SlideShare a Scribd company logo
Matrices & Determinants
Determinants
Dr. Pankaj Das
Mail id: Pankaj.iasri@gmail.com
Matrices & Determinants
TYPES OF MATRICES
NAME DESCRIPTION EXAMPLE
Rectangular
matrix
No. of rows is not equal to
no. of columns
Square matrix No. of rows is equal to no. of
columns
Diagonal
matrix
Non-zero element in principal
diagonal and zero in all other
positions
Scalar matrix Diagonal matrix in which all
the elements on principal
diagonal and same








5
0
2
1
2
6

2 1 3
2 0 1
1 2 4




















7
0
0
0
4
0
0
0
2










4
0
0
0
4
0
0
0
4
Matrices & Determinants
TYPES OF MATRICES
NAME DESCRIPTION EXAMPLE
Row matrix A matrix with only 1
row
Column matrix A matrix with only I
column
Identity matrix Diagonal matrix
having each
diagonal element
equal to one (I)
Zero matrix A matrix with all zero
entries

3 2 14
 

2
3












1
0
0
1
0 0
0 0






Matrices & Determinants
TYPES OF MATRICES
NAME DESCRIPTION EXAMPLE
Upper Triangular
matrix
Square matrix
having all the entries
zero below the
principal diagonal
Lower Triangular
matrix
Square matrix
having all the entries
zero above the
principal diagonal










7
0
0
6
4
0
3
5
2










7
3
6
0
4
5
0
0
2
Matrices & Determinants
Determinants
If is a square matrix of order 1,
then |A| = | a11 | = a11
ij
A = a
 
 
If is a square matrix of order 2, then
11 12
21 22
a a
A =
a a
 
 
 
|A| = = a11a22 – a21a12
a a
a a
1
1 1
2
2
1 2
2
Matrices & Determinants
Example
4 - 3
Evaluate the determinant :
2 5
 
4 - 3
Solution : = 4 ×5 - 2 × -3 = 20 + 6 = 26
2 5
Matrices & Determinants
Solution
If A = is a square matrix of order 3, then
11 12 13
21 22 23
31 32 33
a a a
a a a
a a a
 
 
 
 
 
[Expanding along first row]
11 12 13
22 23 21 23 21 22
21 22 23 11 12 13
32 33 31 33 31 32
31 32 33
a a a
a a a a a a
| A |= a a a = a - a + a
a a a a a a
a a a
     
11 22 33 32 23 12 21 33 31 23 13 21 32 31 22
= a a a - a a - a a a - a a + a a a - a a
   
11 22 33 12 31 23 13 21 32 11 23 32 12 21 33 13 31 22
a a a a a a a a a a a a a a a a a a
     
Matrices & Determinants
Example
2 3 - 5
Evaluate the determinant : 7 1 - 2
-3 4 1
 
2 3 - 5
1 - 2 7 - 2 7 1
7 1 - 2 = 2 - 3 + -5
4 1 -3 1 -3 4
-3 4 1
     
= 2 1+ 8 - 3 7 - 6 - 5 28 + 3
= 18 - 3 - 155
= -140
[Expanding along first row]
Solution :
Matrices & Determinants
Minors
-1 4
If A = , then
2 3
 
 
 
21 21 22 22
M = Minor of a = 4, M = Minor of a = -1
11 11 12 12
M = Minor of a = 3, M = Minor of a = 2
Matrices & Determinants
Minors
4 7 8
If A = -9 0 0 , then
2 3 4
 
 
 
 
 
M11 = Minor of a11 = determinant of the order 2 × 2 square
sub-matrix is obtained by leaving first
row and first column of A
0 0
= =0
3 4
Similarly, M23 = Minor of a23
4 7
= =12-14=-2
2 3
M32 = Minor of a32 etc.
4 8
= =0+72=72
-9 0
Matrices & Determinants
Cofactors
 i+j
ij ij ij
C = Cofactor of a in A = -1 M ,
ij ij
where M is minor of a in A
Matrices & Determinants
Cofactors (Con.)
C11 = Cofactor of a11 = (–1)1 + 1 M11 = (–1)1 +1
0 0
=0
3 4
C23 = Cofactor of a23 = (–1)2 + 3 M23 =  
4 7
2
2 3
C32 = Cofactor of a32 = (–1)3 + 2M32 = etc.
4 8
- =-72
-9 0
4 7 8
A = -9 0 0
2 3 4
 
 
 
 
 
Matrices & Determinants
Value of Determinant in Terms of
Minors and Cofactors
11 12 13
21 22 23
31 32 33
a a a
If A = a a a , then
a a a
 
 
 
 
 
 
3 3
i j
ij ij ij ij
j 1 j 1
A 1 a M a C

 
  
 
i1 i1 i2 i2 i3 i3
= a C +a C +a C , for i=1 or i=2 or i=3
Matrices & Determinants
Properties of Determinants
1. The value of a determinant remains unchanged, if its
rows and columns are interchanged.
1 1 1 1 2 3
2 2 2 1 2 3
3 3 3 1 2 3
a b c a a a
a b c = b b b
a b c c c c
i e A A

. . '
2. If any two rows (or columns) of a determinant are interchanged,
then the value of the determinant is changed by minus sign.
 
1 1 1 2 2 2
2 2 2 1 1 1 2 1
3 3 3 3 3 3
a b c a b c
a b c = - a b c R R
a b c a b c
Applying 
Matrices & Determinants
Properties (Con.)
3. If all the elements of a row (or column) is multiplied by a
non-zero number k, then the value of the new determinant
is k times the value of the original determinant.
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
ka kb kc a b c
a b c = k a b c
a b c a b c
which also implies
1 1 1 1 1 1
2 2 2 2 2 2
3 3 3 3 3 3
a b c ma mb mc
1
a b c = a b c
m
a b c a b c
Matrices & Determinants
Properties (Con.)
4. If each element of any row (or column) consists of
two or more terms, then the determinant can be
expressed as the sum of two or more determinants.
1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3
a +x b c a b c x b c
a +y b c = a b c + y b c
a +z b c a b c z b c
5. The value of a determinant is unchanged, if any row
(or column) is multiplied by a number and then added
to any other row (or column).
 
1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 1 1 2 3
3 3 3 3 3 3 3 3
a b c a +mb - nc b c
a b c = a +mb - nc b c C C + mC -nC
a b c a +mb - nc b c
Applying 
Matrices & Determinants
Properties (Con.)
6. If any two rows (or columns) of a determinant are
identical, then its value is zero.
2 2 2
3 3 3
0 0 0
a b c = 0
a b c
7. If each element of a row (or column) of a determinant is zero,
then its value is zero.
1 1 1
2 2 2
1 1 1
a b c
a b c = 0
a b c
Matrices & Determinants
Properties (Con.)
 
a 0 0
8 Let A = 0 b 0 be a diagonal matrix, then
0 0 c
 
 
 
 
 
a 0 0
= 0 b 0
0 0 c
A abc

Matrices & Determinants
Row(Column) Operations
Following are the notations to evaluate a determinant:
Similar notations can be used to denote column
operations by replacing R with C.
(i) Ri to denote ith row
(ii) Ri Rj to denote the interchange of ith and jth
rows.
(iii) Ri Ri + lRj to denote the addition of l times the
elements of jth row to the corresponding elements
of ith row.
(iv) lRi to denote the multiplication of all elements of
ith row by l.


