UNIT5 | unit 5 matrices

MATHS I

UNIT-5

Matrices

5.1 Rank of a matrix by elementary transformation and its normal form.

The rank of a matrix is the largest order of the non-zero minor of the matrix (we search at least one nonzero minor to find the rank of the matrix).

A matrix A is said to have the rank r if and only if there exist at least one nonzero minor of the given matrix of order r and every minor greater than order r is zero.

This is denoted by .

Normal form of a matrix:

Every non-singular matrix A of rank r can be converted by applying sequence of the elementary transformation in the form of

Is called the normal form of the matrix A.

Also, the rank of a matrix can be calculated by converting it into normal form then the order of identity matrix obtained is the rank of the given matrix.

Example1: Reduce the following matrix into normal form and find its rank,

Let A =

Apply we get

Apply

Hence the rank of matrix A is 2 i.e. .

Example2: Reduce the following matrix into normal form and find its rank,

Let A =

Apply and

Apply

Hence the rank of the matrix A is 2 i.e. .

Example3: Reduce the following matrix into normal form and find its rank,

Let A =

Apply

Apply and

Apply

Hence the rank of matrix A is 2 i.e. .

5.2 System of linear equations.

The rank of matrix remains unchanged by elementary transformations. i.e. from a matrix. A we get another matrix B by using some elementary transformation. Then

Rank of A = Rank of B

2. Equivalent matrices:

The matrix B is obtained from a matrix A by a sequence of a finite no. Of elementary transformations is said to be equivalent to A. And we write.

Normal form or canonical form:

Every mxn matrix of rank r can be reduced to the form

By a finite sequence of elementary transformation. This form is called normal form or the first canonical form of the matrix A.

Ex. 1

Reduce the following matrix to normal form of Hence find it’s rank,

Solution:

We have,

Apply

Rank of A = 1

Ex. 2

Find the rank of the matrix

Solution:

We have,

Apply R12

Rank of A = 3

Ex. 3

Find the rank of the following matrices by reducing it to the normal form.

Solution:

Apply C14

H.W.

Reduce the follo9wing matrices in to the normal form and hence find their ranks.

Reduction of a matrix a to normal form PAQ.

If A is a matrix of rank r, then there exist a non – singular matrices P & Q such that PAQ is in normal form.

i.e.

To obtained the matrices P and Q we use the following procedure.

Working rule:-

If A is a mxn matrix, write A = Im A In.
Apply row transformations on A on l.h.s. And the same row transformations on the prefactor Im.
Apply column transformations on A on l.h.s and the column transformations on the postfactor In.

So that A on the l.h.s. Reduces to normal form.

Example 1

If Find Two

Matrices P and Q such that PAQ is in normal form.

Solution:

Here A is a square matrix of order 3 x 3. Hence we write,

A = I3 A.I3

i.e.

Example 2

Find a non – singular matrices p and Q such that P A Q is in normal form where

Solution:

Here A is a matrix of order 3 x 4. Hence we write A as,

i.e.

5.3 Symmetric, skew symmetric and orthogonal matrices.

Symmetric matrix: In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally, because equal matrices have equal dimensions, only square matrices can be symmetric. The entries of a symmetric matrix are symmetric with respect to the main diagonal. So, if aᵢⱼ denotes the entry in the i-th row and j-th column then for all indices i and j. Every square diagonal matrix is symmetric, since all off-diagonal elements are zero.

Example1: Let us test whether the given matrices are symmetric or not i.e., we check for ,

A =

(1) A =

Now

A =

Hence the given matric symmetric

Example2: let A be a real symmetric matrix whose diagonal entries are all positive real numbers.

Is this true for all of the diagonal entries of the inverse matrix A-1 are also positive? If so, prove it. Otherwise give a counter example

Solution: The statement is false, hence we give a counter example

Let us consider the following 22 matrix

A =

The matrix A satisfies the required conditions, that is A is symmetric and its diagonal entries are positive.

The determinant det(A) = (1)(1)-(2)(2) = -3 and the inverse of A is given by

A-1= =

By the formula for the inverse matrix for 22 matrices.

This shows that the diagonal entries of the inverse matric A-1 are negative.

Skew-symmetric: A skew-symmetric matrix is a square matrix that is equal to the negative of its own transpose. Anti-symmetric matrices are commonly called as skew-symmetric matrices.

Example3:Let A and B be nn skew-matrices.Namely AT = -A and BT = -B

(a) Prove that A+B is skew-symmetric.

(b) Prove that cA is skew-symmetric for any scalar c.

Solution: (a) (A+B)T = AT + BT = (-A)+(-B) = -(A+B)

Hence A+B is skew symmetric.

(b) (cA)T = c.AT =c(-A) = -cA

Thus, cA is skew-symmetric.

(c)Let P be an mn matrix. Prove that PT AP is skew-symmetric.

Using the properties, we get,

(PT AP)T = PTAT(PT)T = PTATp

= PT (-A) P = - (PT AP)

Thus (PT AP) is skew-symmetric.

Orthogonal matrix: An orthogonal matrix is the real specialization of a unitary matrix, and thus always a normal matrix. Although we consider only real matrices here, the definition can be used for matrices with entries from any field.

Suppose A is a square matrix with real elements and of n x n order and AT is the transpose of A. Then according to the definition, if, AT = A-1 is satisfied, then,

A AT = I

Where ‘I’ is the identity matrix, A-1 is the inverse of matrix A, and ‘n’ denotes the number of rows and columns.

