1. CSE 504 Discrete Mathematics & Foundations of Computer Science
Dr. Djamel Bouchaffra
The Integers and Division
Ch. 2 (Part 1) Section 2.4

2. Example: Let n and d be positive integers
Example: Let n and d be positive integers. How many positive integers not exceeding n are divisible by d? Solution: They are of the form {dk}, where k is a positive integer.
0 < dk  n  0 < k  n/d
 There are n/d positive integers not exceeding n that are divisible by d.

3. The Division Algorithm
Let a be an integer and d a positive integer. Then !(q, r)  Z2; 0  r < d: a = dq +r.

4. Definition :
d is called the divisor, a the dividend, q the quotient and r the remainder.
q = a div d, r = a mod d.
Example: =
Quotient
= 101 div 11
Remainder
= 2 = 101 mod 11

5. Greatest Common Divisors & Least Common Multiples
Definition:
Let a and b be integers, not both zero. The largest integer d such that d|a and d|b is called the greatest common divisor of a and b. It is denoted gcd (a, b).
Example: gcd (24, 36)
Div (24) = {1,2,3,4,6,8,12,24}
Div (36) = {1,2,3,4,6,8,9,12,18,36}
Com(24,36) = = {1,2,3,4,6,12}
gcd(24,36) = 12

6. Definition:
The integers a and b are relatively prime (rp) if gcd(a, b) =1.
Example: 17 and 22 are rp since gcd(17,22) = 1.

7. Definition:
The least common multiple (lcm) of the positive integers a and b is the smallest positive integer that is divisible by both a and b.
where max(x,y) denotes the maximum of x and y.
Example : What is the least common multiple of:
and 2433?
Solution: lcm( ,2433) = 2 max(3,4). 3max(5,3). 7max(2,0) =

8. Theorem:
Let a and b be positive integers. Then ab = gcd(a,b).lcm(a.b).

9. Modular Arithmetic Definition:
Let (a, b)  Z2,, m  Z+ then a is a congruent to b modulo m if m divides a –b.
Notation: a  b (mod m).
Theorem:
Let a and b be integers, and let m be a positive integer. Then a  b (mod m) if and only if a mod m = b mod m.

10. Theorem: 24  14 (mod 6)? Since: 6|(17 – 5) = 12  17  5 (mod 6)
Example:  5 (mod 6)
24  14 (mod 6)?
Since: 6|(17 – 5) = 12  17  5 (mod 6)
6 does not divide  24 is not congruent to 14 (mod 6)
Theorem:
Let m be a positive integer. The integers a and b are congruent modulo m if and only if
k  Z; a = b + km

11. Applications of Congruences
Hashing Functions
Pseudorandom Numbers
Cryptology (Caesar Cepher)

12. Assignment of memory location to a student record h(k) = k mod m
Hashing Functions
Assignment of memory location to a student record
h(k) = k mod m
Example:
h ( ) = mod 111 = 14 when m = 111
# of available
memory location
Key: social security #

13. Needed for computer simulation
Pseudorandom Numbers
Needed for computer simulation
Linear congruential method : xn+1 = (axn + c) mod m
Put them between 0 and 1 as: yn = xn/m

14. Mathematical expression: f(p) = (p + 3) mod 26 0  p  25
Cryptology (Caesar Cepher)
Encryption:
Making messages secrets by shifting each letter three letters forward in the alphabet
B  E X  A
Mathematical expression:
f(p) = (p + 3) mod  p  25

15. Example: What is the secret message produced from the message “Meet you in the park”
Solution:
Replace letters with numbers:
meet =
you =
in = 8 13
the =
park =
Replace each of these numbers p by f(p) = (p + 3) mod 26 meet = you = in = the = park =
Translate back into letters: “PHHW BRX LQ WKH SDUN”

16. Decryption (Deciphering)
f(p) = (p + k) mod 26 (shift cepher)  f -1(p) = (p – k) mod 26
Caesar’s method and shift cipher are very vulnerable and thus have low level of security (reason frequency of occurrence of letters in the message)  Replace letters with blocks of letters.

17. CSE 504 Discrete Mathematics & Foundations of Computer Science
Dr. Djamel Bouchaffra
Matrices
Ch. 2 (Part 4) Section 2.7

18. Definition:
A matrix is a rectangular array of numbers. A matrix with m rows and n columns is called an m x n matrix. A matrix with the same number of rows as columns is called square. Two matrices are equal if they have the same number of rows and the same number of columns and the corresponding entries in every position are equal.
Example: The matrix is a 3 X 2 matrix.

19. Definition:
Let
The ith row of A is the 1 x n matrix [ai1, ai2, …, ain]. The jth column of A is the n x 1 matrix
The (i, j)th element or entry of A is the element aij, that is, the number in the ith row and jth column of A. A convenient shorthand notation for expressing the matrix A is to write A = [aij], which indicates that A is the matrix with its (i, j)th element equal to aij.

