A matrix is a rectangular arrangement of numbers (real or complex) which may be represented as

matrix is enclosed by [ ] or ( ) or | | | |

Compact form the above matrix is represented by [a_{ij}]_{m x n} or A = [a_{ij}].

- Element of a Matrix The numbers a
_{11}, a_{12}… etc., in the above matrix are known as the element of the matrix, generally represented as a_{ij}, which denotes element in ith row and jth column. - Order of a Matrix In above matrix has m rows and n columns, then A is of order m x n.

**Types of Matrices**

- Row Matrix A matrix having only one row and any number of columns is called a row matrix.
- Column Matrix A matrix having only one column and any number of rows is called column matrix.
- Rectangular Matrix A matrix of order m x n, such that m ≠ n, is called rectangular matrix.
- Horizontal Matrix A matrix in which the number of rows is less than the number of columns, is called a horizontal matrix.
- Vertical Matrix A matrix in which the number of rows is greater than the number of columns, is called a vertical matrix.
- Null/Zero Matrix A matrix of any order, having all its elements are zero, is called a null/zero matrix. i.e., a
_{ij}= 0, ∀ i, j - Square Matrix A matrix of order m x n, such that m = n, is called square matrix.
- Diagonal Matrix A square matrix A = [a
_{ij}]_{m x n}, is called a diagonal matrix, if all the elements except those in the leading diagonals are zero, i.e., a_{ij}= 0 for i ≠ j. It can be represented as

A = diag[a_{11}a_{22}… a_{nn}] - Scalar Matrix A square matrix in which every non-diagonal element is zero and all diagonal elements are equal, is called scalar matrix. i.e., in scalar matrix

a_{ij}= 0, for i ≠ j and a_{ij}= k, for i = j - Unit/Identity Matrix A square matrix, in which every non-diagonal element is zero and every diagonal element is 1, is called, unit matrix or an identity matrix.
- Upper Triangular Matrix A square matrix A = a[
_{ij}]_{n x n}is called a upper triangular matrix, if a[_{ij}], = 0, ∀ i > j. - Lower Triangular Matrix A square matrix A = a[
_{ij}]_{n x n}is called a lower triangular matrix, if a[_{ij}], = 0, ∀ i < j. - Submatrix A matrix which is obtained from a given matrix by deleting any number of rows or columns or both is called a submatrix of the given matrix.
- Equal Matrices Two matrices A and B are said to be equal, if both having same order and corresponding elements of the matrices are equal.
- Principal Diagonal of a Matrix In a square matrix, the diagonal from the first element of the first row to the last element of the last row is called the principal diagonal of a matrix.
- Singular Matrix A square matrix A is said to be singular matrix, if determinant of A denoted by det (A) or |A| is zero, i.e., |A|= 0, otherwise it is a non-singular matrix.

**Algebra of Matrices**

**1. Addition of Matrices**

Let A and B be two matrices each of order m x n. Then, the sum of matrices A + B is defined only if matrices A and B are of same order.

If A = [a_{ij}]_{m x n} , A = [a_{ij}]_{m x n}

Then, A + B = [a_{ij} + b_{ij}]_{m x n}

**Properties of Addition of Matrices** If A, B and C are three matrices of order m x n, then

**Commutative Law**A + B = B + A**Associative Law**(A + B) + C = A + (B + C)**Existence of Additive Identity**A zero matrix (0) of order m x n (same as of A), is additive identity, if

A + 0 = A = 0 + A**Existence of Additive Inverse**If A is a square matrix, then the matrix (- A) is called additive inverse, if

A + ( – A) = 0 = (- A) + A**Cancellation Law**

A + B = A + C ⇒ B = C (left cancellation law)

B + A = C + A ⇒ B = C (right cancellation law)

**2. Subtraction of Matrices**

Let A and B be two matrices of the same order, then subtraction of matrices, A – B, is defined as