Example4: prove Q= is an orthogonal matrix

Solution: Given Q =

So, QT = …..(1)

Now,we have to prove QT = Q-1

Now we find Q-1

Q-1 =

Q-1 = …(2)

Now, compare (1) and (2) we get QT = Q-1

Therefore, Q is an orthogonal matrix.

5.4 Eigen values and eigen vectors.

In linear algebra, an eigenvector (/ˈaɪɡənˌvɛktər/) or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted by λ, is the factor by which the eigenvector is scaled. Geometrically, an eigenvector, corresponding to a real nonzero eigenvalue, points in a direction in which it is stretched by the transformation and the eigenvalue is the factor by which it is stretched.

In order to find eigenvalues of a matrix, following steps are to followed:

Step 1: Make sure the given matrix A is a square matrix. Also, determine the identity matrix I of the same order.

Step 2: Estimate the matrixA– λ I, where λ is a scalar quantity.

Step 3: Find the determinant of matrix A–λ I and equate it to zero.

Step 4: From the equation thus obtained, calculate all the possible values of λ which are the required eigenvalues of matrix A.

Example1: Find the eigenvalues for the following matrix?

Solution: Given

Hence the required eigenvalues are 6 and 1

Example 2: Find the eigenvalues of a

Solution: Let A=

Then,

These are required eigenvalues.

5.5 Diagonalization of matrices.

A square {\displaystyle n\times n}n×n matrix {\displaystyle A}A over a field {\displaystyle F}F is called diagonalizable or nondefective if there exists an invertible matrix {\displaystyle P} P such that {\displaystyle P^{-1}AP} P-1AP is a diagonal matrix. Formally,

{\displaystyle A\in F^{n\times n}{\text{ diagonalizable}}\iff \exists \,P,P^{-1}\in F^{n\times n}:\;P^{-1}\!AP{\text{ diagonal}}} $A\in F^{n\times n}{\text{ diagonalizable}}\iff \exists \,P,P^{-1}\in F^{n\times n}:\;P^{-1}\!AP{\text{ diagonal}}$ A diagonalizable :P-1AP diagonal

Let A be the n×n matrix that you want to diagonalize (if possible).

Find the characteristic polynomial p(t)of A.
Find eigenvalues λ of the matrix A and their algebraic multiplicities from the characteristic polynomial p(t).
For each eigenvalue λ of A, find a basis of the eigenspace Eλ.
If there is an eigenvalue λ such that the geometric multiplicity of λ, dim(Eλ), is less than the algebraic multiplicity of λ, then the matrix A is not diagonalizable. If not, A is diagonalizable, and proceed to the next step.
If we combine all basis vectors for all eigenspaces, we obtained n linearly independent eigenvectors ,,….
Define the non-singular matrix S= [,,….].

6. Define the diagonal matrix D, whose (i, i)-entry is the eigenvalue λ such that the ii-th column vector vi is in the eigenspace Eλ.

7. Then the matrix A is diagonalized as

S -1 AS=D.

Example1: Let us consider the following 3 ×3 matrix

A =

Solution: We want to diagonalize the matrix if possible.

Step 1: Find the characteristic polynomial

The Charcterstic polynomial p(t) of A is

Using the cofactor expansion, we get

Step2: From the characteristic polynomial obtained step1, we see that eigenvalues are

And

Step2: Find the eigenspaces

Let us first find the eigenspaces corresponding to the eigenvalue

By definition, is the null space of the matrix

By elementary row operations.

Hence if (A-I)x=0 for x

Therefore, we have

From this, we see that the set

Is a basis for the eigenvalues

Thus, the dimension of , which is the geometric multiplicity of

Similarly, we find a basis of the eigenspaces for the eigenvalue We have

By elementary row operations.

Then if (A-2I) x=0 for x

Therefore we obtain

From this we see that the set

and the geometric multiplicity is 1.

Since for both eigenvalues, the geometric multiplicity is equal to the algebraic multiplicity, the matrix A is not defective, and hence diagonalizable.

Step4: Determine linearly independent eigenvectors

From step3, the vectors

Are linearly independent eigenvectors.

Step5: Define the invertible matrix S

Define the matrix S=

And the matrix S is non-singular (since the column vectors are linearly independent).

Step 6: Define the diagonal matrix D

Define the diagonal matrix

Note that (1,1) entry of D is 1 because the first column vector

Of S is in the eigenvalues that is is an eigenvector corresponding to eigenvalue

Similarly, the (2,2) entry of D is 1 because the second column of S is in .

The (3,3)entry of D is 2 because the third column vector of S is in

(The order you arrange the vector to form S does not matter but once you made S, then the order of the diagonal entries is determined by S, that is , the order of eigenvectors on S)

Step7: Finish the diagonalization

Finally, we can diagonalize the matrix A as

Where

(Here you don’t have to find the inverse matrix unless you are asked to do so).

5.6 Cayley-Hamilton theorem and orthogonal transformation.

In linear algebra, the Cayley–Hamilton theorem (termed after the mathematicians Arthur Cayley and William Rowan Hamilton) says that every square matrix over a commutative ring (for instance the real or complex field) satisfies its own characteristic equation. If A is a provided as n×n matrix and In is the n×n identity matrix, then the distinctive polynomial of A is articulated as:

P(x) = det (xIn – A)

Where the determinant operation is ‘det’ and for the scalar element of the base ring, the variable is taken as x. As the entries of the matrix are (linear or constant) polynomials in x, the determinant is also an n-th order monic polynomial in x.

Example 1:

Verify the Cayley-Hamilton theorem for A=