Algebra
421
0011 0010 1010 1101 0001 0100 1011
DEFINATION
A
a11 , , a1n
a21 , , a2n
am1 , , amn
Aij
421
0011 0010 1010 1101 0001 0100 1011
TYPES
VECTOR
MATRIX
SCALAR
MATRIX
SQUARE
MATRIX
SYMMETRIC
MATRIX
DIAGONAL
MATRIX
IDENTITY
MATRIX
421
0011 0010 1010 1101 0001 0100 1011
VECTOR MATRIX
A vector is a special type of matrix that has
only one row (called a row vector) or one
column (column vector ) .Below A is a
column while B is a row vector.
3
1
4
A 1745B
421
0011 0010 1010 1101 0001 0100 1011
SCALAR MATRIX
A diagonal matrix in which all of
the diagonal elements are equal is
called Scalar Matrix
500
050
005
B
421
0011 0010 1010 1101 0001 0100 1011
SQUARE MATRIX
312
163
745
A
A matrix with same number of
rows and columns is a square
matrix
53
47
B
421
0011 0010 1010 1101 0001 0100 1011
SYMMETRIC MATRIX
421
0011 0010 1010 1101 0001 0100 1011
DIAGONAL MATRIX
A diagonal matrix is a matrix is a
symmetric matrix where all the off diagonal
elements are 0 .
Ex:-
500
050
005
B
421
0011 0010 1010 1101 0001 0100 1011
IDENTITY MATRIX
An identity matrix is a diagonal matrix with
1 & only 1 on diagonal .The diagonal matrix
is always denoted as I
100
010
001
A
421
0011 0010 1010 1101 0001 0100 1011
Addition
Subtraction
Multiplication
Inverse
Matrix Operations
421
0011 0010 1010 1101 0001 0100 1011
Two matrices can be added or subtracted
if and only if the number of rows and
columns are same.
ADDITION AND SUBTRACTION
421
0011 0010 1010 1101 0001 0100 1011
A
a11 a12
a21 a22
B
b11 b12
b21 b22
22222121
12121111
baba
baba
BA
If and
then
22222121
12121111
baba
baba
BA
also
421
0011 0010 1010 1101 0001 0100 1011
EXAMPLE
1
4
2
3
5
8
6
7
+ =
6
12
8
10
A B+ = C
421
0011 0010 1010 1101 0001 0100 1011
EXAMPLE
1
4
2
3
5
8
6
7
- =
4
4
4
4
B A- = C
421
0011 0010 1010 1101 0001 0100 1011
Multiplication
Matrices A and B can be multiplied if the no. of
coloum of first matrix is same as the no. of rows of
the second
[r x c] and [s x d]
c = s
i.e.
421
0011 0010 1010 1101 0001 0100 1011
Step I
1
4
2
3
5
8
6
7
x =
A Bx =
421
0011 0010 1010 1101 0001 0100 1011
Step II
1
4
2
3
5
8
6
7
x =
A Bx = C
(5x1)
C11 = A11 x B11k=1
n
421
0011 0010 1010 1101 0001 0100 1011
Step III
1
4
2
3
5
8
6
7
x =
A Bx = C
(5x1)+(6x3)
C11 = A12 x B21k=2
n
421
0011 0010 1010 1101 0001 0100 1011
Step IV
1
4
2
3
5
8
6
7
x =
A Bx = C
23 (5x2)+(6x4)
C12 = A1k x Bk2k=1
n
421
0011 0010 1010 1101 0001 0100 1011
Step V
1
4
2
3
5
8
6
7
x =
A Bx = C
23
(7x1)+(8x3)
34
C21
= A2k
x Bk1k=1
n
421
0011 0010 1010 1101 0001 0100 1011
Step VI
1
4
2
3
5
8
6
7
x =
A Bx = C
23 34
(7x2)+(8x4)31
C22
= A2k
x Bk2k=1
n
421
0011 0010 1010 1101 0001 0100 1011
Result
1
4
2
3
5
8
6
7
x =
A Bx = C
23 34
31 46
m x n n x p m x p
Inverse
421
0011 0010 1010 1101 0001 0100 1011
Find
determinant
Swap the
diagonal
elements(a11
and a22)
Change signs
of non-
diagonal
elements(a12
and a21)
Divide each
element by
determinant
INVERSE OF A 2x2 MATRIX
421
0011 0010 1010 1101 0001 0100 1011
Step I--Find the determinant
A determinant is a scalar number which is calculated from a matrix.
This number can determine whether a set of linear equations are
solvable, in other words whether the matrix can be inverted.