Matrices & Determinants
Evaluation of Determinants
If a determinant becomes zero on putting
is the factor of the determinant.
 
x = , then x -
 
2
3
x 5 2
For example, if Δ = x 9 4 , then at x =2
x 16 8
, because C1 and C2 are identical at x = 2
Hence, (x – 2) is a factor of determinant .
  0

Matrices & Determinants
Sign System for Expansion of
Determinant
Sign System for order 2 and order 3 are
given by
+ – +
+ –
, – + –
– +
+ – +
Matrices & Determinants
 
42 1 6 6×7 1 6
i 28 7 4 = 4×7 7 4
14 3 2 2×7 3 2
 
1
6 1 6
=7 4 7 4 Taking out 7 common from C
2 3 2
Example-1
6 -3 2
2 -1 2
-10 5 2
42 1 6
28 7 4
14 3 2
Find the value of the following determinants
(i) (ii)
Solution :
1 3
= 7 × 0 C and C are identical
= 0
 
 
Matrices & Determinants
Example –1 (ii)
6 -3 2
2 -1 2
-10 5 2
(ii)
 
 
 
   
    
 
3 2 3 2
1 2 1 2
5 2 5 2
 
    
 
 
    
 

1
1 2
3 3 2
( 2) 1 1 2 Taking out 2 common from C
5 5 2
( 2) 0 C and C are identical
0
Matrices & Determinants
Evaluate the determinant
1 a b+c
1 b c+a
1 c a+b
Solution :
 
3 2 3
1 a b+c 1 a a+b+c
1 b c+a = 1 b a+b+c Applying c c +c
1 c a+b 1 c a+b+c

    3
1 a 1
= a+b+c 1 b 1 Taking a+b+c common from C
1 c 1
 
 
Example - 2
  1 3
= a + b + c × 0 C and C are identical
= 0
 
 
Matrices & Determinants
2 2 2
a b c
We have a b c
bc ca ab
 
2
1 1 2 2 2 3
(a-b) b-c c
= (a-b)(a+b) (b-c)(b+c) c Applying C C -C and C C -C
-c(a-b) -a(b-c) ab
 
   
2
1 2
1 1 c
Taking a-b and b-c common
=(a-b)(b-c) a+b b+c c
from C and C respectively
-c -a ab
 
 
 
Example - 3
bc
2 2 2
a b c
a b c
ca ab
Evaluate the determinant:
Solution:
Matrices & Determinants
 
2
1 1 2
0 1 c
=(a-b)(b-c) -(c-a) b+c c Applying c c -c
-(c-a) -a ab

2
0 1 c
=-(a-b)(b-c)(c-a) 1 b+c c
1 -a ab
 
2
2 2 3
0 1 c
= -(a-b)(b-c)(c-a) 0 a+b+c c -ab Applying R R -R
1 -a ab

Now expanding along C1 , we get
(a-b) (b-c) (c-a) [- (c2 – ab – ac – bc – c2)]
= (a-b) (b-c) (c-a) (ab + bc + ac)
Solution Cont.
Matrices & Determinants
Without expanding the determinant,
prove that
3
3x+y 2x x
4x+3y 3x 3x =x
5x+6y 4x 6x
3x+y 2x x 3x 2x x y 2x x
L.H.S= 4x+3y 3x 3x = 4x 3x 3x + 3y 3x 3x
5x+6y 4x 6x 5x 4x 6x 6y 4x 6x
3 2
3 2 1 1 2 1
= x 4 3 3 +x y 3 3 3
5 4 6 6 4 6
Example-4
Solution :
 
3 2
1 2
3 2 1
= x 4 3 3 +x y×0 C and C are identical in II determinant
5 4 6
Matrices & Determinants
Solution Cont.
 
3
1 1 2
1 2 1
= x 1 3 3 Applying C C -C
1 4 6

 
3
2 2 1 3 3 2
1 2 1
=x 0 1 2 ApplyingR R -R and R R -R
0 1 3
 
 
3
1
3
= x ×(3-2) Expanding along C
=x = R.H.S.
3
3 2 1
=x 4 3 3
5 4 6
Matrices & Determinants
Prove that : = 0 , where wis cube root of unity.
3 5
3 4
5 5
1 ω ω
ω 1 ω
ω ω 1
3 5 3 3 2
3 4 3 3
5 5 3 2 3 2
1 ω ω 1 ω ω .ω
L.H.S= ω 1 ω = ω 1 ω .ω
ω ω 1 ω .ω ω .ω 1
 
2
3
2 2
1 2
1 1 ω
= 1 1 ω ω =1
ω ω 1
=0=R.H.S. C and C are identical
 
 
Example -5
Solution :
Matrices & Determinants
Example-6
2
x+a b c
a x+b c =x (x+a+b+c)
a b x+C
Prove that :
 
1 1 2 3
x+a b c x+a+b+c b c
L.H.S= a x+b c = x+a+b+c x+b c
a b x+C x+a+b+c b x+c
Applying C C +C +C

Solution :
 
  1
1 b c
= x+a+b+c 1 x+b c
1 b x+c
Taking x+a+b+c commonfrom C
 
 
Matrices & Determinants
Solution cont.
 
2 2 1 3 3 1
1 b c
=(x+a+b+c) 0 x 0
0 0 x
Applying R R -R and R R -R
 
Expanding along C1 , we get
(x + a + b + c) [1(x2)] = x2 (x + a + b + c)
= R.H.S
Matrices & Determinants
 
1 1 2 3
2(a+b+c) 2(a+b+c) 2(a+b+c)
= c+a a+b b+c Applying R R +R +R
a+b b+c c+a

1 1 1
=2(a+b+c) c+a a+b b+c
a+b b+c c+a
Example -7
Solution :
Using properties of determinants, prove that
2 2 2
b+c c+a a+b
c+a a+b b+c =2(a+b+c)(ab+bc+ca-a -b -c ).
a+b b+c c+a
b+c c+a a+b
L.H.S= c+a a+b b+c
a+b b+c c+a
Matrices & Determinants
 
1 1 2 2 2 3
0 0 1
=2(a+b+c)(c-b) (a-c) b+c Applying C C -C and C C -C
(a-c) (b-a) c+a
 
Now expanding along R1 , we get
2
2(a+b+c) (c-b)(b-a)-(a-c)
 
 
2 2 2
=2(a+b+c) bc -b - ac+ab-(a +c -2ac)
 
 
Solution Cont.
2 2 2
=2(a+b+c) ab+bc+ac-a -b -c
=R.H.S
 
 
Matrices & Determinants
Using properties of determinants prove that
2
x+4 2x 2x
2x x+4 2x =(5x+4)(4-x)
2x 2x x+4
Example - 8
1 2x 2x
=(5x+4)1 x+4 2x
1 2x x+4
Solution :
 
1 1 2 3
x+4 2x 2x 5x+4 2x 2x
L.H.S= 2x x+4 2x =5x+4 x+4 2x Applying C C +C +C
2x 2x x+4 5x+4 2x x+4

Matrices & Determinants
Solution Cont.
 
2 2 1 3 3 2
1 2x 2x
=(5x+4) 0 -(x-4) 0 ApplyingR R -R and R R -R
0 x-4 -(x-4)
 
Now expanding along C1 , we get
2
(5x+4) 1(x- 4) -0
 
 
2
=(5x+4)(4-x)
=R.H.S
Matrices & Determinants
Example -9
Using properties of determinants, prove that
x+9 x x
x x+9 x =243 (x+3)
x x x+9
x+9 x x
L.H.S= x x+9 x
x x x+9
 
1 1 2 3
3x+9 x x
= 3x+9 x+9 x Applying C C +C +C
3x+9 x x+9

Solution :
Matrices & Determinants
 
1
=3(x+3) 81 Expanding along C
=243(x+3)
=R.H.S.

1 x x
=(3x+9)1 x+9 x
1 x x+9
Solution Cont.
  2 2 1 3 3 2
1 x x
=3 x+3 0 9 0 Applying R R -R and R R -R
0 -9 9
 
 
 
Matrices & Determinants
Example -10
Solution :
2 2 2 2 2
2 2 2 2 2
1 1 3
2 2 2 2 2
(b+c) a bc b +c a bc
L.H.S.= (c+a) b ca = c +a b ca Applying C C -2C
(a+b) c ab a +b c ab
 

 
 
2 2 2 2
2 2 2 2
1 1 2
2 2 2 2
a +b +c a bc
a +b +c b ca Applying C C +C
a +b +c c ab
 
2
2 2 2 2
2
1 a bc
=(a +b +c )1 b ca
1 c ab
2 2
2 2 2 2 2
2 2
(b+c) a bc
(c+a) b ca =(a +b +c )(a-b)(b-c)(c-a)(a+b+c)
(a+b) c ab
Show that
Matrices & Determinants
Solution Cont.
 
