How to inverse a matrix using NumPy

In this article, we will see NumPy Inverse Matrix in Python before that we will try to understand the concept of it. The inverse of a matrix is just a reciprocal of the matrix as we do in normal arithmetic for a single number which is used to solve the equations to find the value of unknown variables. The inverse of a matrix is that matrix which when multiplied with the original matrix will give an identity matrix. 

The inverse of a matrix exists only if the matrix is non-singular i.e., the determinant should not be 0. Using determinant and adjoint, we can easily find the inverse of a square matrix using the below formula,

if det(A) != 0
    A-1 = adj(A)/det(A)
else
    "Inverse doesn't exist"  

Matrix Equation:

where,

A^-1: The inverse of matrix A

x: The unknown variable column

B: The solution matrix

Inverse Matrix using NumPy

Python provides a very easy method to calculate the inverse of a matrix. The function numpy.linalg.inv() is available in the NumPy module and is used to compute the inverse matrix in Python.

Syntax: numpy.linalg.inv(a)

Parameters:

• a: Matrix to be inverted

Returns:  Inverse of the matrix a.

Example 1: In this example, we will create a 3 by 3 NumPy array matrix and then convert it into an inverse matrix using the np.linalg.inv() function.

Output:

[[ 0.17647059 -0.00326797 -0.02287582]
 [ 0.05882353 -0.13071895  0.08496732]
 [-0.11764706  0.1503268   0.05228758]]

Example 2: In this example, we will create a 4 by 4 NumPy array matrix and then convert it using np.linalg.inv() function into an inverse Matrix in Python.

Output:

[[ 0.13368984  0.10695187  0.02139037 -0.09090909]
 [-0.00229183  0.02673797  0.14820474 -0.12987013]
 [-0.12987013  0.18181818  0.06493506 -0.02597403]
 [ 0.11000764 -0.28342246 -0.11382735  0.23376623]]

Example 3: In this example, we will create multiple NumPy array matrices and then convert them into their inverse matrices using np.linalg.inv() function.

Output:

[[[-2.    1.  ]
  [ 1.5  -0.5 ]]

 [[-1.25  0.75]
  [ 0.75 -0.25]]]