A matrix is a rectangular array of numbers enclosed in brackets. In a notational sense, what differentiates a list of numbers from a matrix is its format. The numbers are listed so that each number has a certain, specific position between the brackets. Each number, or value, in a matrix is called an entry .
One of the main benefits of matrices is the properties which allow them to be manipulated and used for many different, but useful purposes.
Matrices can vary in size. This variation in size is called dimensions . Just like the dimensions of a room (width x length) matrices have dimensions (number of rows x number of columns). Thus, a 2 x 3 (read 2 by 3) matrix will have 2 rows and 3 columns.
Example of a 2 x 3 matrix:
M
=
[
572
98
302
1
732
22
]
{\displaystyle M={\begin{bmatrix}572&98&302\\1&732&22\\\end{bmatrix}}}
Another term associated with matrices is address . Like your home address, an address describes where each value, or entry, of a matrix lives. The address is composed of the lowercase letter of the matrix with the row and column number (in that order) as subscripts.
Using the 2 x 3 matrix M as an example, the positions of the values are as follows:
M
=
[
m
11
m
12
m
13
m
21
m
22
m
23
]
=
[
572
98
302
1
732
22
]
{\displaystyle M={\begin{bmatrix}m_{11}&m_{12}&m_{13}\\m_{21}&m_{22}&m_{23}\end{bmatrix}}={\begin{bmatrix}572&98&302&\\1&732&22\end{bmatrix}}}
m
11
=
572
m
12
=
98
m
13
=
302
m
21
=
1
m
22
=
732
m
23
=
22
{\displaystyle m_{11}=572\quad m_{12}=98\quad m_{13}=302\quad m_{21}=1\quad m_{22}=732\quad m_{23}=22}
A square matrix is any matrix that has the same number of rows as it does columns.
Example: 2 x 2 or 3 x 3 matrices are both square matrices.
2
x
2
=
[
1
12
21
2
]
3
x
3
=
[
7
9
30
1
2
2
5
43
6
]
{\displaystyle 2x2={\begin{bmatrix}{\color {red}1}&12\\21&{\color {red}2}\end{bmatrix}}\qquad 3x3={\begin{bmatrix}{\color {red}7}&9&30\\1&{\color {red}2}&2\\5&43&{\color {red}6}\end{bmatrix}}}
Take note of the numbers in red above in the 2 x 2 and 3 x 3 square matrices. These numbers are in the addresses of the main diagonal. The main diagonal of a square matrix is the diagonal from the upper left corner entry to the bottom right corner entry. Notice that only square matrices can have a main diagonal.
Adding and Subtracting Matrices
edit
To add or subtract matrices, the sum or difference is found when addition or subtraction is applied to corresponding entries.
For example,
[
7
4
]
+
[
3
9
]
=
[
10
13
]
{\displaystyle {\begin{bmatrix}{\color {red}7}&{\color {blue}4}\end{bmatrix}}+{\begin{bmatrix}{\color {red}3}&{\color {blue}9}\end{bmatrix}}={\begin{bmatrix}{\color {red}10}&{\color {blue}13}\end{bmatrix}}}
[
a
11
a
12
]
+
[
b
11
b
12
]
=
[
a
11
+
b
11
a
12
+
b
12
]
{\displaystyle {\begin{bmatrix}{\color {red}a_{11}}&{\color {blue}a_{12}}\end{bmatrix}}+{\begin{bmatrix}{\color {red}b_{11}}&{\color {blue}b_{12}}\end{bmatrix}}={\begin{bmatrix}{\color {red}a_{11}+b_{11}}&{\color {blue}a_{12}+b_{12}}\end{bmatrix}}}
[
7
4
]
−
[
3
9
]
=
[
4
−
5
]
{\displaystyle {\begin{bmatrix}{\color {red}7}&{\color {blue}4}\end{bmatrix}}-{\begin{bmatrix}{\color {red}3}&{\color {blue}9}\end{bmatrix}}={\begin{bmatrix}{\color {red}4}&{\color {blue}-5}\end{bmatrix}}}
[
a
11
a
12
]
−
[
b
11
b
12
]
=
[
a
11
−
b
11
a
12
−
b
12
]
{\displaystyle {\begin{bmatrix}{\color {red}a_{11}}&{\color {blue}a_{12}}\end{bmatrix}}-{\begin{bmatrix}{\color {red}b_{11}}&{\color {blue}b_{12}}\end{bmatrix}}={\begin{bmatrix}{\color {red}a_{11}-b_{11}}&{\color {blue}a_{12}-b_{12}}\end{bmatrix}}}
Since addition or subtraction takes place using corresponding entries, matrices must have the same dimensions in order to complete either operation.