2
2 2 2
2 2 1 3 3 2
1 a bc
=(a +b +c ) 0 (b-a)(b+a) c(a-b) Applying R R -R and R R -R
0 (c-b)(c+b) a(b-c)
 
 
2 2 2 2 2
1
=(a +b +c )(a-b)(b-c)(-ab-a +bc+c ) Expanding along C
2 2 2
=(a +b +c )(a-b)(b-c)(c-a)(a+b+c)=R.H.S.
2
2 2 2
1 a bc
=(a +b +c )(a-b)(b-c) 0 -(b+a) c
0 -(b+c) a
    
2 2 2
=(a +b +c )(a-b)(b-c) b c-a + c-a c+a
 
 
Matrices & Determinants
Applications of Determinants
(Area of a Triangle)
The area of a triangle whose vertices are
is given by the expression
1 1 2 2 3 3
(x , y ), (x , y ) and (x , y )
1 1
2 2
3 3
x y 1
1
Δ= x y 1
2
x y 1
1 2 3 2 3 1 3 1 2
1
= [x (y - y ) + x (y - y ) + x (y - y )]
2
Matrices & Determinants
Example
Find the area of a triangle whose
vertices are (-1, 8), (-2, -3) and (3, 2).
Solution :
1 1
2 2
3 3
x y 1 -1 8 1
1 1
Area of triangle= x y 1 = -2 -3 1
2 2
x y 1 3 2 1
 
1
= -1(-3-2)-8(-2-3)+1(-4+9)
2
 
1
= 5+40+5 =25 sq.units
2
Matrices & Determinants
Condition of Collinearity of Three
Points
If are three points,
then A, B, C are collinear
1 1 2 2 3 3
A (x , y ), B (x , y ) and C (x , y )
1 1 1 1
2 2 2 2
3 3 3 3
Area of triangle ABC = 0
x y 1 x y 1
1
x y 1 = 0 x y 1 = 0
2
x y 1 x y 1

 
Matrices & Determinants
If the points (x, -2) , (5, 2), (8, 8) are collinear,
find x , using determinants.
Example
Solution :
x -2 1
5 2 1 =0
8 8 1

      
x 2-8 - -2 5-8 +1 40-16 =0

-6x-6+24=0

6x=18 x=3
 
Since the given points are collinear.
Matrices & Determinants
Solution of System of 2 Linear
Equations (Cramer’s Rule)
Let the system of linear equations be
 
2 2 2
a x+b y = c ... ii
 
1 1 1
a x+b y = c ... i
1 2
D D
Then x = , y = provided D 0,
D D

1 1 1 1 1 1
1 2
2 2 2 2 2 2
a b c b a c
where D = , D = and D =
a b c b a c
Matrices & Determinants
Cramer’s Rule (Con.)
then the system is consistent and has infinitely many
solutions.
  1 2
2 If D = 0 and D = D = 0,
then the system is inconsistent and has no solution.
 
1 If D 0
Note :
,

then the system is consistent and has unique solution.
  1 2
3 If D=0 and one of D , D 0,

Matrices & Determinants
Example
2 -3
D= =2+9=11 0
3 1

1
7 -3
D = =7+15=22
5 1
2
2 7
D = =10-21=-11
3 5
Solution :
1 2
D 0
D D
22 -11
By Cramer's Rule x= = =2 and y= = =-1
D 11 D 11


Using Cramer's rule , solve the following
system of equations 2x-3y=7, 3x+y=5
Matrices & Determinants
Solution of System of 3 Linear
Equations (Cramer’s Rule)
Let the system of linear equations be
 
2 2 2 2
a x+b y+c z = d ... ii
 
1 1 1 1
a x+b y+c z = d ... i
 
3 3 3 3
a x+b y+c z = d ... iii
3
1 2 D
D D
Then x = , y = z = provided D 0,
D D D
, 
1 1 1 1 1 1 1 1 1
2 2 2 1 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3
a b c d b c a d c
where D = a b c , D = d b c , D = a d c
a b c d b c a d c
1 1 1
3 2 2 2
3 3 3
a b d
and D = a b d
a b d
Matrices & Determinants
Cramer’s Rule (Con.)
Note:
(1) If D  0, then the system is consistent and has a unique
solution.
(2) If D=0 and D1 = D2 = D3 = 0, then the system has infinite
solutions or no solution.
(3) If D = 0 and one of D1, D2, D3  0, then the system
is inconsistent and has no solution.
(4) If d1 = d2 = d3 = 0, then the system is called the system of
homogeneous linear equations.
(i) If D  0, then the system has only trivial solution x = y = z = 0.
(ii) If D = 0, then the system has infinite solutions.
Matrices & Determinants
Example
Using Cramer's rule , solve the following
system of equations
5x - y+ 4z = 5
2x + 3y+ 5z = 2
5x - 2y + 6z = -1
Solution :
5 -1 4
D= 2 3 5
5 -2 6
1
5 -1 4
D = 2 3 5
-1 -2 6
= 5(18+10)+1(12+5)+4(-4 +3)
= 140 +17 –4
= 153
= 5(18+10) + 1(12-25)+4(-4 -15)
= 140 –13 –76 =140 - 89
= 51 0

Matrices & Determinants
3
5 -1 5
D = 2 3 2
5 -2 -1
= 5(-3 +4)+1(-2 - 10)+5(-4-15)
= 5 – 12 – 95 = 5 - 107
= - 102
Solution Cont.
1 2
3
D 0
D D
153 102
By Cramer's Rule x = = =3, y = = =2
D 51 D 51
D -102
and z= = =-2
D 51


2
5 5 4
D = 2 2 5
5 -1 6
= 5(12 +5)+5(12 - 25)+ 4(-2 - 10)
= 85 + 65 – 48 = 150 - 48
= 102
Matrices & Determinants
Example
Solve the following system of homogeneous linear equations:
x + y – z = 0, x – 2y + z = 0, 3x + 6y + -5z = 0
Solution:
     
1 1 - 1
We have D = 1 - 2 1 = 1 10 - 6 - 1 -5 - 3 - 1 6 + 6
3 6 - 5
= 4 + 8 - 12 = 0
 
 
 
 
 
The systemhas infinitely many solutions.

Putting z = k, in first two equations, we get
x + y = k, x – 2y = -k
Matrices & Determinants
Solution (Con.)
1
k 1
D -k - 2 -2k + k k
By Cramer's rule x = = = =
D -2 - 1 3
1 1
1 - 2

2
1 k
D 1 - k -k - k 2k
y = = = =
D -2 - 1 3
1 1
1 - 2
k 2k
x = , y = , z = k , where k R
3 3
 
These values of x, y and z = k satisfy (iii) equation.
Matrices & Determinants
Thank you

More Related Content

What's hot

Graphs of trigonometry functions
Graphs of trigonometry functionsGraphs of trigonometry functions
Graphs of trigonometry functionslgemgnani
 
1.3 Complex Numbers
1.3 Complex Numbers1.3 Complex Numbers
1.3 Complex Numberssmiller5
 
Further pure mathmatics 3 vectors
Further pure mathmatics 3 vectorsFurther pure mathmatics 3 vectors
Further pure mathmatics 3 vectorsDennis Almeida
 
x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!
x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!
x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!Junpei Tsuji
 
Methods of solving ODE
Methods of solving ODEMethods of solving ODE
Methods of solving ODEkishor pokar
 
A Solutions To Exercises On Complex Numbers
A Solutions To Exercises On Complex NumbersA Solutions To Exercises On Complex Numbers
A Solutions To Exercises On Complex NumbersScott Bou
 
Systems of Equations by Elimination
Systems of Equations by EliminationSystems of Equations by Elimination
Systems of Equations by Eliminationmelissabarnhart
 
Equation of a Circle in standard and general form
Equation of  a Circle in standard and general formEquation of  a Circle in standard and general form
Equation of a Circle in standard and general formAraceliLynPalomillo
 