A – B = [a_{ij} – b_{ij}]_{n x n},

where A = [a_{ij}]_{m x n}, B = [b_{ij}]_{m x n}

**3. Multiplication of a Matrix by a Scalar**

Let A = [a_{ij}]_{m x n} be a matrix and k be any scalar. Then, the matrix obtained by multiplying each element of A by k is called the scalar multiple of A by k and is denoted by kA, given as

kA= [ka_{ij}]_{m x n}

**Properties of Scalar Multiplication If A and B are matrices of order m x n, then**

- k(A + B) = kA + kB
- (k
_{1}+ k_{2})A = k_{1}A + k_{2}A - k
_{1}k_{2}A = k_{1}(k_{2}A) = k_{2}(k_{1}A) - (- k)A = – (kA) = k( – A)

**4. Multiplication of Matrices**

Let A = [a_{ij}]_{m x n} and B = [b_{ij}]_{n x p} are two matrices such that the number of columns of A is equal to the number of rows of B, then multiplication of A and B is denoted by AB, is given by

where c_{ij} is the element of matrix C and C = AB

**Properties of Multiplication of Matrices**

- Commutative Law Generally AB ≠ BA
- Associative Law (AB)C = A(BC)
- Existence of multiplicative Identity A.I = A = I.A,

I is called multiplicative Identity. - Distributive Law A(B + C) = AB + AC
- Cancellation Law If A is non-singular matrix, then

AB = AC ⇒ B = C (left cancellation law)

BA = CA ⇒B = C (right cancellation law) - AB = 0, does not necessarily imply that A = 0 or B = 0 or both A and B = 0

**Important Points to be Remembered**

(i) If A and B are square matrices of the same order, say n, then both the product AB and BA are defined and each is a square matrix of order n.

(ii) In the matrix product AB, the matrix A is called premultiplier (prefactor) and B is called postmultiplier (postfactor).

(iii) The rule of multiplication of matrices is row column wise (or → ↓ wise) the first row of AB is obtained by multiplying the first row of A with first, second, third,… columns of B respectively; similarly second row of A with first, second, third, … columns of B, respectively and so on.

**Positive Integral Powers of a Square Matrix**

Let A be a square matrix. Then, we can define

- A
^{n + 1}= A^{n}. A, where n ∈ N. - A
^{m}. A^{n}= A^{m + n} - (A
^{m})^{n}= A^{mn}, ∀ m, n ∈ N

**Matrix Polynomial**

Let f(x)= a_{0}x^{n} + a_{1}x^{n – 1} -1 + a_{2}x^{n – 2} + … + a_{n}. Then

f(A)= a_{0}A^{n} + a_{1}A^{n – 2} + … + a_{n}I_{n}

is called the matrix polynomial.

**Transpose of a Matrix**

Let A = [a_{ij}]_{m x n}, be a matrix of order m x n. Then, the n x m matrix obtained by interchanging the rows and columns of A is called the transpose of A and is denoted by A’ or A^{T}.

A’ = A^{T} = [a_{ij}]_{n x m}

**Properties of Transpose**

- (A’)’ = A
- (A + B)’ = A’ + B’
- (AB)’ = B’A’
- (KA)’ = kA’
- (A
^{N})’ = (A’)^{N} - (ABC)’ = C’ B’ A’

**Symmetric and Skew-Symmetric Matrices**

- A square matrix A = [a
_{ij}]_{n x n}, is said to be symmetric, if A’ = A.

i.e., a_{ij}= a_{ji}, ∀i and j. - A square matrix A is said to be skew-symmetric matrices, if i.e., aij = — aji, di and j

**Properties of Symmetric and Skew-Symmetric Matrices**

- Elements of principal diagonals of a skew-symmetric matrix are all zero. i.e., a
_{ii}= — a_{ii}2_{ii}= 0 or a_{ii}= 0, for all values of i. - If A is a square matrix, then

(a) A + A’ is symmetric.

