset of
size k from n types of elements is:
n+k-1
n+k-1
=
n-1
k
Taking k items from a set of n
Order matters, no repetition:
n!
(n-k)!
Order matters, repetition allowed:
Order doesn’t matter, no repetition:
nk
n
k
Order doesn’t matter, repetition allowed:
n+k-1
k
Polynomials Express
Choices and Outcomes
Products of Sum = Sums of Products
(
+
+
+
)(
+
+
+
+
)=
+
b3
b1
b2
t2
t1
t2
t1
t2
b1t1 b1t2
b2t1
b2t2
b3t1
b3t2
t1
(b1+b2+b3)(t1+t2) = b1t1 + b1t2 + b2t1 + b2t2 + b3t1 + b3t2
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
ck =
n
k
The Binomial Formula
(1+X)n
n 0
n 1
n n
=
X +
X +…+
X
0
1
n
Binomial Coefficients
binomial
expression
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
What is the
coefficient of EMSTY
in the expansion of
(E + M + S + T + Y)5?
5!
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
What is the coefficient
of (X1r1X2r2…Xkrk)
in the expansion of
(X1+X2+X3+…+Xk)n?
n!
r1!r2!...rk!
Power Series Representation
n
(1+X)n
=

k=0

“Product form” or
“Generating form”
=

k=0
n
k
n
k
Xk
Xk
For k>n,
n
k
=0
“Power Series” or “Taylor Series” Expansion
By playing these two representations
against each other we obtain a new
representation of a previous insight:
n
(1+X)n
=

k=0
n
Let x = 1,
2n =

k=0
n
k
Xk
n
k
The number of subsets
of an n-element set
By varying x, we can discover new
identities:
n
(1+X)n

=
k=0
n
Let x = -1,
0=

k=0
n
Equivalently,

k odd
n
k
Xk
n
(-1)k
k
n
k
n
=

k even
n
k
The number of subsets
with even size is the
same as the number of
subsets with odd size
n
(1+X)n
=

k=0
n
k
Xk
Proofs that work by manipulating
algebraic forms are called
“algebraic” arguments. Proofs that
build a bijection are called
“combinatorial” arguments
n

k odd
n
k
n
=

k even
n
k
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 
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
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-toone 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!
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
n
(1+X)n
=

k=0
n
k
Xk
The binomial coefficients have so
many representations that many
fundamental mathematical identities
emerge…
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)
“Pascal’s Triangle”
0 =1
0
1 =1
0
2 =1
0
3 =1
0
1 =1
1
2 =2
1
3 =3
1
2 =1
2
3 =3
2
• Al-Karaji, Baghdad 953-1029
• Chu Shin-Chieh 1303
• Blaise Pascal 1654
3 =1
3
Pascal’s Triangle
“It is extraordinary
1
how fertile in
properties the
1
1
triangle is.
1
2
1
Everyone can
try his
1
3
3
1
hand”
1
4
6
4
1
1
1
5
6
10
15
10
20
5
15
1
6
1
Summing the Rows
n
2n
=

n
k
1
=1
1 + 1
=2
1 + 2 + 1
=4
1 + 3 + 3 + 1
=8
1 + 4 + 6 + 4 + 1
= 16
1 + 5 + 10 + 10 + 5 + 1
= 32
1 + 6 + 15 + 20 + 15 + 6 + 1
= 64
k=0
Odds and Evens
1
1
1
1
1
1
1
2
3
4
5
6
1
3
6
10
15
1
1
4
10
20
1
5
15
1
6
1 + 15 + 15 + 1 = 6 + 20 + 6
1
Summing on 1st Avenue
n

1
1
1
1
1
1
1
2
3
4
5
6
1
15

i=1
1
6
1
4
10
20
i =
i=1
3
10
n
1
5
15
1
6
1
i = n+1
2
1
Summing on kth Avenue
1
1
1
1
1
1
1
6
2
4
6
1
4
10
20
i=k
1
3
10
15