Cauchy integral theorem & formula (complex variable & numerical method )
Cauchy integral theorem & formula (complex variable & numerical method )Cauchy integral theorem & formula (complex variable & numerical method )
Cauchy integral theorem & formula (complex variable & numerical method )Digvijaysinh Gohil
 
3.3 the fundamental theorem of algebra t
3.3 the fundamental theorem of algebra t3.3 the fundamental theorem of algebra t
3.3 the fundamental theorem of algebra tmath260
 
Remainder and Factor Theorem
Remainder and Factor TheoremRemainder and Factor Theorem
Remainder and Factor TheoremTrish Hammond
 
3.1 properties of logarithm
3.1 properties of logarithm3.1 properties of logarithm
3.1 properties of logarithmmath123c
 
Mcq differential and ordinary differential equation
Mcq differential and ordinary differential equationMcq differential and ordinary differential equation
Mcq differential and ordinary differential equationSayyad Shafi
 
Geometric progressions
Geometric progressionsGeometric progressions
Geometric progressionsDr. Nirav Vyas
 
Complex Numbers Mathmatics N4
Complex  Numbers  Mathmatics N4Complex  Numbers  Mathmatics N4
Complex Numbers Mathmatics N4Jude Jay
 
Linear equation in two variables
Linear equation in two variablesLinear equation in two variables
Linear equation in two variablesMERBGOI
 

What's hot (20)

Graphs of trigonometry functions
Graphs of trigonometry functionsGraphs of trigonometry functions
Graphs of trigonometry functions
 
1.3 Complex Numbers
1.3 Complex Numbers1.3 Complex Numbers
1.3 Complex Numbers
 
Determinants
DeterminantsDeterminants
Determinants
 
Further pure mathmatics 3 vectors
Further pure mathmatics 3 vectorsFurther pure mathmatics 3 vectors
Further pure mathmatics 3 vectors
 
Remainder theorem
Remainder theoremRemainder theorem
Remainder theorem
 
THE BINOMIAL THEOREM
THE BINOMIAL THEOREM THE BINOMIAL THEOREM
THE BINOMIAL THEOREM
 
x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!
x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!
x^2 + ny^2 の形で表せる素数 - めざせプライムマスター!
 
Methods of solving ODE
Methods of solving ODEMethods of solving ODE
Methods of solving ODE
 
A Solutions To Exercises On Complex Numbers
A Solutions To Exercises On Complex NumbersA Solutions To Exercises On Complex Numbers
A Solutions To Exercises On Complex Numbers
 
Determinants
DeterminantsDeterminants
Determinants
 
Systems of Equations by Elimination
Systems of Equations by EliminationSystems of Equations by Elimination
Systems of Equations by Elimination
 
Equation of a Circle in standard and general form
Equation of  a Circle in standard and general formEquation of  a Circle in standard and general form
Equation of a Circle in standard and general form
 
Cauchy integral theorem & formula (complex variable & numerical method )
Cauchy integral theorem & formula (complex variable & numerical method )Cauchy integral theorem & formula (complex variable & numerical method )
Cauchy integral theorem & formula (complex variable & numerical method )
 
3.3 the fundamental theorem of algebra t
3.3 the fundamental theorem of algebra t3.3 the fundamental theorem of algebra t
3.3 the fundamental theorem of algebra t
 
Remainder and Factor Theorem
Remainder and Factor TheoremRemainder and Factor Theorem
Remainder and Factor Theorem
 
3.1 properties of logarithm
3.1 properties of logarithm3.1 properties of logarithm
3.1 properties of logarithm
 
Mcq differential and ordinary differential equation
Mcq differential and ordinary differential equationMcq differential and ordinary differential equation
Mcq differential and ordinary differential equation
 
Geometric progressions
Geometric progressionsGeometric progressions
Geometric progressions
 
Complex Numbers Mathmatics N4
Complex  Numbers  Mathmatics N4Complex  Numbers  Mathmatics N4
Complex Numbers Mathmatics N4
 
Linear equation in two variables
Linear equation in two variablesLinear equation in two variables
Linear equation in two variables
 

Similar to Introduction of determinant

Determinants and matrices.ppt
Determinants and matrices.pptDeterminants and matrices.ppt
Determinants and matrices.pptSauravDash10
 
chp-1-matrices-determinants1 (2).ppt
chp-1-matrices-determinants1 (2).pptchp-1-matrices-determinants1 (2).ppt
chp-1-matrices-determinants1 (2).pptrushikumar17
 
chp-1-matrices-determinants1.ppt
chp-1-matrices-determinants1.pptchp-1-matrices-determinants1.ppt
chp-1-matrices-determinants1.pptMichealMicheal11
 
determinants-160504230830_repaired.pdf
determinants-160504230830_repaired.pdfdeterminants-160504230830_repaired.pdf
determinants-160504230830_repaired.pdfTGBSmile
 
matrices and determinantes
matrices and determinantes matrices and determinantes
matrices and determinantes gandhinagar
 
Solucao_Marion_Thornton_Dinamica_Classic (1).pdf
Solucao_Marion_Thornton_Dinamica_Classic (1).pdfSolucao_Marion_Thornton_Dinamica_Classic (1).pdf
Solucao_Marion_Thornton_Dinamica_Classic (1).pdfFranciscoJavierCaedo
 
Chapter 3: Linear Systems and Matrices - Part 3/Slides
Chapter 3: Linear Systems and Matrices - Part 3/SlidesChapter 3: Linear Systems and Matrices - Part 3/Slides
Chapter 3: Linear Systems and Matrices - Part 3/SlidesChaimae Baroudi
 
MATRICES maths project.pptxsgdhdghdgf gr to f HR f
MATRICES maths project.pptxsgdhdghdgf gr to f HR fMATRICES maths project.pptxsgdhdghdgf gr to f HR f
MATRICES maths project.pptxsgdhdghdgf gr to f HR fpremkumar24914
 
267 4 determinant and cross product-n
267 4 determinant and cross product-n267 4 determinant and cross product-n
267 4 determinant and cross product-nmath260
 
Matrix and its operations
Matrix and its operationsMatrix and its operations
Matrix and its operationsPankaj Das
 
Sample0 mtechcs06
Sample0 mtechcs06Sample0 mtechcs06
Sample0 mtechcs06bikram ...
 
Sample0 mtechcs06
Sample0 mtechcs06Sample0 mtechcs06
Sample0 mtechcs06bikram ...
 
Matrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANK
Matrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANKMatrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANK
Matrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANKWaqas Afzal
 

Similar to Introduction of determinant (20)

Determinants and matrices.ppt
Determinants and matrices.pptDeterminants and matrices.ppt
Determinants and matrices.ppt
 
chp-1-matrices-determinants1 (2).ppt
chp-1-matrices-determinants1 (2).pptchp-1-matrices-determinants1 (2).ppt
chp-1-matrices-determinants1 (2).ppt
 
chp-1-matrices-determinants1.ppt
chp-1-matrices-determinants1.pptchp-1-matrices-determinants1.ppt
chp-1-matrices-determinants1.ppt
 
determinants-160504230830.pdf
determinants-160504230830.pdfdeterminants-160504230830.pdf
determinants-160504230830.pdf
 
determinants-160504230830_repaired.pdf
determinants-160504230830_repaired.pdfdeterminants-160504230830_repaired.pdf
determinants-160504230830_repaired.pdf
 
matrices and determinantes
matrices and determinantes matrices and determinantes
matrices and determinantes
 
TABREZ KHAN.ppt
TABREZ KHAN.pptTABREZ KHAN.ppt
TABREZ KHAN.ppt
 
Chapter0
Chapter0Chapter0
Chapter0
 
Solucao_Marion_Thornton_Dinamica_Classic (1).pdf
Solucao_Marion_Thornton_Dinamica_Classic (1).pdfSolucao_Marion_Thornton_Dinamica_Classic (1).pdf
Solucao_Marion_Thornton_Dinamica_Classic (1).pdf
 
Chapter 3: Linear Systems and Matrices - Part 3/Slides
Chapter 3: Linear Systems and Matrices - Part 3/SlidesChapter 3: Linear Systems and Matrices - Part 3/Slides
Chapter 3: Linear Systems and Matrices - Part 3/Slides
 
