# Mathematics | Matrix Introduction

A matrix represents a collection of numbers arranged in an order of rows and columns. It is necessary to enclose the elements of a matrix in parentheses or brackets.

A matrix with 9 elements is shown below.

This Matrix [M] has 3 rows and 3 columns. Each element of matrix [M] can be referred to by its row and column number. For example, a_{23 }= 6

**Order of a Matrix:**

The order of a matrix is defined in terms of its number of rows and columns.

Order of a matrix = No. of rows ×No. of columns

Therefore Matrix [M] is a matrix of order 3 × 3.

**Transpose of a Matrix :**

The transpose [M]^{T} of an m x n matrix [M] is the n x m matrix obtained by interchanging the rows and columns of [M].

if A = [a_{ij}] mxn , then A^{T} = [b_{ij}] nxm where b_{ij} = a_{ji}

**Properties of the transpose of a matrix:**

- (A
^{T})^{T}= A - (A+B)
^{T}= A^{T}+ B^{T} - (AB)
^{T}= B^{T}A^{T}

**Singular and Nonsingular Matrix:**

- Singular Matrix: A square matrix is said to be a singular matrix if its determinant is zero i.e. |A|=0
- Nonsingular Matrix: A square matrix is said to be a non-singular matrix if its determinant is non-zero.

**Properties of Matrix addition and multiplication:**

- A + B = B + A (Commutative)
- (A + B) + C = A + (B + C) (Associative)
- AB ? BA (Not Commutative)
- (AB) C = A (BC) (Associative)
- A (B+C) = AB + AC (Distributive)

## Types of Matrices:

**Square Matrix:**A square Matrix has as many rows as it has columns. i.e. no of rows = no of columns.**Symmetric matrix:**A square matrix is said to be symmetric if the transpose of the original matrix is equal to its original matrix. i.e. (A^{T}) = A.**Skew-symmetric:**A skew-symmetric (or antisymmetric or antimetric[1]) matrix is a square matrix whose transpose equals its negative.i.e. (A^{T}) = -A.**Diagonal Matrix:**A diagonal matrix is a square matrix in which the entries outside the main diagonal are all zero. The term usually refers to square diagonal matrices.**Identity Matrix:**A square matrix in which all the elements of the principal diagonal are ones and all other elements are zeros. Identity matrix is denoted as I.**Orthogonal Matrix:**A matrix is said to be orthogonal if AA^{T}= A^{T}A = I**Idempotent Matrix:**A matrix is said to be idempotent if A^{2}= A**Involuntary Matrix:**A matrix is said to be Involuntary if A^{2}= I.**Zero or Null Matrix:**A matrix is said to zero or null matrix if all its elements are zero**Upper Triangular Matrix:**A square matrix in which all the elements below the diagonal are zero is known as the upper triangular matrix**Lower Triangular Matrix:**A square matrix in which all the elements above the diagonal are zero is known as the lower triangular matrix

**Note:** Every Square Matrix can uniquely be expressed as the sum of a symmetric matrix and skew-symmetric matrix. A = 1/2 (A^{T} + A) + 1/2 (A – A^{T}).

**Determinant of a matrix :**

The determinant of a matrix is a number associated with that square matrix. The determinant of a matrix can only be calculated for a square matrix. It is represented by |A|.

**Example: **

Input:2 X 2 MatrixThen, the determinant is -> |A| = ad – bc

**Adjoint of a square matrix:** The adjoint of a matrix A is the transpose of the cofactor matrix of A

**Properties of Adjoint:**

- A(Adj A) = (Adj A) A = |A| I
_{n} - Adj(AB) = (Adj B) . (Adj A)
- |Adj A| = |A|
^{n-1} - Adj(kA) = k
^{n-1}Adj(A) - |adj(adj(A))| = |A| ^ (n-1) ^ 2
- adj(adj(A)) = |A| ^ (n-2) * A
- If A = [L,M,N] then adj(A) = [MN, LN, LM]
- adj(I) = I

Where, “n = number of rows = number of columns”

**The inverse of a square matrix: **

Here |A| should not be equal to zero, which means matrix A should be non-singular.

**Properties of the inverse:**

- (A
^{-1})^{-1}= A - (AB)
^{-1}= B^{-1}A^{-1} - only a non singular square matrix can have an inverse.

**Where should we use the inverse matrix?**

If you have a set of simultaneous equations:

**7x + 2y + z = 21****3y – z = 5 ****-3x + 4y – 2x = -1 **

As we know when AX = B, then X = A^{-1}B so we can calculate the inverse of A and by multiplying it by B, we can get the values of x, y, and z.

**Trace of a matrix:**

The trace of a matrix is denoted as tr(A) which is used only for square matrix and equals the sum of the diagonal elements of the matrix. Remember trace of a matrix is also equal to the sum of the eigenvalue of the matrix. For example:

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