1
3
5
n
1
5
15
1
6
1
i = n+1
k+1
k
Fibonacci Numbers
1
=2
1
1
=3
=
5
1
2
1
=8
1
3
3
1 = 13
1
1
1
4
5
6
6
10
15
4
10
20
1
5
15
1
6
1
Sums of Squares
1
1
1
2
2
1
1
1
1
1
2
6
2
3
2
4
5
1
2
2
3
6
10
15
2
1
4
10
20
1
5
15
1
6
1
Al-Karaji Squares
1
1
=1
2 +21
=4
1
1
3 +2 3
1
1
1
1
4 +26
5 +210
6 +215
4
10
20
=9
1
5
15
= 16
1
= 25
1
6
1
= 36
Pascal Mod 2
All these properties can
be proved inductively
and algebraically. We
will give combinatorial
proofs using the
Manhattan block
walking representation
of binomial coefficients
How many shortest routes from A to B?
A
B
10
5
Manhattan
jth street
4
There are
3
2
1
0 0
1
2
kth avenue
3
4
j+k shortest routes from (0,0) to (j,k)
k
Manhattan
Level n
4
There are
3
2
1
0
0
1
2
kth avenue
3
4
n shortest routes from (0,0) to (n-k,k)
k
Manhattan
Level n
4
3
There are
2
1
n
k
0
0
1
2
kth avenue
3
4
shortest routes from (0,0) to
level n and kth avenue
Level n
4
3
2
1
0
0
1
1
1
2
1
1
2
1
kth avenue
3
4
1
3
1
3
4
4
1
1
6
1
1 5 10 10 5
6 15 20 15 6
Level n
4
3
2
1
0
1
1
1
3
1
2
kth avenue
1
3
1
1
4
1
6
1
1 5 10 + 10 5
6 15 20 15 6
1
n
k
4
n-1
n-1
=
+
k-1
k
Level n
4
3
2
1
0
0
n

k=0
n
k
2
1
2
kth avenue
3
2n
=
n
4
Level n
4
3
2
1
0
0
n

i=k
i
k
1
2
kth avenue
3
n+1
=
k+1
4
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:
< * , * , * , * , *, *, … >
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,…>
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,…>
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, … >
Vector Programs
Example:
Store:
V! := <1>;
V! = <1,0,0,0,…>
V! = <1,1,0,0,…>
V! = <1,2,1,0,…>
V!= <1,3,3,1,…>
Loop n times
V! := V! + RIGHT(V!);
V! = nth row of Pascal’s triangle
1
X +
2
X +
Vector programs can
be implemented by
polynomials!
3
X
Programs  Polynomials
The vector V! = < a0, a1, a2, . . . > will be
represented by the polynomial:

PV =

i=0
aiXi
Formal Power Series
The vector V! = < a0, a1, a2, . . . > will be
represented by the formal power series:

PV =

i=0
aiXi
V! = < a0, a1, a2, . . . >

PV =

aiXi
i=0
<0> is represented by
0
<k> is represented by
k
V! + T! is represented by
RIGHT(V!) is represented by
(PV + PT)
(PV X)
Vector Programs
Example:
V! := <1>;
PV := 1;
Loop n times
V! := V! + RIGHT(V!);
PV := PV + PV X;
V! = nth row of Pascal’s triangle
Vector Programs
Example:
V! := <1>;
PV := 1;
Loop n times
V! := V! + RIGHT(V!);
PV := PV(1+X);
V! = nth row of Pascal’s triangle
Vector Programs
Example:
V! := <1>;
Loop n times
V! := V! + RIGHT(V!);
PV = (1+ X)n
V! = nth row of Pascal’s triangle
• Polynomials count
• Binomial formula
• Combinatorial proofs of
binomial identities
• Vector programs
Here’s What
You Need to
Know…