Matrices & Determinants
Matrices & DeterminantsMatrices & Determinants
Matrices & Determinants
 
MATRICES maths project.pptxsgdhdghdgf gr to f HR f
MATRICES maths project.pptxsgdhdghdgf gr to f HR fMATRICES maths project.pptxsgdhdghdgf gr to f HR f
MATRICES maths project.pptxsgdhdghdgf gr to f HR f
 
267 4 determinant and cross product-n
267 4 determinant and cross product-n267 4 determinant and cross product-n
267 4 determinant and cross product-n
 
Matrix and its operations
Matrix and its operationsMatrix and its operations
Matrix and its operations
 
Sample0 mtechcs06
Sample0 mtechcs06Sample0 mtechcs06
Sample0 mtechcs06
 
Sample0 mtechcs06
Sample0 mtechcs06Sample0 mtechcs06
Sample0 mtechcs06
 
Vivek
VivekVivek
Vivek
 
Hprec5.1
Hprec5.1Hprec5.1
Hprec5.1
 
Matrices
MatricesMatrices
Matrices
 
Matrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANK
Matrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANKMatrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANK
Matrices ,Basics, Determinant, Inverse, EigenValues, Linear Equations, RANK
 

More from Pankaj Das

Statistical quality control introduction
Statistical quality control introductionStatistical quality control introduction
Statistical quality control introductionPankaj Das
 
Methods of integration
Methods of integrationMethods of integration
Methods of integrationPankaj Das
 
Applications of integration
Applications of integrationApplications of integration
Applications of integrationPankaj Das
 
Set theory and its operations
Set theory and its operations Set theory and its operations
Set theory and its operations Pankaj Das
 
Introduction of vectors
Introduction of vectorsIntroduction of vectors
Introduction of vectorsPankaj Das
 
Introduction of matrix
Introduction of matrixIntroduction of matrix
Introduction of matrixPankaj Das
 

More from Pankaj Das (6)

Statistical quality control introduction
Statistical quality control introductionStatistical quality control introduction
Statistical quality control introduction
 
Methods of integration
Methods of integrationMethods of integration
Methods of integration
 
Applications of integration
Applications of integrationApplications of integration
Applications of integration
 
Set theory and its operations
Set theory and its operations Set theory and its operations
Set theory and its operations
 
Introduction of vectors
Introduction of vectorsIntroduction of vectors
Introduction of vectors
 
Introduction of matrix
Introduction of matrixIntroduction of matrix
Introduction of matrix
 

Recently uploaded

PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATIONPRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATIONChetanK57
 
FAIRSpectra - Towards a common data file format for SIMS images
FAIRSpectra - Towards a common data file format for SIMS imagesFAIRSpectra - Towards a common data file format for SIMS images
FAIRSpectra - Towards a common data file format for SIMS imagesAlex Henderson
 
Hemoglobin metabolism_pathophysiology.pptx
Hemoglobin metabolism_pathophysiology.pptxHemoglobin metabolism_pathophysiology.pptx
Hemoglobin metabolism_pathophysiology.pptxmuralinath2
 
Pests of sugarcane_Binomics_IPM_Dr.UPR.pdf
Pests of sugarcane_Binomics_IPM_Dr.UPR.pdfPests of sugarcane_Binomics_IPM_Dr.UPR.pdf
Pests of sugarcane_Binomics_IPM_Dr.UPR.pdfPirithiRaju
 
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
 
Predicting property prices with machine learning algorithms.pdf
Predicting property prices with machine learning algorithms.pdfPredicting property prices with machine learning algorithms.pdf
Predicting property prices with machine learning algorithms.pdfbinhminhvu04
 
Topography and sediments of the floor of the Bay of Bengal
Topography and sediments of the floor of the Bay of BengalTopography and sediments of the floor of the Bay of Bengal
Topography and sediments of the floor of the Bay of BengalMd Hasan Tareq
 
Microbial Type Culture Collection (MTCC)
Microbial Type Culture Collection (MTCC)Microbial Type Culture Collection (MTCC)
Microbial Type Culture Collection (MTCC)abhishekdhamu51
 
INSIGHT Partner Profile: Tampere University
INSIGHT Partner Profile: Tampere UniversityINSIGHT Partner Profile: Tampere University
INSIGHT Partner Profile: Tampere UniversitySteffi Friedrichs
 
Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...
Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...
Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...Alba Morales
 
Structures and textures of metamorphic rocks
Structures and textures of metamorphic rocksStructures and textures of metamorphic rocks
Structures and textures of metamorphic rockskumarmathi863
 
platelets- lifespan -Clot retraction-disorders.pptx
platelets- lifespan -Clot retraction-disorders.pptxplatelets- lifespan -Clot retraction-disorders.pptx
platelets- lifespan -Clot retraction-disorders.pptxmuralinath2
 
GEOLOGICAL FIELD REPORT On Kaptai Rangamati Road-Cut Section.pdf
GEOLOGICAL FIELD REPORT  On  Kaptai Rangamati Road-Cut Section.pdfGEOLOGICAL FIELD REPORT  On  Kaptai Rangamati Road-Cut Section.pdf
GEOLOGICAL FIELD REPORT On Kaptai Rangamati Road-Cut Section.pdfUniversity of Barishal
 
Lab report on liquid viscosity of glycerin
Lab report on liquid viscosity of glycerinLab report on liquid viscosity of glycerin
Lab report on liquid viscosity of glycerinossaicprecious19
 
National Biodiversity protection initiatives and Convention on Biological Di...
National Biodiversity protection initiatives and  Convention on Biological Di...National Biodiversity protection initiatives and  Convention on Biological Di...
National Biodiversity protection initiatives and Convention on Biological Di...PABOLU TEJASREE
 
Shuaib Y-basedComprehensive mahmudj.pptx
Shuaib Y-basedComprehensive mahmudj.pptxShuaib Y-basedComprehensive mahmudj.pptx
Shuaib Y-basedComprehensive mahmudj.pptxMdAbuRayhan16
 
Transport in plants G1.pptx Cambridge IGCSE
Transport in plants G1.pptx Cambridge IGCSETransport in plants G1.pptx Cambridge IGCSE
Transport in plants G1.pptx Cambridge IGCSEjordanparish425
 
Anemia_ different types_causes_ conditions
Anemia_ different types_causes_ conditionsAnemia_ different types_causes_ conditions
Anemia_ different types_causes_ conditionsmuralinath2
 
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...Sérgio Sacani
 
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
 

Recently uploaded (20)

PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATIONPRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
PRESENTATION ABOUT PRINCIPLE OF COSMATIC EVALUATION
 
FAIRSpectra - Towards a common data file format for SIMS images
FAIRSpectra - Towards a common data file format for SIMS imagesFAIRSpectra - Towards a common data file format for SIMS images
FAIRSpectra - Towards a common data file format for SIMS images
 
Hemoglobin metabolism_pathophysiology.pptx
Hemoglobin metabolism_pathophysiology.pptxHemoglobin metabolism_pathophysiology.pptx
Hemoglobin metabolism_pathophysiology.pptx
 
Pests of sugarcane_Binomics_IPM_Dr.UPR.pdf
Pests of sugarcane_Binomics_IPM_Dr.UPR.pdfPests of sugarcane_Binomics_IPM_Dr.UPR.pdf
Pests of sugarcane_Binomics_IPM_Dr.UPR.pdf
 
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...
 
Predicting property prices with machine learning algorithms.pdf
Predicting property prices with machine learning algorithms.pdfPredicting property prices with machine learning algorithms.pdf
Predicting property prices with machine learning algorithms.pdf
 
Topography and sediments of the floor of the Bay of Bengal
Topography and sediments of the floor of the Bay of BengalTopography and sediments of the floor of the Bay of Bengal
Topography and sediments of the floor of the Bay of Bengal
 
Microbial Type Culture Collection (MTCC)
Microbial Type Culture Collection (MTCC)Microbial Type Culture Collection (MTCC)
Microbial Type Culture Collection (MTCC)
 
INSIGHT Partner Profile: Tampere University
INSIGHT Partner Profile: Tampere UniversityINSIGHT Partner Profile: Tampere University
INSIGHT Partner Profile: Tampere University
 
Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...
Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...
Musical Meetups Knowledge Graph (MMKG): a collection of evidence for historic...
 
