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## Determinants and Matrices

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Title: Determinants and Matrices

1
Unit 5
• Determinants and Matrices

2
5.1 Definition of Determinants
• (A) A square array of real (or complex) numbers
arranged in n rows and n columns is called a
determinant of the nth order .

3
5.1 Definition of Determinants
• The number in the determinant is called an
element or entry of the determinant. There are n2
elements in a determinant of the nth order.
Usually, we use small letters to denote elements.
The symbol aij denotes the element located in
the ith row, the jth column.

e.g. a34 located in 3th row
4th column
4
5.1 Definition of Determinants
• (B) The value of a determinant of order 2 is
defined by

The expression a11a22 - a12a21 on the right hand
side of (1) is called the expansion of the second
order determinant.
5
5.1 Definition of Determinants
• (C) The value of a determinant of order 3 is
defined by

The expression on the right hand side of (2) is
called the expansion of the third order
determinant.
6
5.2 Properties of Determinants
• (1) The value of a determinant remains unchanged
if all the rows and columns are correspondingly
interchanged.

7
5.2 Properties of Determinants
• (2) The determinant changes sign only but its
absolute value remains unchanged if any two rows
(or any two columns) are interchanged.

8
5.2 Properties of Determinants
• (3) If the elements of a row (resp. a column) are
proportional to those of another row (resp.
another column), then the value of the
determinant is zero.

9
5.2 Properties of Determinants
• (4) If the elements of a row (resp. a column) are
identical to those of another row (resp. another
column), then the value of the determinant is
zero.

10
5.2 Properties of Determinants
• (5) If the elements of any row (resp. a column)
of a determinant are multiplied by the same
factor, the resulting determinant is equal to the
product of that factor and the original
determinant.

11
5.2 Properties of Determinants
• (5 ext) If the elements of a row (resp. a column)
are zero, the value of the determinant is zero.

12
5.2 Properties of Determinants
• (6) If the elements of any row (resp. a column)
are added or subtracted by equimultiples of the
corresponding elements of another row (resp.
another column), the value of the determinant is
altered.

13
5.2 Properties of Determinants
• (6) If the elements of any row (resp. a column)
are added or subtracted by equimultiples of the
corresponding elements of another row (resp.
another column), the value of the determinant is
altered.

14
5.2 Properties of Determinants
• (7) If the elements of a row (resp. a column) of
a determinant consists of an algebraic sum of the
terms, the determinant is equal to the sum of two
other determinants in each of which the elements
consist of single term.

15
5.2 Properties of Determinants
• (7) If the elements of a row (resp. a column) of
a determinant consists of an algebraic sum of the
terms, the determinant is equal to the sum of two
other determinants in each of which the elements
consist of single term.

16
P.133 Ex.5A
17
5.3 Minors and Cofactors
• Let aij be an element of the determinant A
located in the ith row, the jth column. The minor
of aij , is denoted by ?ij, is defined as the
determinant formed by deleting the ith row and
jth column of A.

18
5.3 Minors and Cofactors
• Let A be a determinant, aij be an element of A in
the ith row, the jth column and ?ij be the minor
of aij. The cofactor of aij is defined by (-1)ij
?ij and is usually denoted by Aij.

19
5.3 Minors and Cofactors
20
5.3 Minors and Cofactors
The sum of the products of the elements of any
row (or any column) and their own cofactors is
equal to the value of the determinant.
21
5.3 Minors and Cofactors
The sum of the products of the elements of any
row (or any column) and their own cofactors is
equal to the value of the determinant.
22
P.145 Ex.5B
23
5.4 Factorization of Determinants
To factorize a given determinant, the following
two methods are usually employed
(1) Apply properties of a determinant to
transform the entries in a row (or a column)
until there is a common factor among the entries.
(2) If the determinant is a polynomial in x and
if the determinant vanishes when x a, then (x
- a) is a factor.
24
5.4 Factorization of Determinants
25
5.4 Factorization of Determinants
• Method II
• remains unchanged if a, b, c are respectively
replaced by b, c, a.Thus it is a cyclic symmetric
about a, b, c and is homogeneous.
• If we put a b or b c or c a, ? 0.
• Hence ? has factors a b, b c and c a.
• As ? is of degree 3, we let ? ? k(a-b)(b-c)(c-a).
• By comparing coefficients of bc2, we get k 1.
• ? ? ? (a-b)(b-c)(c-a)