Consider this operation
[
52
36
]
+
[
12
16
5
]
====>
[
a
11
a
21
]
+
[
b
11
b
12
b
13
]
C
A
N
′
T
B
E
D
O
N
E
{\displaystyle {\begin{bmatrix}52\\36\\\end{bmatrix}}+{\begin{bmatrix}12&16&5\\\end{bmatrix}}====>{\begin{bmatrix}a_{11}\\a_{21}\\\end{bmatrix}}+{\begin{bmatrix}b_{11}&b_{12}&b_{13}\\\end{bmatrix}}\quad {\color {red}CAN'T\quad BE\quad DONE}}
[
a
11
+
b
11
?
+
b
12
?
+
b
13
a
21
+
?
−
−
−
−
−
−
]
{\displaystyle {\begin{bmatrix}a_{11}+b_{11}&?+b_{12}&?+b_{13}\\a_{21}+?&---&---\\\end{bmatrix}}}
Now consider these matrices
[
52
4
36
18
]
+
[
12
16
34
2
]
====>
[
a
11
a
12
a
21
a
22
]
+
[
b
11
b
12
b
21
b
22
]
=
[
a
11
+
b
11
a
12
+
b
12
a
21
+
b
21
a
22
+
b
22
]
=
[
64
20
70
20
]
{\displaystyle {\begin{bmatrix}52&4\\36&18\\\end{bmatrix}}+{\begin{bmatrix}12&16\\34&2\\\end{bmatrix}}====>{\begin{bmatrix}a_{11}&a_{12}\\a_{21}&a_{22}\\\end{bmatrix}}+{\begin{bmatrix}b_{11}&b_{12}\\b_{21}&b_{22}\end{bmatrix}}={\begin{bmatrix}a_{11}+b_{11}&a_{12}+b_{12}\\a_{21}+b_{21}&a_{22}+b_{22}\\\end{bmatrix}}={\begin{bmatrix}64&20\\70&20\\\end{bmatrix}}}
Properties of Equality for Matrices
edit
Commutative Property
edit
Matrix addition is commutative
A
+
B
=
B
+
A
{\displaystyle A+B=B+A}
[
5
9
0
1
3
2
]
+
[
7
8
3
4
7
6
]
=
[
7
8
3
4
7
6
]
+
[
5
9
0
1
3
2
]
{\displaystyle {\begin{bmatrix}5&9&0\\1&3&2\\\end{bmatrix}}+{\begin{bmatrix}7&8&3\\4&7&6\\\end{bmatrix}}={\begin{bmatrix}7&8&3\\4&7&6\\\end{bmatrix}}+{\begin{bmatrix}5&9&0\\1&3&2\\\end{bmatrix}}}
>______________
[
12
17
3
5
10
8
]
=
[
12
17
3
5
10
8
]
{\displaystyle {\begin{bmatrix}12&17&3\\5&10&8\\\end{bmatrix}}={\begin{bmatrix}12&17&3\\5&10&8\\\end{bmatrix}}}
Associative Property
edit
Matrix addition is associative.
A
+
B
+
C
=
(
A
+
B
)
+
C
=
A
+
(
B
+
C
)
{\displaystyle A+B+C=(A+B)+C=A+(B+C)}
[
4
7
1
8
]
+
[
3
18
24
7
]
+
[
1
8
14
6
]
=
(
[
4
7
1
8
]
+
[
3
18
24
7
]
)
+
[
1
8
14
6
]
=
[
4
7
1
8
]
+
(
[
3
18
24
7
]
+
[
1
8
14
6
]
)
{\displaystyle {\begin{bmatrix}4&7\\1&8\\\end{bmatrix}}+{\begin{bmatrix}3&18\\24&7\\\end{bmatrix}}+{\begin{bmatrix}1&8\\14&6\\\end{bmatrix}}={\Bigg (}{\begin{bmatrix}4&7\\1&8\\\end{bmatrix}}+{\begin{bmatrix}3&18\\24&7\\\end{bmatrix}}{\Bigg )}+{\begin{bmatrix}1&8\\14&6\\\end{bmatrix}}={\begin{bmatrix}4&7\\1&8\\\end{bmatrix}}+{\Bigg (}{\begin{bmatrix}3&18\\24&7\\\end{bmatrix}}+{\begin{bmatrix}1&8\\14&6\\\end{bmatrix}}{\Bigg )}}
[
4
7
1
8
]
+
[
3
18
24
7
]
+
[
1
8
14
6
]
=
(
[
7
25
25
15
]
)
+
[
1
8
14
6
]
=
[
4
7
1
8
]
+
(
[
4
26
38
13
]
)
{\displaystyle {\begin{bmatrix}4&7\\1&8\\\end{bmatrix}}+{\begin{bmatrix}3&18\\24&7\\\end{bmatrix}}+{\begin{bmatrix}1&8\\14&6\\\end{bmatrix}}={\Bigg (}{\begin{bmatrix}7&25\\25&15\\\end{bmatrix}}{\Bigg )}+{\begin{bmatrix}1&8\\14&6\\\end{bmatrix}}\qquad \quad ={\begin{bmatrix}4&7\\1&8\\\end{bmatrix}}+{\Bigg (}{\begin{bmatrix}4&26\\38&13\\\end{bmatrix}}{\Bigg )}}
>_________
[
8
33
39
21
]
=
[
8
33
39
21
]
=
[
8
33
39
21
]
{\displaystyle {\begin{bmatrix}8&33\\39&21\\\end{bmatrix}}\qquad \quad \quad =\qquad \qquad {\begin{bmatrix}8&33\\39&21\\\end{bmatrix}}\qquad \qquad \quad ={\begin{bmatrix}8&33\\39&21\\\end{bmatrix}}}
The zero matrix is the additive identity matrix 'O'.