Structures and textures of metamorphic rocks
Structures and textures of metamorphic rocksStructures and textures of metamorphic rocks
Structures and textures of metamorphic rocks
 
platelets- lifespan -Clot retraction-disorders.pptx
platelets- lifespan -Clot retraction-disorders.pptxplatelets- lifespan -Clot retraction-disorders.pptx
platelets- lifespan -Clot retraction-disorders.pptx
 
GEOLOGICAL FIELD REPORT On Kaptai Rangamati Road-Cut Section.pdf
GEOLOGICAL FIELD REPORT  On  Kaptai Rangamati Road-Cut Section.pdfGEOLOGICAL FIELD REPORT  On  Kaptai Rangamati Road-Cut Section.pdf
GEOLOGICAL FIELD REPORT On Kaptai Rangamati Road-Cut Section.pdf
 
Lab report on liquid viscosity of glycerin
Lab report on liquid viscosity of glycerinLab report on liquid viscosity of glycerin
Lab report on liquid viscosity of glycerin
 
National Biodiversity protection initiatives and Convention on Biological Di...
National Biodiversity protection initiatives and  Convention on Biological Di...National Biodiversity protection initiatives and  Convention on Biological Di...
National Biodiversity protection initiatives and Convention on Biological Di...
 
Shuaib Y-basedComprehensive mahmudj.pptx
Shuaib Y-basedComprehensive mahmudj.pptxShuaib Y-basedComprehensive mahmudj.pptx
Shuaib Y-basedComprehensive mahmudj.pptx
 
Transport in plants G1.pptx Cambridge IGCSE
Transport in plants G1.pptx Cambridge IGCSETransport in plants G1.pptx Cambridge IGCSE
Transport in plants G1.pptx Cambridge IGCSE
 
Anemia_ different types_causes_ conditions
Anemia_ different types_causes_ conditionsAnemia_ different types_causes_ conditions
Anemia_ different types_causes_ conditions
 
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...
Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...
 