20. Matrix Arithmetic Definition
Let A = [aij] and B = [bij] be m x n matrices. The sum of A and B, denoted by A + B, is the m x n matrix that has aij + bij as its (i, j)th element. In other words, A + B = [aij + bij].
Example:

21. Cij = ai1b1j + ai2b2j + … + aikbkj.
Definition:
Let A be an m x k matrix and B be a k x n matrix. The product of A and B, denoted by AB, is the m x n matrix with its (i, j)th entry equal to the sum of the products of the corresponding elements from the ith row of A and the jth column of B. In other words, if AB = [cij], then
Cij = ai1b1j + ai2b2j + … + aikbkj.

22. Example: Let Find AB if it is defined.
Solution: Since A is a 4 x 3 matrix and B is a 3 x 2 matrix, the product AB is defined and is a 4 x 2 matrix. To find the elements of AB, the corresponding elements of the rows of A and the columns of B are first multiplied and then these products are added. For instance, the element in the (3, 1)th position of AB is the sum of the products of the corresponding elements of the third row of A and the first column of B; namely 3 * * * 3 = 7. When all the elements of AB are computed, we see that
Matrix multiplication is not commutative.

23. Example: Let Does AB = BA? Solution: We find that
Hence, AB  BA.

24. Matrix chain multiplication
Problem: How should the matrix-chain A1A2…An be computed using the fewest multiplication of integers, where A1A2…An are m1 x m2, m2 x m3, …, mn x m n+1 matrices respectively and each has integers as entries?

25. Solution:  2 possibilities to compute A1A2A3
Example: A1 = 30 x 20 (30 rows and 20 columns) A2 = 20 x A3 = 40 x 10
Solution:  2 possibilities to compute A1A2A3
A1 (A2A3)
(A1A2)A3
1) First A2A3 requires 20 * 40 * 10 = 8000 multiplications A1(A2A3) requires 30 * 20 * 10 = 6000 multiplications Total: multiplications.
2) First A1A2 requires 30 * 20 * 40 = multiplications (A1A2)A3 requires 30 * 40 * 10 = Total: multiplications.
 (1) is more efficient!

26. Transposes and power matrices
Definition
The identity matrix of order n is the n x n matrix In = [ij], where ij = 1 if i = j and ij = 0 if i  j. Hence

27. Definition Let A = [aij] be an m x n matrix
Definition Let A = [aij] be an m x n matrix. The transpose of A, denoted At, is the n x m matrix obtained by interchanging the rows and the columns of A. In other words, if At = [bij], then bij = aij for i = 1, 2, …, n and j = 1, 2, …, m.
Example: The transpose of the matrix is

28. Definition
A square matrix A is called symmetric if A = At. Thus A = [aij] is symmetric if aij = aji for all i and j with 1  i  n and 1  j  n.
Example: The matrix is symmetric.

29. Zero-one matrices
It is a matrix with entries that are 0 or 1. They represent discrete structures using Boolean arithmetic.
We define the following Boolean operations:

30. Definition
Let A = [aij] and B = [bij] be m x n zero-one matrices. Then the join of A and B is the zero-one matrix with (i, j)th entry aij  bij. The join of A and B is denoted A  B. The meet of A and B is the zero-one matrix with (i, j)th entry aij  bij. The meet of A and B is denoted by A  B.

31. Example: Find the join and meet of the zero-one matrices Solution: We find that the joint of A and B is:
The meet of A and B is:

32. cij = (ai1  b1j)  (ai2  b2j)  …  (aik  bkj).
Definition:
Let A = [aij] be an m x k zero-one matrix and B = [bij] be a
k x n zero-one matrix. Then the Boolean product of A and B, denoted by A  B, is the m x n matrix with (i, j)th entry [cij] where
cij = (ai1  b1j)  (ai2  b2j)  …  (aik  bkj).

33. Example: Find the Boolean product of A and B, where
Solution:

34. Algorithm The Boolean Product procedure Boolean product (A,B: zero-one matrices) for i := 1 to m for j := 1 to n begin cij := 0 for q := 1 to k cij := cij (aiq  bqj) end {C = [cij] is the Boolean product of A and B}

35. Definition
Let A be a square zero-one matrix and let r be a positive integer. The rth Boolean power of A is the Boolean product of r factors of A. The rth Boolean product of A is denoted by A[r]. Hence
(This is well defined since the Boolean product of matrices is associative.) We also define A[0] to be In.

36. Example: Let . Find A[n] for all positive integers n.
Solution: We find that
We also find that
Additional computation shows that
The reader can now see that A[n] = A[5] for all positive integers n with n  5.