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Introduction
Introduction of Matrix

A matrix is an ordered rectangular array of numbers or functions. The numbers or functions are called the elements or the entries of the matrix. matrix

Rows and Columns matrix

The horizontal lines of elements are said to constitute, rows of the matrix and the vertical lines of elements are said to constitute, columns of the matrix. column matrix

Order of a matrix

A matrix having m rows and n columns is called a matrix of order m × n or simply m × n matrix (read as an m by n matrix). In general form, an m x n matrix has the rectangular array : order of matrix or A = [aᵢⱼ]m × n, 1≤ i ≤ m, 1≤ j ≤ n i, j ∈ N Thus the ith row consists of the elements aᵢ₁, aᵢ₂, aᵢ₃,..., aᵢn, while the jth column consists of the elements a₁ⱼ, a₂ⱼ, a₃ⱼ,..., amⱼ,

Types of matrix

There have many types of matrix following below :
(i) Row matrix
(ii) Column matrix
(iii) Square matrix
(iv) Diagonal matrix
(v) Scalar matrix
(vi) Identity matrix
(vii) Zero matrix

Row matrix

A matrix is said to be a column matrix if it has only one column. For Example, row matrix In general, B = [bᵢⱼ] 1 × n is a row matrix of order 1 × n.

Column matrix

A matrix is said to be a column matrix if it has only one column. For Example, column matrix In general, A = [aᵢⱼ] m × 1 is a column matrix of order m × 1.

Square matrix

A matrix in which the number of rows are equal to the number of columns, is said to be a square matrix. Thus an m × n matrix is said to be a square matrix if m = n and is known as a square matrix of order ‘n’. For Example, square matrix In general, A = [aᵢⱼ] m × m is a square matrix of order m.

Diagonal matrix

A square matrix B = [bᵢⱼ] m × m is said to be a diagonal matrix if all its non diagonal elements are zero, that is a matrix B = [bᵢⱼ] m × m is said to be a diagonal matrix if bᵢⱼ = 0, when i ≠ j. For Example, diagonal matrix

Scalar matrix

A diagonal matrix is said to be a scalar matrix if its diagonal elements are equal, that is, a square matrix B = [bᵢⱼ] n × n is said to be a scalar matrix if bᵢⱼ = 0, when i ≠ j bᵢⱼ = k, when i = j, for some constant k. For Example, scalar matrix

Identity matrix

A square matrix in which elements in the diagonal are all 1 and rest are all zero is called an identity matrix. For Example, identity matrix

Zero matrix

A matrix is said to be zero matrix or null matrix if all its elements are zero. We denote zero matrix by O. We denote the identity matrix of order n by In. When order is clear from the context, we simply write it as I. For Example, zero matrix

Equality of matrices

Two matrices A = [aij] and B = [bij] are said to be equal if
(i) they are of the same order
(ii) each element of A is equal to the corresponding element of B, that is aᵢⱼ = bᵢⱼ for all i and j. For example, equality matrix

Operations on Matrices

There have some operations on matrice following bwlow :
(i) Addition of matrices
(ii) Difference of matrices
(iii) Scalar multiplication of matrices
(iv) Negative of a matrices
(v) Multiplication of matices

Addition of matrices

The sum of two or more matrices is called addition of matrices. For Example, addition of matrix In general, if A = [aᵢⱼ] and B = [bᵢⱼ] are two matrices of the same order, say m × n. Then, the sum of the two matrices A and B is defined as a matrix C = [cᵢⱼ]m × n, where cᵢⱼ = aᵢⱼ + bᵢⱼ, for all possible values of i and j.

Difference of matrices

The diffrence between two matrices is called difference of matrices. For Example, difference of matrix In general, if A = [aᵢⱼ] and B = [bᵢⱼ] are two matrices of the same order, say m × n. Then, the difference of the two matrices A and B is defined as a matrix C = [cᵢⱼ]m × n, where cᵢⱼ = aᵢⱼ - bᵢⱼ, for all possible values of i and j.

Scalar multiplication of matrices

A matrix in which multiply by any scalar value is called scalar multiplication of matrices. For Example, scalar multiplication of matrix In general, we may define multiplication of a matrix by a scalar as follows: if A = [aᵢⱼ] m × n is a matrix and k is a scalar, then kA is another matrix which is obtained by multiplying each element of A by the scalar k.
In other words, kA = k [aᵢⱼ] m × n = [k (aᵢⱼ)] m × n, that is, (i, j)th element of kA is kaᵢⱼ for all possible values of i and j.

Negative of a matices

The negative of a matrix is denoted by –A. We define –A = (– 1) A. For example, negative matrix

Multiplication of matices

The product of two matrices A and B is defined if the number of columns of A is equal to the number of rows of B. Let A = [aᵢⱼ] be an m × n matrix and B = [bjk] be an n × p matrix. For example, multiplication of matrix

Transpose of a Matrix

If A = [aᵢⱼ] be an m × n matrix, then the matrix obtained by interchanging the rows and columns of A is called the transpose of A. Transpose of the matrix A is denoted by A′ or (AT). In other words, if A = [aᵢⱼ]m × n, then A′ = [aⱼᵢ]n × m. For example, transpose of matrix

Symmetric matrices

A square matrix A = [aᵢⱼ] is said to be symmetric if A′ = A, that is, [aᵢⱼ] = [aⱼᵢ] for all possible values of i and j. For Example, symmetic matrix

Skew Symmetric Matrices

A square matrix A = [aᵢⱼ] is said to be skew symmetric matrix if A′ = – A, that is, [aⱼᵢ] = -[aᵢⱼ] for all possible values of i and j. For Example, skew symmetric matrix This means that all the diagonal elements of a skew symmetric matrix are zero.

Elementary Operation (Transformation) of a Matrix

There are six operations (transformations) on a matrix, three of which are due to rows and three due to columns, which are known as elementary operations or transformations. transformation of matrix Rule 1. The interchange of any two rows or two columns.
Rule 2. The multiplication of the elements of any row or column by a non zero number.
Rule 3. The addition to the elements of any row or column, the corresponding elements of any other row or column multiplied by any non zero number.

Invertible Matrices

If A is a square matrix of order m, and if there exists another square matrix B of the same order m, such that AB = BA = I, then B is called the inverse matrix of A and it is denoted by A-1. In that case A is said to be invertible. For example, invertible matrix

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