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
 

Introduction of determinant

  • 1. Matrices & Determinants Determinants Dr. Pankaj Das Mail id: Pankaj.iasri@gmail.com
  • 2. Matrices & Determinants TYPES OF MATRICES NAME DESCRIPTION EXAMPLE Rectangular matrix No. of rows is not equal to no. of columns Square matrix No. of rows is equal to no. of columns Diagonal matrix Non-zero element in principal diagonal and zero in all other positions Scalar matrix Diagonal matrix in which all the elements on principal diagonal and same         5 0 2 1 2 6  2 1 3 2 0 1 1 2 4                     7 0 0 0 4 0 0 0 2           4 0 0 0 4 0 0 0 4
  • 3. Matrices & Determinants TYPES OF MATRICES NAME DESCRIPTION EXAMPLE Row matrix A matrix with only 1 row Column matrix A matrix with only I column Identity matrix Diagonal matrix having each diagonal element equal to one (I) Zero matrix A matrix with all zero entries  3 2 14    2 3             1 0 0 1 0 0 0 0      
  • 4. Matrices & Determinants TYPES OF MATRICES NAME DESCRIPTION EXAMPLE Upper Triangular matrix Square matrix having all the entries zero below the principal diagonal Lower Triangular matrix Square matrix having all the entries zero above the principal diagonal           7 0 0 6 4 0 3 5 2           7 3 6 0 4 5 0 0 2
  • 5. Matrices & Determinants Determinants If is a square matrix of order 1, then |A| = | a11 | = a11 ij A = a     If is a square matrix of order 2, then 11 12 21 22 a a A = a a       |A| = = a11a22 – a21a12 a a a a 1 1 1 2 2 1 2 2
  • 6. Matrices & Determinants Example 4 - 3 Evaluate the determinant : 2 5   4 - 3 Solution : = 4 ×5 - 2 × -3 = 20 + 6 = 26 2 5
  • 7. Matrices & Determinants Solution If A = is a square matrix of order 3, then 11 12 13 21 22 23 31 32 33 a a a a a a a a a           [Expanding along first row] 11 12 13 22 23 21 23 21 22 21 22 23 11 12 13 32 33 31 33 31 32 31 32 33 a a a a a a a a a | A |= a a a = a - a + a a a a a a a a a a       11 22 33 32 23 12 21 33 31 23 13 21 32 31 22 = a a a - a a - a a a - a a + a a a - a a     11 22 33 12 31 23 13 21 32 11 23 32 12 21 33 13 31 22 a a a a a a a a a a a a a a a a a a      
  • 8. Matrices & Determinants Example 2 3 - 5 Evaluate the determinant : 7 1 - 2 -3 4 1   2 3 - 5 1 - 2 7 - 2 7 1 7 1 - 2 = 2 - 3 + -5 4 1 -3 1 -3 4 -3 4 1       = 2 1+ 8 - 3 7 - 6 - 5 28 + 3 = 18 - 3 - 155 = -140 [Expanding along first row] Solution :
  • 9. Matrices & Determinants Minors -1 4 If A = , then 2 3       21 21 22 22 M = Minor of a = 4, M = Minor of a = -1 11 11 12 12 M = Minor of a = 3, M = Minor of a = 2
  • 10. Matrices & Determinants Minors 4 7 8 If A = -9 0 0 , then 2 3 4           M11 = Minor of a11 = determinant of the order 2 × 2 square sub-matrix is obtained by leaving first row and first column of A 0 0 = =0 3 4 Similarly, M23 = Minor of a23 4 7 = =12-14=-2 2 3 M32 = Minor of a32 etc. 4 8 = =0+72=72 -9 0
  • 11. Matrices & Determinants Cofactors  i+j ij ij ij C = Cofactor of a in A = -1 M , ij ij where M is minor of a in A
  • 12. Matrices & Determinants Cofactors (Con.) C11 = Cofactor of a11 = (–1)1 + 1 M11 = (–1)1 +1 0 0 =0 3 4 C23 = Cofactor of a23 = (–1)2 + 3 M23 =   4 7 2 2 3 C32 = Cofactor of a32 = (–1)3 + 2M32 = etc. 4 8 - =-72 -9 0 4 7 8 A = -9 0 0 2 3 4          
  • 13. Matrices & Determinants Value of Determinant in Terms of Minors and Cofactors 11 12 13 21 22 23 31 32 33 a a a If A = a a a , then a a a             3 3 i j ij ij ij ij j 1 j 1 A 1 a M a C         i1 i1 i2 i2 i3 i3 = a C +a C +a C , for i=1 or i=2 or i=3
  • 14. Matrices & Determinants Properties of Determinants 1. The value of a determinant remains unchanged, if its rows and columns are interchanged. 1 1 1 1 2 3 2 2 2 1 2 3 3 3 3 1 2 3 a b c a a a a b c = b b b a b c c c c i e A A  . . ' 2. If any two rows (or columns) of a determinant are interchanged, then the value of the determinant is changed by minus sign.   1 1 1 2 2 2 2 2 2 1 1 1 2 1 3 3 3 3 3 3 a b c a b c a b c = - a b c R R a b c a b c Applying 
  • 15. Matrices & Determinants Properties (Con.) 3. If all the elements of a row (or column) is multiplied by a non-zero number k, then the value of the new determinant is k times the value of the original determinant. 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 ka kb kc a b c a b c = k a b c a b c a b c which also implies 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 a b c ma mb mc 1 a b c = a b c m a b c a b c
  • 16. Matrices & Determinants Properties (Con.) 4. If each element of any row (or column) consists of two or more terms, then the determinant can be expressed as the sum of two or more determinants. 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 a +x b c a b c x b c a +y b c = a b c + y b c a +z b c a b c z b c 5. The value of a determinant is unchanged, if any row (or column) is multiplied by a number and then added to any other row (or column).   1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 2 3 3 3 3 3 3 3 3 3 a b c a +mb - nc b c a b c = a +mb - nc b c C C + mC -nC a b c a +mb - nc b c Applying 
  • 17. Matrices & Determinants Properties (Con.) 6. If any two rows (or columns) of a determinant are identical, then its value is zero. 2 2 2 3 3 3 0 0 0 a b c = 0 a b c 7. If each element of a row (or column) of a determinant is zero, then its value is zero. 1 1 1 2 2 2 1 1 1 a b c a b c = 0 a b c
  • 18. Matrices & Determinants Properties (Con.)   a 0 0 8 Let A = 0 b 0 be a diagonal matrix, then 0 0 c           a 0 0 = 0 b 0 0 0 c A abc 
  • 19. Matrices & Determinants Row(Column) Operations Following are the notations to evaluate a determinant: Similar notations can be used to denote column operations by replacing R with C. (i) Ri to denote ith row (ii) Ri Rj to denote the interchange of ith and jth rows. (iii) Ri Ri + lRj to denote the addition of l times the elements of jth row to the corresponding elements of ith row. (iv) lRi to denote the multiplication of all elements of ith row by l.  
  • 20. Matrices & Determinants Evaluation of Determinants If a determinant becomes zero on putting is the factor of the determinant.   x = , then x -   2 3 x 5 2 For example, if Δ = x 9 4 , then at x =2 x 16 8 , because C1 and C2 are identical at x = 2 Hence, (x – 2) is a factor of determinant .   0 
  • 21. Matrices & Determinants Sign System for Expansion of Determinant Sign System for order 2 and order 3 are given by + – + + – , – + – – + + – +
  • 22. Matrices & Determinants   42 1 6 6×7 1 6 i 28 7 4 = 4×7 7 4 14 3 2 2×7 3 2   1 6 1 6 =7 4 7 4 Taking out 7 common from C 2 3 2 Example-1 6 -3 2 2 -1 2 -10 5 2 42 1 6 28 7 4 14 3 2 Find the value of the following determinants (i) (ii) Solution : 1 3 = 7 × 0 C and C are identical = 0    
  • 23. Matrices & Determinants Example –1 (ii) 6 -3 2 2 -1 2 -10 5 2 (ii)                  3 2 3 2 1 2 1 2 5 2 5 2                    1 1 2 3 3 2 ( 2) 1 1 2 Taking out 2 common from C 5 5 2 ( 2) 0 C and C are identical 0
  • 24. Matrices & Determinants Evaluate the determinant 1 a b+c 1 b c+a 1 c a+b Solution :   3 2 3 1 a b+c 1 a a+b+c 1 b c+a = 1 b a+b+c Applying c c +c 1 c a+b 1 c a+b+c      3 1 a 1 = a+b+c 1 b 1 Taking a+b+c common from C 1 c 1     Example - 2   1 3 = a + b + c × 0 C and C are identical = 0    
  • 25. Matrices & Determinants 2 2 2 a b c We have a b c bc ca ab   2 1 1 2 2 2 3 (a-b) b-c c = (a-b)(a+b) (b-c)(b+c) c Applying C C -C and C C -C -c(a-b) -a(b-c) ab       2 1 2 1 1 c Taking a-b and b-c common =(a-b)(b-c) a+b b+c c from C and C respectively -c -a ab       Example - 3 bc 2 2 2 a b c a b c ca ab Evaluate the determinant: Solution:
  • 26. Matrices & Determinants   2 1 1 2 0 1 c =(a-b)(b-c) -(c-a) b+c c Applying c c -c -(c-a) -a ab  2 0 1 c =-(a-b)(b-c)(c-a) 1 b+c c 1 -a ab   2 2 2 3 0 1 c = -(a-b)(b-c)(c-a) 0 a+b+c c -ab Applying R R -R 1 -a ab  Now expanding along C1 , we get (a-b) (b-c) (c-a) [- (c2 – ab – ac – bc – c2)] = (a-b) (b-c) (c-a) (ab + bc + ac) Solution Cont.