26
P.149 Ex.5C
27
5.5 Definition and Basic Operation of Matrices
Matrix A rectangular array of real (or complex)
numbers arranged in m rows and n column is called
a m x n matrix. A m x n matrix is usually
represented in the form
28
5.5 Definition and Basic Operation of Matrices
The number aij in the ith row and the jth column
of a matrix is called an element or entry. Hence
a m x n matrix contains mn elements. We call
m x n the order of a matrix, and we usually use
capital letters to denote matrices.
Matrix is a mathematical tool, it is not a number.
29
5.5 Definition and Basic Operation of Matrices
Equality of matrices Two matrices A (aij) and
B (bij) are equal if and only if they are of
the same orders and aij bij, for all i 1, 2 ,
.., m j 1, 2,..., n.
30
5.5 Definition and Basic Operation of Matrices
Sum of Matrices The sum of two m x n matrices A
(aij) and B (bij) is the m x n matrix C
(cij), where cij aij bij, for all i 1, 2,
., m j 1, 2, , n.
31
5.5 Definition and Basic Operation of Matrices
Scalar Multiplication The scalar multiplication
of a m x n matrix A (aij) by a scalar ? (here ?
is a real or a complex number) is the m x n
matrix C (cij), where
cij ? aij, for every i 1, 2, , m j 1,
2,, n. We usually write C ?A. This defines
scalar multiplication of a matrix A by a scalar ?.
32
5.5 Definition and Basic Operation of Matrices
Negative Matrix Let A be any matrix, the symbol
A denotes (-1)A.
33
5.5 Definition and Basic Operation of Matrices
Difference of Matrices Let A, B be two matrices
of the same order. The difference A - B is
defined by A - B A (-1)B.
34
5.5 Definition and Basic Operation of Matrices
For any three matrices A, B, C of the same order
and any scalars ?, ?, we have
• (A B) C A (B C)

(2) Addition is commutative A B B A
(3) Scalar multiplication is distributive over
35
5.5 Definition and Basic Operation of Matrices
Multiplication of Matrices Let A (aik) be a m x
n matrix and B (bkj) be a n x p matrix, the
product AB is the m x p matrix C (cij), where
for all i 1, 2,., m j 1, 2, , p.
The product AB defines multiplication of matrices.
36
5.5 Definition and Basic Operation of Matrices
Multiplication of Matrices
37
5.5 Definition and Basic Operation of Matrices
Multiplication of Matrices
38
5.5 Definition and Basic Operation of Matrices
Multiplication of Matrices
39
5.5 Definition and Basic Operation of Matrices
Multiplication of Matrices
Multiplication of matrices is non-commutative.
40
5.5 Definition and Basic Operation of Matrices
Multiplicative Properties of Matrices
• Let A, B, C be three matrices of order m x n, n
x p, p x q respectively, then
(AB)C A(BC)

(2) Let A, B be two matrices of order m x n and
C, D be matrices of order n x p, q x m
respectively, then
(3) Let A, B be two matrices of order m x n and
n x p respectively. For any scalar ?, we have
?(AB) (?A)B A(?B).
41
5.6 Special Types of Matrices
(1) The square Matrix A n x n matrix is called
a square matrix of order n.
(2) The identity Matrix A square matrix A
(aij) of order n is called an identity matrix or
a unit matrix of order n if and only if
and is denoted by In.
42
5.6 Special Types of Matrices
(3) The scalar Matrix A square matrix (bij) is
called a scalar matrix if and only if
43
5.6 Special Types of Matrices
44
5.6 Special Types of Matrices
(5) The Zero/ Null of Matrix A matrix (not
necessarily square) is called a zero matrix or a
null matrix if and only if all its elements are
zero. A zero matrix of order m x n is usually
denoted by 0mxn.
(i) For any matrix A and a zero matrix, both are
of the same order, A 0 0 A A
(ii) For any square matrix A and a square zero
matrix 0, both are of the same order, A x 0
0 x A 0
45
5.6 Special Types of Matrices
46
5.6 Special Types of Matrices
(7) The Idempotent Matrix A square matrix is
called idempotent if and only if A2 A.
47
5.6 Special Types of Matrices
(8) The transpose of Matrix The transpose of a
m x n matrix A (aij), denoted by AT or At or A,
is the n x m matrix AT (bij), where bij aji.
48
5.6 Special Types of Matrices
(8 ext.) Properties of the transpose of matrix
49
5.6 Special Types of Matrices
(9) The Symmetric Matrix A square matrix A is
said to be symmetric if and only if AT A.
50
5.6 Special Types of Matrices
(10) The skew-symmetric Matrix A square matrix
a is said to be skew-symmetric if and only if AT
- A
51
5.6 Special Types of Matrices
(11) The Row Vector and the Column Vector A
1 x n matrix is called a row vector.
A m x 1 matrix is called column vector.
52
P.156 Ex.5D
53
5.7 Multiplicative Inverse of a Square Matrix
The determinant of A, denoted by det A or A, is
defined as the determinant
54
5.7 Multiplicative Inverse of a Square Matrix
General properties between determinant and matrix
• For any square matrix A of order n and any scalar
?, det (?A) ?n det A