• Find the determinant
= (a11 x a22) - (a21 x a12)
For
det(A) = (2x3) – (1x5) = 1
2
3
5
1
=A
421
0011 0010 1010 1101 0001 0100 1011
Step II--Swap elements a11 and a22
• Swap elements a11 and a22
Thus
becomes
2
3
5
1
=A
3
2
5
1
421
0011 0010 1010 1101 0001 0100 1011
Step III--Change sign of a12 and a21
• Change sign of a12 and a21
Thus
becomes
3
2
5
1
=A
3
2
-5
-1
421
0011 0010 1010 1101 0001 0100 1011
Step IV
• Divide every element by the determinant
Thus
becomes
(no change as the determinant was 1)
3
2
-5
-1
=A
3
2
-5
-1
421
0011 0010 1010 1101 0001 0100 1011
Step V– Check the result
• Check results with A
-1
A = I
Thus
equals
3
2
-5
-1
x
1
1
0
0
2
3
5
1
Final matrix ppt

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Final matrix ppt

  • 2. 421 0011 0010 1010 1101 0001 0100 1011 DEFINATION A a11 , , a1n a21 , , a2n am1 , , amn Aij
  • 3. 421 0011 0010 1010 1101 0001 0100 1011 TYPES VECTOR MATRIX SCALAR MATRIX SQUARE MATRIX SYMMETRIC MATRIX DIAGONAL MATRIX IDENTITY MATRIX
  • 4. 421 0011 0010 1010 1101 0001 0100 1011 VECTOR MATRIX A vector is a special type of matrix that has only one row (called a row vector) or one column (column vector ) .Below A is a column while B is a row vector. 3 1 4 A 1745B
  • 5. 421 0011 0010 1010 1101 0001 0100 1011 SCALAR MATRIX A diagonal matrix in which all of the diagonal elements are equal is called Scalar Matrix 500 050 005 B
  • 6. 421 0011 0010 1010 1101 0001 0100 1011 SQUARE MATRIX 312 163 745 A A matrix with same number of rows and columns is a square matrix 53 47 B
  • 7. 421 0011 0010 1010 1101 0001 0100 1011 SYMMETRIC MATRIX
  • 8. 421 0011 0010 1010 1101 0001 0100 1011 DIAGONAL MATRIX A diagonal matrix is a matrix is a symmetric matrix where all the off diagonal elements are 0 . Ex:- 500 050 005 B
  • 9. 421 0011 0010 1010 1101 0001 0100 1011 IDENTITY MATRIX An identity matrix is a diagonal matrix with 1 & only 1 on diagonal .The diagonal matrix is always denoted as I 100 010 001 A
  • 10. 421 0011 0010 1010 1101 0001 0100 1011 Addition Subtraction Multiplication Inverse Matrix Operations
  • 11. 421 0011 0010 1010 1101 0001 0100 1011 Two matrices can be added or subtracted if and only if the number of rows and columns are same. ADDITION AND SUBTRACTION
  • 12. 421 0011 0010 1010 1101 0001 0100 1011 A a11 a12 a21 a22 B b11 b12 b21 b22 22222121 12121111 baba baba BA If and then 22222121 12121111 baba baba BA also
  • 13. 421 0011 0010 1010 1101 0001 0100 1011 EXAMPLE 1 4 2 3 5 8 6 7 + = 6 12 8 10 A B+ = C
  • 14. 421 0011 0010 1010 1101 0001 0100 1011 EXAMPLE 1 4 2 3 5 8 6 7 - = 4 4 4 4 B A- = C
  • 15. 421 0011 0010 1010 1101 0001 0100 1011 Multiplication Matrices A and B can be multiplied if the no. of coloum of first matrix is same as the no. of rows of the second [r x c] and [s x d] c = s i.e.
  • 16. 421 0011 0010 1010 1101 0001 0100 1011 Step I 1 4 2 3 5 8 6 7 x = A Bx =
  • 17. 421 0011 0010 1010 1101 0001 0100 1011 Step II 1 4 2 3 5 8 6 7 x = A Bx = C (5x1) C11 = A11 x B11k=1 n
  • 18. 421 0011 0010 1010 1101 0001 0100 1011 Step III 1 4 2 3 5 8 6 7 x = A Bx = C (5x1)+(6x3) C11 = A12 x B21k=2 n
  • 19. 421 0011 0010 1010 1101 0001 0100 1011 Step IV 1 4 2 3 5 8 6 7 x = A Bx = C 23 (5x2)+(6x4) C12 = A1k x Bk2k=1 n
  • 20. 421 0011 0010 1010 1101 0001 0100 1011 Step V 1 4 2 3 5 8 6 7 x = A Bx = C 23 (7x1)+(8x3) 34 C21 = A2k x Bk1k=1 n
  • 21. 421 0011 0010 1010 1101 0001 0100 1011 Step VI 1 4 2 3 5 8 6 7 x = A Bx = C 23 34 (7x2)+(8x4)31 C22 = A2k x Bk2k=1 n
  • 22. 421 0011 0010 1010 1101 0001 0100 1011 Result 1 4 2 3 5 8 6 7 x = A Bx = C 23 34 31 46 m x n n x p m x p
  • 24. 421 0011 0010 1010 1101 0001 0100 1011 Find determinant Swap the diagonal elements(a11 and a22) Change signs of non- diagonal elements(a12 and a21) Divide each element by determinant INVERSE OF A 2x2 MATRIX
  • 25. 421 0011 0010 1010 1101 0001 0100 1011 Step I--Find the determinant A determinant is a scalar number which is calculated from a matrix. This number can determine whether a set of linear equations are solvable, in other words whether the matrix can be inverted. • Find the determinant = (a11 x a22) - (a21 x a12) For det(A) = (2x3) – (1x5) = 1 2 3 5 1 =A
  • 26. 421 0011 0010 1010 1101 0001 0100 1011 Step II--Swap elements a11 and a22 • Swap elements a11 and a22 Thus becomes 2 3 5 1 =A 3 2 5 1
  • 27. 421 0011 0010 1010 1101 0001 0100 1011 Step III--Change sign of a12 and a21 • Change sign of a12 and a21 Thus becomes 3 2 5 1 =A 3 2 -5 -1
  • 28. 421 0011 0010 1010 1101 0001 0100 1011 Step IV • Divide every element by the determinant Thus becomes (no change as the determinant was 1) 3 2 -5 -1 =A 3 2 -5 -1
  • 29. 421 0011 0010 1010 1101 0001 0100 1011 Step V– Check the result • Check results with A -1 A = I Thus equals 3 2 -5 -1 x 1 1 0 0 2 3 5 1