A Zero matrix is a matrix in which all of the entries are zero.
O
=
[
0
0
0
0
]
{\displaystyle O={\begin{bmatrix}0&0\\0&0\\\end{bmatrix}}}
Any matrix added to the matrix 'O' will retain its' same values.
Example of additive identity
A
+
O
=
A
{\displaystyle A+O=A}
[
7
4
6
9
]
+
[
0
0
0
0
]
=
[
7
4
6
9
]
{\displaystyle {\begin{bmatrix}7&4\\6&9\\\end{bmatrix}}+{\begin{bmatrix}0&0\\0&0\\\end{bmatrix}}={\begin{bmatrix}7&4\\6&9\\\end{bmatrix}}}
It takes two matrices to form a pair of inverses. Two matrices are additive inverses if their sum is the zero matrix. This occurs when the additive inverse of a matrix contains the values opposite of each entry.
A
+
(
−
A
)
=
O
{\displaystyle A+(-A)=O}
[
8
2
−
1
−
6
9
−
3
]
+
[
−
8
−
2
1
6
−
9
3
]
=
[
8
+
−
8
2
+
−
2
−
1
+
1
−
6
+
6
9
+
−
9
−
3
+
3
]
=
[
0
0
0
0
0
0
]
{\displaystyle {\begin{bmatrix}{\color {red}8}&{\color {red}2}&{\color {red}-1}\\{\color {red}-6}&{\color {red}9}&{\color {red}-3}\\\end{bmatrix}}+{\begin{bmatrix}{\color {blue}-8}&{\color {blue}-2}&{\color {blue}1}\\{\color {blue}6}&{\color {blue}-9}&{\color {blue}3}\\\end{bmatrix}}={\begin{bmatrix}{\color {red}8}+{\color {blue}-8}&{\color {red}2}+{\color {blue}-2}&{\color {red}-1}+{\color {blue}1}\\{\color {red}-6}+{\color {blue}6}&{\color {red}9}+{\color {blue}-9}&{\color {red}-3}+{\color {blue}3}\\\end{bmatrix}}={\begin{bmatrix}0&0&0\\0&0&0\\\end{bmatrix}}}
Multiplicative Identity Matrix
edit
A multiplicative identity matrix , usually denoted by the letter I, is any square matrix that has a value of 1 in all the entries along the main diagonal and 0 in the remaining entries.
I
2
x
2
=
[
1
0
0
1
]
I
3
x
3
=
[
1
0
0
0
1
0
0
0
1
]
{\displaystyle I_{2x2}={\begin{bmatrix}1&0\\0&1\\\end{bmatrix}}\qquad I_{3x3}={\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\\\end{bmatrix}}}
Any matrix multiplied by an identity matrix will retain it's original entries.
A
x
I
=
A
{\displaystyle AxI=A}
A
=
[
6
15
9
23
5
43
]
I
=
[
1
0
0
0
1
0
0
0
1
]
{\displaystyle A={\begin{bmatrix}6&15&9\\23&5&43\\\end{bmatrix}}\quad \quad I={\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\\\end{bmatrix}}}
{\displaystyle }
See Multiplying Matrices
A
I
=
[
(
6
)
(
1
)
+
(
15
)
(
0
)
+
(
9
)
(
0
)
(
6
)
(
0
)
+
(
15
)
(
1
)
+
(
9
)
(
0
)
(
6
)
(
0
)
+
(
15
)
(
0
)
+
(
9
)
(
1
)
(
23
)
(
1
)
+
(
5
)
(
0
)
+
(
43
)
(
0
)
(
23
)
(
0
)
+
(
5
)
(
1
)
+
(
43
)
(
0
)
(
23
)
(
0
)
+
(
5
)
(
0
)
+
(
43
)
(
1
)
]
{\displaystyle AI={\begin{bmatrix}(6)(1)+(15)(0)+(9)(0)&(6)(0)+(15)(1)+(9)(0)&(6)(0)+(15)(0)+(9)(1)\\(23)(1)+(5)(0)+(43)(0)&(23)(0)+(5)(1)+(43)(0)&(23)(0)+(5)(0)+(43)(1)\\\end{bmatrix}}}
A
I
=
[
6
15
9
23
5
43
]
{\displaystyle AI={\begin{bmatrix}6&15&9\\23&5&43\\\end{bmatrix}}}
Multiplicative Inverse Matrix
edit
If matrices
A
x
B
=
I
{\displaystyle AxB=I}
where I is the identity matrix, then A and B are multiplicative inverses of one another.