(b) A — A’ is skew-symmetric matrix. - If A and B are two symmetric (or skew-symmetric) matrices of same order, then A + B is also symmetric (or skew-symmetric).
- If A is symmetric (or skew-symmetric), then kA (k is a scalar) is also symmetric for skew-symmetric matrix.
- If A and B are symmetric matrices of the same order, then the product AB is symmetric, iff BA = AB.
- Every square matrix can be expressed uniquely as the sum of a symmetric and a skew-symmetric matrix.
- The matrix B’ AB is symmetric or skew-symmetric according as A is symmetric or skew-symmetric matrix.
- All positive integral powers of a symmetric matrix are symmetric.
- All positive odd integral powers of a skew-symmetric matrix are skew-symmetric and positive even integral powers of a skew-symmetric are symmetric matrix.
- If A and B are symmetric matrices of the same order, then

(a) AB – BA is a skew-symmetric and

(b) AB + BA is symmetric. - For a square matrix A, AA’ and A’ A are symmetric matrix.

**Trace of a Matrix**

The sum of the diagonal elements of a square matrix A is called the trace of A, denoted by trace (A) or tr (A).

**Properties of Trace of a Matrix**

- Trace (A ± B)= Trace (A) ± Trace (B)
- Trace (kA)= k Trace (A)
- Trace (A’ ) = Trace (A)
- Trace (I
_{n})= n - Trace (0) = 0
- Trace (AB) ≠ Trace (A) x Trace (B)
- Trace (AA’) ≥ 0

**Conjugate of a Matrix**

The matrix obtained from a matrix A containing complex number as its elements, on replacing its elements by the corresponding conjugate complex number is called conjugate of A and is denoted by A.

**Properties of Conjugate of a Matrix**

If A is a matrix of order m x n, then

**Transpose Conjugate of a Matrix**

The transpose of the conjugate of a matrix A is called transpose conjugate of A and is denoted by A^{0} or A^{*}.

i.e., (A’) = A‘ = A^{0} or A^{*}

**Properties of Transpose Conjugate of a Matrix**

(i) (A^{*})^{*} = A

(ii) (A + B)^{*} = A^{*} + B^{*}

(iii) (kA)^{*} = kA^{*}

(iv) (AB)^{*} = B^{*}A^{*}

(V) (An)^{*} = (A^{*})n

**Some Special Types of Matrices**

**1. Orthogonal Matrix**

A square matrix of order n is said to be orthogonal, if AA’ = I_{n} = A’A Properties of Orthogonal Matrix

(i) If A is orthogonal matrix, then A’ is also orthogonal matrix.

(ii) For any two orthogonal matrices A and B, AB and BA is also an orthogonal matrix.

(iii) If A is an orthogonal matrix, A^{-1} is also orthogonal matrix.

**2. ldempotent Matrix**

A square matrix A is said to be idempotent, if A^{2} = A.

**Properties of Idempotent Matrix**

**(i)** If A and B are two idempotent matrices, then

- AB is idempotent, if AB = BA.
- A + B is an idempotent matrix, iff

AB = BA = 0 - AB = A and BA = B, then A
^{2}= A, B^{2}= B

**(ii)**

- If A is an idempotent matrix and A + B = I, then B is an idempotent and AB = BA= 0.
- Diagonal (1, 1, 1, …,1) is an idempotent matrix.
- If I
_{1}, I_{2}and I_{3}are direction cosines, then

is an idempotent as |Δ|^{2} = 1.

A square matrix A is said to be involutory, if A^{2} = I

**4. Nilpotent Matrix**

A square matrix A is said to be nilpotent matrix, if there exists a positive integer m such that A^{2} = 0. If m is the least positive integer such that A^{m} = 0, then m is called the index of the nilpotent matrix A.

**5. Unitary Matrix **

A square matrix A is said to be unitary, if A‘A = I

**Hermitian Matrix**

A square matrix A is said to be hermitian matrix, if A = A^{*} or

= a_{ij}, for a_{ji} only.

