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Relations, Functions, and Matrices

Mathematical
Structures for
Computer Science
Chapter 4

Section 4.6 © 2006 W.H. Freeman & Co.
Copyright
MSCS �ݺ�ߣs
Matrices Relations, Functions and Matrices
Monday, March 29, 2010

Matrix

● Data about many kinds of problems can often be
represented using a rectangular arrangement of values;
such an arrangement is called a matrix.

● A is a matrix with two rows and three columns.
● The dimensions of the matrix are the number of rows
and columns; here A is a 2 × 3 matrix.
● Elements of a matrix A are denoted by aij, where i is
the row number of the element in the matrix and j is
the column number.
● In the example matrix A, a23 = 8 because 8 is the
element in row 2, column 3, of A.
Section 4.6 Matrices 2

Example: Matrix

● The constraints of many problems are represented by
the system of linear equations, e.g.:

x + y = 70

24x + 14y = 1180

The solution is x = 20, y = 50 (you can easily check
that this is a solution).

● The matrix A is the matrix of coefﬁcients for this
system of linear equations.


Matrix
● If X = Y, then x = 3, y = 6, z = 2, and w = 0.

● We will often be interested in square matrices, in which the
number of rows equals the number of columns.
● If A is an n × n square matrix, then the elements a11, a22, ... ,
ann form the main diagonal of the matrix.
● If the corresponding elements match when we think of
folding the matrix along the main diagonal, then the matrix
is symmetric about the main diagonal.
● In a symmetric matrix, aij = aji.

Matrix Operations

● Scalar multiplication calls for multiplying each entry
of a matrix by a ﬁxed single number called a scalar.
The result is a matrix with the same dimensions as the
original matrix.
● The result of multiplying matrix A:

by the scalar r = 3 is:


Matrix Operations

● Addition of two matrices A and B is deﬁned only
when A and B have the same dimensions; then it is
simply a matter of adding the corresponding elements.
● Formally, if A and B are both n × m matrices, then C
= A + B is an n × m matrix with entries cij = aij + bij:


Matrix Operations
● Subtraction of matrices is deﬁned by

A − B = A + (− l)B
● In a zero matrix, all entries are 0. If we add an n × m zero
matrix, denoted by 0, to any n × m matrix A, the result is matrix
A. We can symbolize this by the matrix equation:

0+A=A
● If A and B are n × m matrices and r and s are scalars, the
following matrix equations are true:

A+B=B+A

(A + B) + C = A + (B + C)

r(A + B) = rA + rB

(r + s)A = rA + sA

r(sA) = (rs)A
! ! ! ! rA = Ar

Matrix Operations

● Matrix multiplication is computed as A times B and
denoted as A ⋅ B.
● Condition required for matrix multiplication: the
number of columns in A must equal the number of
rows in B. Thus we can compute A ⋅ B if A is an n × m
matrix and B is an m × p matrix. The result is an n × p
matrix.
● An entry in row i, column j of A ⋅ B is obtained by
multiplying elements in row i of A by the
corresponding elements in column j of B and adding
the results. Formally, A ⋅ B = C, where


Example: Matrix Multiplication

● To ﬁnd A ⋅ B = C for the following matrices:

● Similarly, doing the same for the other row, C is:


Matrix Multiplication

● Compute A ⋅ B and B ⋅ A for the following matrices:

● Note that even if A and B have dimensions so that
both A ⋅ B and B ⋅ A are deﬁned, A ⋅ B need not equal
B ⋅ A.


Matrix Multiplication

● Where A, B, and C are matrices of appropriate
dimensions and r and s are scalars, the following
matrix equations are true (the notation A (B ⋅ C) is
shorthand for A ⋅ (B ⋅ C)):

A (B ⋅ C) = (A ⋅ B) C

A (B + C) = A ⋅ B + A ⋅ C

(A + B) C = A ⋅ C + B ⋅ C

rA ⋅ sB = (rs)(A ⋅ B)
● The n × n matrix with 1s along the main diagonal and
0s elsewhere is called the identity matrix, denoted by
I. If we multiply I times any nn matrix A, we get A as
the result. The equation is:

I⋅A=A⋅I=A

Matrix Multiplication Algorithm
ALGORITHM MatrixMultiplication
//computes n × p matrix A ⋅ B for n × m matrix A, m × p matrix B
//stores result in C
for i = 1 to n do

for j = 1 to p do

C[i, j] = 0

for k =1 to m do

C[i, j] = C[i, j] + A[i, k] * B[k, j]

end for

end for
end for
write out product matrix C
● If A and B are both n × n matrices, then there are Θ(n3)

multiplications and Θ(n3) additions required. Overall complexity
is Θ(n3)

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