A
=
[
−
1
0
2
4
1
−
1
2
0
1
]
B
=
[
−
0.2
0
0.4
1.2
1
−
1.4
0.4
0
0.2
]
{\displaystyle A={\begin{bmatrix}-1&0&2\\4&1&-1\\2&0&1\\\end{bmatrix}}\quad \quad B={\begin{bmatrix}-0.2&0&0.4\\1.2&1&-1.4\\0.4&0&0.2\\\end{bmatrix}}}
A
B
=
[
(
−
1
)
(
−
0.2
)
+
(
0
)
(
1.2
)
+
(
2
)
(
0.4
)
(
−
1
)
(
0
)
+
(
0
)
(
1
)
+
(
2
)
(
0
)
(
−
1
)
(
0.4
)
+
(
0
)
(
−
1.4
)
+
(
2
)
(
0.2
)
(
4
)
(
−
0.2
)
+
(
1
)
(
1.2
)
+
(
−
1
)
(
0.4
)
(
4
)
(
0
)
+
(
1
)
(
1
)
+
(
−
1
)
(
0
)
(
4
)
(
0.4
)
+
(
1
)
(
−
1.4
)
+
(
−
1
)
(
0.2
)
(
2
)
(
−
0.2
)
+
(
0
)
(
1.2
)
+
(
1
)
(
0.4
)
(
2
)
(
0
)
+
(
0
)
(
1
)
+
(
1
)
(
0
)
(
2
)
(
0.4
)
+
(
0
)
(
−
1.4
)
+
(
1
)
(
0.2
)
]
{\displaystyle AB={\begin{bmatrix}(-1)(-0.2)+(0)(1.2)+(2)(0.4)&(-1)(0)+(0)(1)+(2)(0)&(-1)(0.4)+(0)(-1.4)+(2)(0.2)\\(4)(-0.2)+(1)(1.2)+(-1)(0.4)&(4)(0)+(1)(1)+(-1)(0)&(4)(0.4)+(1)(-1.4)+(-1)(0.2)\\(2)(-0.2)+(0)(1.2)+(1)(0.4)&(2)(0)+(0)(1)+(1)(0)&(2)(0.4)+(0)(-1.4)+(1)(0.2)\\\end{bmatrix}}}
A
B
=
I
=
[
1
0
0
0
1
0
0
0
1
]
{\displaystyle AB=I={\begin{bmatrix}1&0&0\\0&1&0\\0&0&1\\\end{bmatrix}}}
Thus, matrices A and B are multiplicative inverses of each other.
Multiplying Matrices
edit
Back to Multiplicative Identity Matrix
Another useful property of matrices is called a scalar . A scalar is a number located outside of a single matrix. To apply the scalar to the matrix, simply multiply each entry of the matrix by the scalar .
For example,
3
[
3
9
15
1
7.5
2
]
=
[
(
3
)
3
(
3
)
9
(
3
)
15
(
3
)
1
(
3
)
7.5
(
3
)
2
]
=
[
9
27
45
3
22.5
6
]
{\displaystyle {\color {red}3}{\begin{bmatrix}3&9&15\\1&7.5&2\\\end{bmatrix}}={\begin{bmatrix}({\color {red}3})3&({\color {red}3})9&({\color {red}3})15\\({\color {red}3})1&({\color {red}3})7.5&({\color {red}3})2\\\end{bmatrix}}={\begin{bmatrix}9&27&45\\3&22.5&6\\\end{bmatrix}}}
In order to multiply two matrices together it is necessary to pay attention to their dimensions. Matrices A and J can be multiplied only if the number of columns in A equals the number of rows in J. Also, another hint is that the product of an a x j and a j x b matrix is an a x b matrix. Notice that the number of columns from the first matrix must equal the number of rows from the second matrix (j = j).
First, we'll look at how to test to see if we can multiply matrices.
Consider matrices Q and R.
Q
=
2
x
3
R
=
3
x
2
3
c
o
l
u
m
n
s
=
3
r
o
w
s
{\displaystyle Q=2\quad x\quad {\color {red}3}\qquad \quad \qquad R={\color {red}3}\quad x\quad 2\qquad {\color {red}3\ columns}={\color {red}3\ rows}}
Since the number of columns of matrix Q equals the rows of matrix R, these matrices can be multiplied. This will produce a 2 x 2 matrix.