**Properties of Hermitian Matrix**

- If A is hermitian matrix, then kA is also hermitian matrix for any non-zero real number k.
- If A and B are hermitian matrices of same order, then λ
_{1}A + λB, also hermitian for any non-zero real number λ_{1}, and λ. - If A is any square matrix, then AA* and A* A are also hermitian.
- If A and B are hermitian, then AB is also hermitian, iff AB = BA
- If A is a hermitian matrix, then A is also hermitian.
- If A and B are hermitian matrix of same order, then AB + BA is also hermitian.
- If A is a square matrix, then A + A* is also hermitian,
- Any square matrix can be uniquely expressed as A + iB, where A and B are hermitian matrices.

**Skew-Hermitian Matrix**

A square matrix A is said to be skew-hermitian if A* = – A or a_{ji} for every i and j.

**Properties of Skew-Hermitian Matrix**

- If A is skew-hermitian matrix, then kA is skew-hermitian matrix, where k is any non-zero real number.
- If A and B are skew-hermitian matrix of same order, then λ
_{1}A + λ_{2}B is also skew-hermitian for any real number λ_{1}and λ_{2}. - If A and B are hermitian matrices of same order, then AB — BA is skew-hermitian.
- If A is any square matrix, then A — A
^{*}is a skew-hermitian matrix. - Every square matrix can be uniquely expressed as the sum of a hermitian and a skew-hermitian matrices.
- If A is a skew-hermitian matrix, then A is a hermitian matrix.
- If A is a skew-hermitian matrix, then A is also skew-hermitian matrix.

**Adjoint of a Square Matrix**

Let A[a_{ij}]_{m x n} be a square matrix of order n and let C_{ij} be the cofactor of a_{ij} in the determinant |A| , then the adjoint of A, denoted by adj (A), is defined as the transpose of the matrix, formed by the cofactors of the matrix.

**Properties of Adjoint of a Square Matrix**

If A and B are square matrices of order n, then

- A (adj A) = (adj A) A = |A|I
- adj (A’) = (adj A)’
- adj (AB) = (adj B) (adj A)
- adj (kA) = k
^{n – 1}(adj A), k ∈ R - adj (A
^{m}) = (adj A)^{m} - adj (adj A) = |A|
^{n – 2}A, A is a non-singular matrix. - |adj A| =|A|
^{n – 1},A is a non-singular matrix. - |adj (adj A)| =|A|
^{(n – 1)2}A is a non-singular matrix. - Adjoint of a diagonal matrix is a diagonal matrix.

**Inverse of a Square Matrix**

Let A be a square matrix of order n, then a square matrix B, such that AB = BA = I, is called inverse of A, denoted by A^{-1}.

i.e.,

or AA^{-1} = A^{-1}A = 1

**Properties of Inverse of a Square Matrix**

- Square matrix A is invertible if and only if |A| ≠ 0
- (A
^{-1})^{-1}= A - (A’)
^{-1}= (A^{-1})’ - (AB)
^{-1}= B^{-1}A^{-1}

In general (A_{1}A_{1}A_{1}… A_{n})^{-1}= A_{n}^{-1}A_{n – 1}^{-1}… A_{3}^{-1}A_{2}^{-1}A_{1}^{-1} - If a non-singular square matrix A is symmetric, then A
^{-1}is also symmetric. - |A
^{-1}| = |A|^{-1} - AA
^{-1}= A^{-1}A = I - (A
^{k})^{-1}= (A^{-1})A^{k}k ∈ N

**Elementary Transformation**

Any one of the following operations on a matrix is called an elementary transformation.

- Interchanging any two rows (or columns), denoted by R
_{i}←→R_{j}or C_{i}←→C_{j} - Multiplication of the element of any row (or column) by a non-zero quantity and denoted by

R_{i}→ kR_{i}or C_{i}→ kC_{j} - Addition of constant multiple of the elements of any row to the corresponding element of any other row, denoted by

R_{i}→ R_{i}+ kR_{j}or C_{i}→ C_{i}+ kC_{j}

**Equivalent Matrix**

- Two matrices A and B are said to be equivalent, if one can be obtained from the other by a sequence of elementary transformation.
- The symbol≈ is used for equivalence.

