Matrices
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Elementary Transformation
Rules for Elementary Transformation :
In Elementary Transformation, you can
1) Interchange any two rows or columns
2) Add or subtract any two rows or columns
3) Divide or multiply any row or column with a constant (and NOT by any other row or column)
Example :
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Rank of a Matrix
Rank :
Rank r means there exists at least one r-rowed square matrix with non-vanishing determinant.
every (r+1) or more rowed matrices have vanishing determinants.
Also, rank of a matrix is the largest order of a non-zero minor of the matrix(i.e. determinant not equal to 0).
Minor Part of a matrix always square ordered
Example: Let A is a matrix of order Then 4 minors are possible by dropping one row each time:
a) Leaving
b) Leaving
c) Leaving
d) Leaving
Properties :
1) Rank of two equivalent matrices is always same.
2) Rank of and is same.
3) Rank of a null matrix is same.
4) For a rectangular matrix of order m n, rank of .
5) For a -square matrix, if rank = , then i.e. is non-singular.
6) For any square matrix, if rank < , then i.e. is singular.
Sample Problem :
Find the rank of the matrix
Solution:
Echelon Form: The approach will be: (a) reduce the diagonal elements to 1 and the rest of the elements to 0 with the help of Elementary Transformations, (b) after that, the number of non-zero rows will be the rank of the matrix. So starting with first column, reduce first element of
to 1 and with the help of that element reduce all the rest of the elements of that row(or column) to 0.
Now, as the rest of the elements are 0, move on to the next column and perform same steps Since, it has 2 non-zero rows, Rank
Normal Form:
Any matrix can be reduced to following 4 forms:
Sample Problem:
Find normal form of matrix
Solution:
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Solution of System of Linear Equations
Linear Equations:
Equations in which degree of the variables is 1.
A set of Linear Equations can be written in matrix form as and where
So to find solution, first we take augmented matrix of and i.e. [ ] which is matrix and written in one matrix seperated by a colon (:).
Then we find rank of and [ ] after which arise two situations:
1) : again two situations:
a) : (where n is number of variables) the equations have Unique solution
b) : the equations have Infinite solutions
in both the situations, solution is found out through equation :
2) : no solutions
Sample Problem 1:
Check consistency and solve:
Solution :
Rank , Rank Equations are Consistent
Since (number of variables) Unique solution
For solution,
Sample Problem 2 :
Test the consistency of following equations and solve them if consistent:
Solution:
Sample Problem 3:
Find values of
and for which given system of equations has (a) Infinite solution, (b) No solutions, (c) Unique solutionSolution:
Case 1: For infinite solutions
, so
So
Case 2: No solutions
Case 3: Unique solution
can have any values -
Eigen Values & Eigen Vectors
To find eigen values and eigen vectors, take a square matrix
find value of [ ], where is a scalar quantity Put i.e. find determinant the equation formed is called Characteristic Equation on solving, we get values of called as Eigen Values put ( for each value of , here is called Eigen Vector the final equation is
Properties:
(a) Any square matrix and its transpose have same eigen values
(b) The sum of eigen values of a matrix is equal to the trace of the matrix (trace is sum of diagonal elements)
(c) The product of eigen values is equal to the determinant of the matrix
Sample Problem 1:
Find Eigen Values and Eigen Vectors -
Solution:
Now, after using Hit & Trial method, we get one value , so for the other two values, we divide the equation by , after which we get a quadratic equation:
Eigen Vector, For : By Cross-Multiplication method:
Eigen Vector -
For :
By Cross-Multiplication method:
Eigen Vector -
For : By Cross-Multiplication method:
Eigen Vector -
So,
Sample Problem 2:
Find Eigen Vectors and Eigen Vectors for matrix
Solution:
By Hit & Trial method, we get one value , so for the other two values, we divide the equation by , after which we get a quadratic equation:
Eigen Vector, For : By Cross-Multiplication method:
Eigen Vector -
For : By Cross-Multiplication method:
which is not possible, so we take one equation of the three,
Put z = 0 Eigen Vector:
For again: Put y = 0 Eigen Vector - So, -
Cayley-Hamilton Theorem
Cayley-Hamilton Theorem:
It states that every square matrix satisfies its own Characteristic Equation.
Sample Problem :
For the matrix given
,
(a) Verify Cayley-Hamilton Theorem
(b) Hence find
(c) Find
(d) FindSolution:
Characteristic Equation,
(a) Putting in place of in the Characteristic Polynomial Hence verified
(b) Multiplying on both sides (c)
Dividing the given expression by to factorize the expression: (d) Multiplying on both sides: -
Diagonilization of Matrices
Diagonilization of a matrix
is the process of reduction of to a Diagonal form such that where is called Modal matrix.
Sample Problem:
Find a matrix
which transforms the given matrix into diagonal matrix:Solution:
Characteristic Equation for the matrix is:
For :
Taking first equation, it is satisfied by,
So, Eigen vector =
Taking first equation again, it is satisfied by,
So, Eigen vector =
For By Cross-Multiplication method:
So, Eigen vector =
Therefore, , (using Cayley-Hamilton Theorem)
Now,