or
Why is there power n?
55
5.7 Multiplicative Inverse of a Square Matrix
Verification
56
5.7 Multiplicative Inverse of a Square Matrix
General properties between determinant and matrix
(2) For any two square matrices A and B of the
same order, det (AB) det A x det B
or
How can we verify this property?
57
5.7 Multiplicative Inverse of a Square Matrix
Verification
58
5.7 Multiplicative Inverse of a Square Matrix
Verification
By direct expansion of the determinants, the
identity holds.
59
5.7 Multiplicative Inverse of a Square Matrix
General properties between determinant and matrix
(3) For any square matrix A, det (AT) det A

or
How can we prove this property?
60
5.7 Multiplicative Inverse of a Square Matrix
61
5.7 Multiplicative Inverse of a Square Matrix
The Cofactor Matrix Let A (aij)nxn be a
square matrix. The cofactor matrix of A, denoted
by cof A, is defined by cof A (Aij)nxn, where
Aij is the cofactor of aij, for every i, j 1,
2,,n.
62
5.7 Multiplicative Inverse of a Square Matrix
The Adjoint Matrix Let A be a square matrix.
The transpose of the cofactor matrix of A, i.e.
(cof A)T, is called the adjoint matrix of A,
63
5.7 Multiplicative Inverse of a Square Matrix
The Non-singular/Invertible Matrix A square
matrix A of order n is said to be non-singular or
invertible if and only if there exists a square
matrix B such that AB
BA In, where In is an identity matrix of order
n, and the matrix B is called the multiplicative
inverse or simply the inverse of A, which is
denoted by A-1, i.e. AA-1 A-1A In.
The inverse of a non-singular matrix is unique.
64
5.7 Multiplicative Inverse of a Square Matrix
Lemma For any square matrix of order n,
identity matrix of order n.
?
65
5.7 Multiplicative Inverse of a Square Matrix
Verification
Why do the elements arrange in this way?
66
5.7 Multiplicative Inverse of a Square Matrix
How can we simplify these three elements?
Then how about the other elements?
Why does each row equal zero?
And
67
5.7 Multiplicative Inverse of a Square Matrix
68
5.7 Multiplicative Inverse of a Square Matrix
Verification
69
5.7 Multiplicative Inverse of a Square Matrix
Lemma For any square matrix of order n,
identity matrix of order n.
70
5.7 Multiplicative Inverse of a Square Matrix
71
5.7 Multiplicative Inverse of a Square Matrix
On the other hand,
A square matrix A is said to be singular or not
invertible if and only if the inverse, A-1, of A
does not exist.
A square matrix A is singular if and only if det
A 0.
72
5.8 Properties of Inverses
If A and B are non-singular matrices of the same
order, and any scalar ? we have
(1) If AC1 AC2 or C1A C2A, then C1 C2
(2) A-1 is non-singular and (A-1)-1 A
(3) AB is non-singular and (AB)-1 B-1A-1
Quite similar to (AB)T BTAT We will show the
identity (AB)-1 B-1A-1 later.
(4) For any positive integer n, An is
non-singular, and (An)-1 (A-1)n.
73
5.8 Properties of Inverses
If A and B are non-singular matrices of the same
order, and any scalar ? we have
(6) AT is non-singular and (AT)-1 (A-1)T.
(7) If AC 0, then C 0.
74
5.8 Properties of Inverses
If A and B are non-singular matrices of the same
order, and any scalar ? we have
Prove (AB)-1 B-1A-1
5E
75
P.164 Ex.5E
76
5.9 Some Illustrative Examples
77
5.9 Some Illustrative Examples
78
P.176 Ex.5F
79
Transformations of Points on the Coordinate Plane
80
5.10 Linear Transformations on the Rectangular
Cartesian Plane
81
5.10 Linear Transformations on the Rectangular
Cartesian Plane
82
5.10 Linear Transformations on the Rectangular
Cartesian Plane
83
5.10 Linear Transformations on the Rectangular
Cartesian Plane
84
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
85
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
86
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
87
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
88
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
89
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
90
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
91
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
92
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
93
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
(IV) Reflection
94
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
(IV) Reflection
The matrix representing the reflection about
95
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
96
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
97
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
98
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
99
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
100
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
101
5.11 Some special Linear Transformations on the
Rectangular Cartesian Plane
102
P.195 Ex.5G