Q
=
[
1
7
9
3
5
1
]
x
R
=
[
8
9
3
5
4
6
]
{\displaystyle Q={\begin{bmatrix}{\color {red}1}&{\color {red}7}&{\color {red}9}\\3&5&1\\\end{bmatrix}}\qquad x\qquad R={\begin{bmatrix}{\color {red}8}&9\\{\color {red}3}&5\\{\color {red}4}&6\\\end{bmatrix}}}
Multiplying Q and R.
During this process, you may find your fingers and mental addition extremely helpful. You can use your left pointer finger to follow the entries in the rows of matrix Q and your right pointer finger to follow the columns of matrix R. To multiply matrices, add the products of consecutive entries in corresponding rows of matrix Q and columns of matrix R.
Q
R
=
[
q
11
q
12
q
13
q
21
q
22
q
23
]
[
r
11
r
12
r
21
r
22
r
31
r
32
]
=
{\displaystyle QR={\begin{bmatrix}{\color {red}q_{11}}&{\color {red}q_{12}}&{\color {red}q_{13}}\\{\color {red}q_{21}}&{\color {red}q_{22}}&{\color {red}q_{23}}\\\end{bmatrix}}{\begin{bmatrix}{\color {blue}r_{11}}&{\color {blue}r_{12}}&\\{\color {blue}r_{21}}&{\color {blue}r_{22}}\\{\color {blue}r_{31}}&{\color {blue}r_{32}}\\\end{bmatrix}}=}
[
Q
R
11
=
q
11
∙
r
11
+
q
12
∙
r
21
+
q
13
∙
r
31
Q
R
12
=
q
11
∙
r
12
+
q
12
∙
r
22
+
q
13
∙
r
32
Q
R
21
=
q
21
∙
r
11
+
q
22
∙
r
21
+
q
23
∙
r
31
Q
R
22
=
q
21
∙
r
12
+
q
22
∙
r
22
+
q
23
∙
r
32
]
{\displaystyle {\begin{bmatrix}QR_{11}={\color {red}q_{11}}\bullet {\color {blue}r_{11}}\quad +&{\color {red}q_{12}}\bullet {\color {blue}r_{21}}\quad +&{\color {red}q_{13}}\bullet {\color {blue}r_{31}}\quad QR_{12}={\color {red}q_{11}}\bullet {\color {blue}r_{12}}\quad +&{\color {red}q_{12}}\bullet {\color {blue}r_{22}}\quad +&{\color {red}q_{13}}\bullet {\color {blue}r_{32}}\\QR_{21}={\color {red}q_{21}}\bullet {\color {blue}r_{11}}\quad +&{\color {red}q_{22}}\bullet {\color {blue}r_{21}}\quad +&{\color {red}q_{23}}\bullet {\color {blue}r_{31}}\quad QR_{22}={\color {red}q_{21}}\bullet {\color {blue}r_{12}}\quad +&{\color {red}q_{22}}\bullet {\color {blue}r_{22}}\quad +&{\color {red}q_{23}}\bullet {\color {blue}r_{32}}\\\end{bmatrix}}}
Q
R
=
[
1
7
9
3
5
1
]
[
8
9
3
5
4
6
]
=
[
(
1
)
(
8
)
+
(
7
)
(
3
)
+
(
9
)
(
4
)
(
1
)
(
9
)
+
(
7
)
(
5
)
+
(
9
)
(
6
)
(
3
)
(
8
)
+
(
5
)
(
3
)
+
(
1
)
(
4
)
(
3
)
(
9
)
+
(
5
)
(
5
)
+
(
1
)
(
6
)
]
=
[
65
98
43
58
]
{\displaystyle QR={\begin{bmatrix}1&7&9\\3&5&1\\\end{bmatrix}}{\begin{bmatrix}8&9\\3&5\\4&6\\\end{bmatrix}}={\begin{bmatrix}(1)(8)+(7)(3)+(9)(4)&(1)(9)+(7)(5)+(9)(6)\\(3)(8)+(5)(3)+(1)(4)&(3)(9)+(5)(5)+(1)(6)\\\end{bmatrix}}={\begin{bmatrix}65&98\\43&58\\\end{bmatrix}}}
Every square matrix has a value called a determinant , and only square matrices have defined determinants. The determinant of a 2x2 square matrix is the difference of the products of the diagonals.
The determinant of a 2 x 2 matrix can be found as follows:
d
e
t
[
9
2
7
8
]
=
(
9
)
(
8
)
−
(
7
)
(
2
)
=
58
{\displaystyle det{\begin{bmatrix}{\color {red}9}&{\color {blue}2}\\{\color {blue}7}&{\color {red}8}\\\end{bmatrix}}=({\color {red}9})({\color {red}8})-({\color {blue}7})({\color {blue}2})=58}
The "down " diagonal is in red and the "up " diagonal is in blue. The up diagonals are always subtracted from the down diagonals.