**Rank of a Matrix**

A positive integer r is said to be the rank of a non-zero matrix A, if

- there exists at least one minor in A of order r which is not zero.
- every minor in A of order greater than r is zero, rank of a matrix A is denoted by ρ(A) = r.

**Properties of Rank of a Matrix**

- The rank of a null matrix is zero ie, ρ(0) = 0
- If In is an identity matrix of order n, then ρ(I
_{n}) = n. - (a) If a matrix A does’t possess any minor of order r, then ρ(A) ≥ r.

(b) If at least one minor of order r of the matrix is not equal to zero, then ρ(A) ≤ r. - If every (r + 1)th order minor of A is zero, then any higher order – minor will also be zero.
- If A is of order n, then for a non-singular matrix A, ρ(A) = n
- ρ(A’)= ρ(A)
- ρ(A
^{*}) = ρ(A) - ρ(A + B) &LE; ρ(A) + ρ(B)
- If A and B are two matrices such that the product AB is defined, then rank (AB) cannot exceed the rank of the either matrix.
- If A and B are square matrix of same order and ρ(A) = ρ(B) = n, then p(AB)= n
- Every skew-symmetric matrix,of odd order has rank less than its order.
- Elementary operations do not change the rank of a matrix.

**Echelon Form of a Matrix**

A non-zero matrix A is said to be in Echelon form, if A satisfies the following conditions

- All the non-zero rows of A, if any precede the zero rows.
- The number of zeros preceding the first non-zero element in a row is less than the number of such zeros in the successive row.
- The first non-zero element in a row is unity.
- The number of non-zero rows of a matrix given in the Echelon form is its rank.

**Homogeneous and Non-Homogeneous System of Linear Equations**

A system of equations AX = B, is called a homogeneous system if B = 0 and if B ≠ 0, then it is called a non-homogeneous system of equations.

**Solution of System of Linear Equations**

The values of the variables satisfying all the linear equations in the system, is called solution of system of linear equations.

**1 . Solution of System of Equations by Matrix Method**

**(i) Non-Homogeneous System of Equations** Let AX = B be a system of n linear equations in n variables.

- If |A| ≠ 0, then the system of equations is consistent and has a unique solution given by X = A
^{-1}B. - If |A| = 0 and (adj A)B = 0, then the system of equations is consistent and has infinitely many solutions.
- If |A| = 0 and (adj A) B ≠ 0, then the system of equations is inconsistent i.e., having no solution

**(ii) Homogeneous System of Equations** Let AX = 0 is a system of n linear equations in n variables.

- If I |A| ≠ 0, then it has only solution X = 0, is called the trivial solution.
- If I |A| = 0, then the system has infinitely many solutions, called non-trivial solution.

**2. Solution of System of Equations by Rank Method**

**(i) Non-Homogeneous System of Equations** Let AX = B, be a system of n linear equations in n variables, then

**Step I**Write the augmented matrix [A:B]**Step II**Reduce the augmented matrix to Echelon form using elementary row-transformation.**Step III**Determine the rank of coefficient matrix A and augmented matrix [A:B] by counting the number of non-zero rows in A and [A:B].

**Important Results**

- If ρ(A) ≠ ρ(AB), then the system of equations is inconsistent.
- If ρ(A) =ρ(AB) = the number of unknowns, then the system of equations is consistent and has a unique solution.
- If ρ(A) = ρ(AB) < the number of unknowns, then the system of equations is consistent and has infinitely many solutions.

**(ii) Homogeneous System of Equations**

- If AX = 0, be a homogeneous system of linear equations then, If ρ(A) = number of unknown, then AX = 0, have a non-trivial solution, i.e., X = 0.
- If ρ(A) < number of unknowns, then AX = 0, have a non-trivial solution, with infinitely many solutions.

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