  • 27. Matrices & Determinants Without expanding the determinant, prove that 3 3x+y 2x x 4x+3y 3x 3x =x 5x+6y 4x 6x 3x+y 2x x 3x 2x x y 2x x L.H.S= 4x+3y 3x 3x = 4x 3x 3x + 3y 3x 3x 5x+6y 4x 6x 5x 4x 6x 6y 4x 6x 3 2 3 2 1 1 2 1 = x 4 3 3 +x y 3 3 3 5 4 6 6 4 6 Example-4 Solution :   3 2 1 2 3 2 1 = x 4 3 3 +x y×0 C and C are identical in II determinant 5 4 6
  • 28. Matrices & Determinants Solution Cont.   3 1 1 2 1 2 1 = x 1 3 3 Applying C C -C 1 4 6    3 2 2 1 3 3 2 1 2 1 =x 0 1 2 ApplyingR R -R and R R -R 0 1 3     3 1 3 = x ×(3-2) Expanding along C =x = R.H.S. 3 3 2 1 =x 4 3 3 5 4 6
  • 29. Matrices & Determinants Prove that : = 0 , where wis cube root of unity. 3 5 3 4 5 5 1 ω ω ω 1 ω ω ω 1 3 5 3 3 2 3 4 3 3 5 5 3 2 3 2 1 ω ω 1 ω ω .ω L.H.S= ω 1 ω = ω 1 ω .ω ω ω 1 ω .ω ω .ω 1   2 3 2 2 1 2 1 1 ω = 1 1 ω ω =1 ω ω 1 =0=R.H.S. C and C are identical     Example -5 Solution :
  • 30. Matrices & Determinants Example-6 2 x+a b c a x+b c =x (x+a+b+c) a b x+C Prove that :   1 1 2 3 x+a b c x+a+b+c b c L.H.S= a x+b c = x+a+b+c x+b c a b x+C x+a+b+c b x+c Applying C C +C +C  Solution :     1 1 b c = x+a+b+c 1 x+b c 1 b x+c Taking x+a+b+c commonfrom C    
  • 31. Matrices & Determinants Solution cont.   2 2 1 3 3 1 1 b c =(x+a+b+c) 0 x 0 0 0 x Applying R R -R and R R -R   Expanding along C1 , we get (x + a + b + c) [1(x2)] = x2 (x + a + b + c) = R.H.S
  • 32. Matrices & Determinants   1 1 2 3 2(a+b+c) 2(a+b+c) 2(a+b+c) = c+a a+b b+c Applying R R +R +R a+b b+c c+a  1 1 1 =2(a+b+c) c+a a+b b+c a+b b+c c+a Example -7 Solution : Using properties of determinants, prove that 2 2 2 b+c c+a a+b c+a a+b b+c =2(a+b+c)(ab+bc+ca-a -b -c ). a+b b+c c+a b+c c+a a+b L.H.S= c+a a+b b+c a+b b+c c+a
  • 33. Matrices & Determinants   1 1 2 2 2 3 0 0 1 =2(a+b+c)(c-b) (a-c) b+c Applying C C -C and C C -C (a-c) (b-a) c+a   Now expanding along R1 , we get 2 2(a+b+c) (c-b)(b-a)-(a-c)     2 2 2 =2(a+b+c) bc -b - ac+ab-(a +c -2ac)     Solution Cont. 2 2 2 =2(a+b+c) ab+bc+ac-a -b -c =R.H.S    
  • 34. Matrices & Determinants Using properties of determinants prove that 2 x+4 2x 2x 2x x+4 2x =(5x+4)(4-x) 2x 2x x+4 Example - 8 1 2x 2x =(5x+4)1 x+4 2x 1 2x x+4 Solution :   1 1 2 3 x+4 2x 2x 5x+4 2x 2x L.H.S= 2x x+4 2x =5x+4 x+4 2x Applying C C +C +C 2x 2x x+4 5x+4 2x x+4 
  • 35. Matrices & Determinants Solution Cont.   2 2 1 3 3 2 1 2x 2x =(5x+4) 0 -(x-4) 0 ApplyingR R -R and R R -R 0 x-4 -(x-4)   Now expanding along C1 , we get 2 (5x+4) 1(x- 4) -0     2 =(5x+4)(4-x) =R.H.S
  • 36. Matrices & Determinants Example -9 Using properties of determinants, prove that x+9 x x x x+9 x =243 (x+3) x x x+9 x+9 x x L.H.S= x x+9 x x x x+9   1 1 2 3 3x+9 x x = 3x+9 x+9 x Applying C C +C +C 3x+9 x x+9  Solution :
  • 37. Matrices & Determinants   1 =3(x+3) 81 Expanding along C =243(x+3) =R.H.S.  1 x x =(3x+9)1 x+9 x 1 x x+9 Solution Cont.   2 2 1 3 3 2 1 x x =3 x+3 0 9 0 Applying R R -R and R R -R 0 -9 9      
  • 38. Matrices & Determinants Example -10 Solution : 2 2 2 2 2 2 2 2 2 2 1 1 3 2 2 2 2 2 (b+c) a bc b +c a bc L.H.S.= (c+a) b ca = c +a b ca Applying C C -2C (a+b) c ab a +b c ab        2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 a +b +c a bc a +b +c b ca Applying C C +C a +b +c c ab   2 2 2 2 2 2 1 a bc =(a +b +c )1 b ca 1 c ab 2 2 2 2 2 2 2 2 2 (b+c) a bc (c+a) b ca =(a +b +c )(a-b)(b-c)(c-a)(a+b+c) (a+b) c ab Show that
  • 39. Matrices & Determinants Solution Cont.   2 2 2 2 2 2 1 3 3 2 1 a bc =(a +b +c ) 0 (b-a)(b+a) c(a-b) Applying R R -R and R R -R 0 (c-b)(c+b) a(b-c)     2 2 2 2 2 1 =(a +b +c )(a-b)(b-c)(-ab-a +bc+c ) Expanding along C 2 2 2 =(a +b +c )(a-b)(b-c)(c-a)(a+b+c)=R.H.S. 2 2 2 2 1 a bc =(a +b +c )(a-b)(b-c) 0 -(b+a) c 0 -(b+c) a      2 2 2 =(a +b +c )(a-b)(b-c) b c-a + c-a c+a    
  • 40. Matrices & Determinants Applications of Determinants (Area of a Triangle) The area of a triangle whose vertices are is given by the expression 1 1 2 2 3 3 (x , y ), (x , y ) and (x , y ) 1 1 2 2 3 3 x y 1 1 Δ= x y 1 2 x y 1 1 2 3 2 3 1 3 1 2 1 = [x (y - y ) + x (y - y ) + x (y - y )] 2
  • 41. Matrices & Determinants Example Find the area of a triangle whose vertices are (-1, 8), (-2, -3) and (3, 2). Solution : 1 1 2 2 3 3 x y 1 -1 8 1 1 1 Area of triangle= x y 1 = -2 -3 1 2 2 x y 1 3 2 1   1 = -1(-3-2)-8(-2-3)+1(-4+9) 2   1 = 5+40+5 =25 sq.units 2
  • 42. Matrices & Determinants Condition of Collinearity of Three Points If are three points, then A, B, C are collinear 1 1 2 2 3 3 A (x , y ), B (x , y ) and C (x , y ) 1 1 1 1 2 2 2 2 3 3 3 3 Area of triangle ABC = 0 x y 1 x y 1 1 x y 1 = 0 x y 1 = 0 2 x y 1 x y 1   
  • 43. Matrices & Determinants If the points (x, -2) , (5, 2), (8, 8) are collinear, find x , using determinants. Example Solution : x -2 1 5 2 1 =0 8 8 1         x 2-8 - -2 5-8 +1 40-16 =0  -6x-6+24=0  6x=18 x=3   Since the given points are collinear.
  • 44. Matrices & Determinants Solution of System of 2 Linear Equations (Cramer’s Rule) Let the system of linear equations be   2 2 2 a x+b y = c ... ii   1 1 1 a x+b y = c ... i 1 2 D D Then x = , y = provided D 0, D D  1 1 1 1 1 1 1 2 2 2 2 2 2 2 a b c b a c where D = , D = and D = a b c b a c
  • 45. Matrices & Determinants Cramer’s Rule (Con.) then the system is consistent and has infinitely many solutions.   1 2 2 If D = 0 and D = D = 0, then the system is inconsistent and has no solution.   1 If D 0 Note : ,  then the system is consistent and has unique solution.   1 2 3 If D=0 and one of D , D 0, 
  • 46. Matrices & Determinants Example 2 -3 D= =2+9=11 0 3 1  1 7 -3 D = =7+15=22 5 1 2 2 7 D = =10-21=-11 3 5 Solution : 1 2 D 0 D D 22 -11 By Cramer's Rule x= = =2 and y= = =-1 D 11 D 11   Using Cramer's rule , solve the following system of equations 2x-3y=7, 3x+y=5
  • 47. Matrices & Determinants Solution of System of 3 Linear Equations (Cramer’s Rule) Let the system of linear equations be   2 2 2 2 a x+b y+c z = d ... ii   1 1 1 1 a x+b y+c z = d ... i   3 3 3 3 a x+b y+c z = d ... iii 3 1 2 D D D Then x = , y = z = provided D 0, D D D ,  1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 a b c d b c a d c where D = a b c , D = d b c , D = a d c a b c d b c a d c 1 1 1 3 2 2 2 3 3 3 a b d and D = a b d a b d
  • 48. Matrices & Determinants Cramer’s Rule (Con.) Note: (1) If D  0, then the system is consistent and has a unique solution. (2) If D=0 and D1 = D2 = D3 = 0, then the system has infinite solutions or no solution. (3) If D = 0 and one of D1, D2, D3  0, then the system is inconsistent and has no solution. (4) If d1 = d2 = d3 = 0, then the system is called the system of homogeneous linear equations. (i) If D  0, then the system has only trivial solution x = y = z = 0. (ii) If D = 0, then the system has infinite solutions.
  • 49. Matrices & Determinants Example Using Cramer's rule , solve the following system of equations 5x - y+ 4z = 5 2x + 3y+ 5z = 2 5x - 2y + 6z = -1 Solution : 5 -1 4 D= 2 3 5 5 -2 6 1 5 -1 4 D = 2 3 5 -1 -2 6 = 5(18+10)+1(12+5)+4(-4 +3) = 140 +17 –4 = 153 = 5(18+10) + 1(12-25)+4(-4 -15) = 140 –13 –76 =140 - 89 = 51 0 
  • 50. Matrices & Determinants 3 5 -1 5 D = 2 3 2 5 -2 -1 = 5(-3 +4)+1(-2 - 10)+5(-4-15) = 5 – 12 – 95 = 5 - 107 = - 102 Solution Cont. 1 2 3 D 0 D D 153 102 By Cramer's Rule x = = =3, y = = =2 D 51 D 51 D -102 and z= = =-2 D 51   2 5 5 4 D = 2 2 5 5 -1 6 = 5(12 +5)+5(12 - 25)+ 4(-2 - 10) = 85 + 65 – 48 = 150 - 48 = 102
  • 51. Matrices & Determinants Example Solve the following system of homogeneous linear equations: x + y – z = 0, x – 2y + z = 0, 3x + 6y + -5z = 0 Solution:       1 1 - 1 We have D = 1 - 2 1 = 1 10 - 6 - 1 -5 - 3 - 1 6 + 6 3 6 - 5 = 4 + 8 - 12 = 0           The systemhas infinitely many solutions.  Putting z = k, in first two equations, we get x + y = k, x – 2y = -k
  • 52. Matrices & Determinants Solution (Con.) 1 k 1 D -k - 2 -2k + k k By Cramer's rule x = = = = D -2 - 1 3 1 1 1 - 2  2 1 k D 1 - k -k - k 2k y = = = = D -2 - 1 3 1 1 1 - 2 k 2k x = , y = , z = k , where k R 3 3   These values of x, y and z = k satisfy (iii) equation.