Matrices that are larger than a 2 x 2 matrix become a little more complicated when finding the determinant but the same rules apply.
The "down " diagonal is not necessarily the same as the main diagonal mentioned earlier. The down diagonal happens to be the main diagonal for a 2 x 2 matrix but larger matrices will have multiple down diagonals and only one main diagonal.
Let's find
d
e
t
[
1
6
19
8
17
5
14
9
3
]
{\displaystyle det{\begin{bmatrix}1&6&19\\8&17&5\\14&9&3\\\end{bmatrix}}}
When finding the determinant of a 3 x 3 matrix it is helpful to write the first two columns to the right side of the matrix like so,
d
e
t
[
1
6
19
8
17
5
14
9
3
]
1
6
8
17
14
9
{\displaystyle det{\begin{bmatrix}1&6&19\\8&17&5\\14&9&3\\\end{bmatrix}}{\begin{matrix}1&6\\8&17\\14&9\end{matrix}}}
As shown above in the 2x2 matrix, the numbers are color coded. The blue numbers, once again, indicate they are used in the up diagonals, the red are used in the down diagonals, and those in magenta are used in both.
d
e
t
[
1
6
19
8
17
5
14
9
3
]
1
6
8
17
14
9
=
(
1
∗
17
∗
3
)
+
(
6
∗
5
∗
14
)
+
(
19
∗
8
∗
9
)
−
[
(
14
∗
17
∗
19
)
+
(
9
∗
5
∗
1
)
+
(
3
∗
8
∗
6
)
]
{\displaystyle det{\begin{bmatrix}{\color {red}1}&{\color {red}6}&{\color {magenta}19}\\8&{\color {magenta}17}&{\color {magenta}5}\\{\color {blue}14}&{\color {blue}9}&{\color {magenta}3}\\\end{bmatrix}}{\begin{matrix}{\color {blue}1}&{\color {blue}6}\\{\color {magenta}8}&17\\{\color {red}14}&{\color {red}9}\end{matrix}}=({\color {red}1}*{\color {red}17}*{\color {red}3})+({\color {red}6}*{\color {red}5}*{\color {red}14})+({\color {red}19}*{\color {red}8}*{\color {red}9})-{\Big [}({\color {blue}14}*{\color {blue}17}*{\color {blue}19})+({\color {blue}9}*{\color {blue}5}*{\color {blue}1})+({\color {blue}3}*{\color {blue}8}*{\color {blue}6}){\Big ]}}
=
(
51
+
420
+
1
,
368
)
−
(
4
,
522
+
45
+
144
)
=
−
2872
{\displaystyle =({\color {red}51}+{\color {red}420}+{\color {red}1,368})-({\color {blue}4,522}+{\color {blue}45}+{\color {blue}144})=-2872}
Thus, the determinant of the above 3x3 matrix is -2872.
While square matrices of any size have a determinant, there is no way to extend this diagonal method of computing that determinant for a square matrix of size 4x4 or larger.
Match the following terms with their definitions.
1. What is a matrix? A mold in which something, such as printing type or a phonograph record, is cast or shaped.
2. How can you change one format of an equation into another? If both funds are in the same fund" family" you can do an "Exchange"
3.How do you perform scalar multiplication? You just take a regular number called a "scalar" and multiply it on every entry in the matrix.
Match the names of the properties with their equation equivalent.
____Additive Identity>______________ 1.
A
+
B
+
C
=
(
A
+
B
)
+
C
=
A
+
(
B
+
C
)
{\displaystyle A+B+C=(A+B)+C=A+(B+C)}
____ Additive Inverse>______________ 2.
A
x
I
=
A
{\displaystyle AxI=A}
____ Associative Property>__________ 3.
A
+
B
=
B
+
A
{\displaystyle A+B=B+A}
____ Commutative Property>_________ 4.
A
+
O
=
A
{\displaystyle A+O=A}
____ Multiplicative Identity>__________ 5.
A
+
(
−
A
)
=
O
{\displaystyle A+(-A)=O}
____ Multiplicative Inverse>__________ 6.
A
x
B
=
I
{\displaystyle AxB=I}
Properties Solutions
Add the matrices.
1)
[
52
36
]
+
[
12
5
]
{\displaystyle {\begin{bmatrix}52\\36\\\end{bmatrix}}+{\begin{bmatrix}12\\5\\\end{bmatrix}}}
2)
[
12
7
17
6
91
21
]
+
[
0
14
32
5
28
1
]
{\displaystyle {\begin{bmatrix}12&7&17\\6&91&21\\\end{bmatrix}}+{\begin{bmatrix}0&14&32\\5&28&1\\\end{bmatrix}}}
3)
[
3
65
8
32
]
+
[
9
71
16
11
4
10
]
{\displaystyle {\begin{bmatrix}3&65\\8&32\\\end{bmatrix}}+{\begin{bmatrix}9&71&16\\11&4&10\\\end{bmatrix}}}
4)
[
4
56
14
15
21
35
]
+
[
87
13
5
12
]
{\displaystyle {\begin{bmatrix}4&56&14\\15&21&35\\\end{bmatrix}}+{\begin{bmatrix}87&13\\5&12\\\end{bmatrix}}}
5)
[
2
34
41
23
14
5
69
52
6
61
]
+
[
1
4
5
17
53
7
8
99
31
93
]
{\displaystyle {\begin{bmatrix}2&34&41&23&14\\5&69&52&6&61\\\end{bmatrix}}+{\begin{bmatrix}1&4&5&17&53\\7&8&99&31&93\\\end{bmatrix}}}
6)
[
19
32
44
]
+
[
12
89
42
]
{\displaystyle {\begin{bmatrix}19\\32\\44\\\end{bmatrix}}+{\begin{bmatrix}12\\89\\42\\\end{bmatrix}}}
Adding Matrices Solutions
Subtracting Matrices
edit
Subtract the matrices.
1)
[
52
36
]
−
[
12
5
]
{\displaystyle {\begin{bmatrix}52\\36\\\end{bmatrix}}-{\begin{bmatrix}12\\5\\\end{bmatrix}}}
2)
[
1
16
43
6
21
89
]
−
[
8
2
61
9
17
26
]
{\displaystyle {\begin{bmatrix}1&16&43\\6&21&89\\\end{bmatrix}}-{\begin{bmatrix}8&2&61\\9&17&26\\\end{bmatrix}}}
3)
[
65
14
4.5
43
]
−
[
31
13
0
24
]
{\displaystyle {\begin{bmatrix}65&14\\4.5&43\\\end{bmatrix}}-{\begin{bmatrix}31&13\\0&24\\\end{bmatrix}}}
4)
[
52
17
88
2
63
16
]
−
[
13
18
4
11
]
{\displaystyle {\begin{bmatrix}52&17&88\\2&63&16\\\end{bmatrix}}-{\begin{bmatrix}13&18\\4&11\\\end{bmatrix}}}
5)
[
3.4
7.6
5.2
9.6
1.2
8.8
]
−
[
9.8
7.6
5.1
2.3
6.9
4.5
]
{\displaystyle {\begin{bmatrix}3.4&7.6\\5.2&9.6\\1.2&8.8\end{bmatrix}}-{\begin{bmatrix}9.8&7.6\\5.1&2.3\\6.9&4.5\\\end{bmatrix}}}
Subtracting Matrices Solutions
Multiplying Matrices
edit
Multiply the matrices or by the scalar to find the product.
1)
[
4
7
2
8
3
9
]
[
0
6
9
7
2
3
]
{\displaystyle {\begin{bmatrix}4&7\\2&8\\3&9\\\end{bmatrix}}{\begin{bmatrix}0&6&9\\7&2&3\\\end{bmatrix}}}
2)
[
4
7
2
8
3
9
1
6
]
[
0
6
17
9
7
2
36
12
]
{\displaystyle {\begin{bmatrix}4&7\\2&8\\3&9\\1&6\\\end{bmatrix}}{\begin{bmatrix}0&6&17&9\\7&2&36&12\\\end{bmatrix}}}
3)
[
14
0
1
4
2
9
1
6
8
]
[
1
4
5
1
2
7
12
6
8
]
{\displaystyle {\begin{bmatrix}14&0&1\\4&2&9\\1&6&8\\\end{bmatrix}}{\begin{bmatrix}1&4&5\\1&2&7\\12&6&8\\\end{bmatrix}}}
4)
8
[
41
17
54
2
8
12
4
5
3
]
{\displaystyle 8{\begin{bmatrix}41&17&54\\2&8&12\\4&5&3\\\end{bmatrix}}}
5)
[
4
7
2
8
3
9
]
[
0
6
7
2
9
3
]
{\displaystyle {\begin{bmatrix}4&7\\2&8\\3&9\\\end{bmatrix}}{\begin{bmatrix}0&6\\7&2\\9&3\\\end{bmatrix}}}
Multiplying Matrices Solutions
Find the determinant of the following matrices.
1)
[
5
1
0
6
]
{\displaystyle {\begin{bmatrix}5&1\\0&6\\\end{bmatrix}}}
2)
[
1
12
4
9
6
3
2
2
0
]
{\displaystyle {\begin{bmatrix}1&12&4\\9&6&3\\2&2&0\\\end{bmatrix}}}
3)
[
14
4
1
1
2
7
12
6
3
]
{\displaystyle {\begin{bmatrix}14&4&1\\1&2&7\\12&6&3\\\end{bmatrix}}}
4)
[
1
4
9
3
]
{\displaystyle {\begin{bmatrix}1&4\\9&3\\\end{bmatrix}}}
5)
[
7
0
2
6
]
{\displaystyle {\begin{bmatrix}7&0\\2&6\\\end{bmatrix}}}
Determinants Solutions
Definitions Solutions
edit
Match the following terms with their definitions.
8 address>_______________ 1. Diagonal from the upper left corner entry to the bottom right corner entry
6 determinant>____________ 2. A rectangular array of numbers enclosed in brackets
3 dimensions>____________ 3. Variation in size of a matrix
1 main diagonal>__________ 4. Any matrix that has the same number of rows as it does columns
2 matrix>________________ 5. Matrix in which all of the entries are zero
7 scalar>________________ 6. The difference of the products of the diagonals
4 square matrix>__________ 7. Number located outside of a single matrix which is multiplied by each entry of the matrix
5 zero matrix>____________ 8. Describes where each value, or entry, of a matrix lives
Properties Solutions
edit
Match the names of the properties with their equation equivalent.
4 Additive Identity>______________ 1.
A
+
B
+
C
=
(
A
+
B
)
+
C
=
A
+
(
B
+
C
)
{\displaystyle A+B+C=(A+B)+C=A+(B+C)}
5 Additive Inverse>______________ 2.
A
x
I
=
A
{\displaystyle AxI=A}
1 Associative Property>__________ 3.
A
+
B
=
B
+
A
{\displaystyle A+B=B+A}
3 Commutative Property>_________ 4.
A
+
O
=
A
{\displaystyle A+O=A}
2 Multiplicative Identity>__________ 5.
A
+
(
−
A
)
=
O
{\displaystyle A+(-A)=O}
6 Multiplicative Inverse>__________ 6.
A
x
B
=
I
{\displaystyle AxB=I}
Adding Matrices Solutions
edit
Add the matrices.
1)
[
64
41
]
{\displaystyle {\begin{bmatrix}64\\41\\\end{bmatrix}}}
2)
[
12
21
49
11
119
22
]
{\displaystyle {\begin{bmatrix}12&21&49\\11&119&22\\\end{bmatrix}}}
3)
{\displaystyle \quad }
Cannot be done because the matrices do not have the same dimensions.
4)
{\displaystyle \quad }
Cannot be done because the matrices do not have the same dimensions.
5)
[
3
38
46
40
67
12
77
151
37
154
]
{\displaystyle {\begin{bmatrix}3&38&46&40&67\\12&77&151&37&154\\\end{bmatrix}}}
6)
[
31
121
86
]
{\displaystyle {\begin{bmatrix}31\\121\\86\\\end{bmatrix}}}
Subtracting Matrices Solutions
edit
Subtract the matrices.
1)
[
40
31
]
{\displaystyle {\begin{bmatrix}40\\31\\\end{bmatrix}}}
2)
[
−
7
14
−
18
−
3
4
63
]
{\displaystyle {\begin{bmatrix}-7&14&-18\\-3&4&63\\\end{bmatrix}}}
3)
[
34
1
4.5
19
]
{\displaystyle {\begin{bmatrix}34&1\\4.5&19\\\end{bmatrix}}}
4) Cannot be done because the matrices do not have the same dimensions.
5)
[
−
6.4
0
0.1
7.3
−
5.7
4.3
]
{\displaystyle {\begin{bmatrix}-6.4&0\\0.1&7.3\\-5.7&4.3\\\end{bmatrix}}}
Multiplying Matrices Solutions
edit
Multiply the matrices or by the scalar to find the product.
1)
[
49
38
57
56
28
42
63
36
54
]
{\displaystyle {\begin{bmatrix}49&38&57\\56&28&42\\63&36&54\\\end{bmatrix}}}
2)
[
49
38
320
120
56
28
322
114
63
36
375
135
42
18
233
81
]
{\displaystyle {\begin{bmatrix}49&38&320&120\\56&28&322&114\\63&36&375&135\\42&18&233&81\\\end{bmatrix}}}
3)
[
26
62
78
114
74
106
103
64
111
]
{\displaystyle {\begin{bmatrix}26&62&78\\114&74&106\\103&64&111\\\end{bmatrix}}}
4)
[
328
136
432
16
64
96
32
40
24
]
{\displaystyle {\begin{bmatrix}328&136&432\\16&64&96\\32&40&24\\\end{bmatrix}}}
5) Cannot be done because the number of columns from the first matrix does not equal the number of rows in the second matrix.
Determinants Solutions
edit
Find the determinant of the following matrices.
1) 30
2) 90
3) -198
4) -33
5) 42