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How to inverse a matrix using NumPy
  • Last Updated : 18 Aug, 2020
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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 as an identity matrix. The inverse of a matrix exists only if the matrix is non-singular i.e., determinant should not be 0. Using determinant and adjoint, we can easily find the inverse of a square matrix using below formula,

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

Matrix Equation

=>Ax = B\\ =>A^{-1}Ax = A^{-1}B\\ =>x = A^{-1}B

where,

A-1: The inverse of matrix A

x: The unknown variable column



B: The solution matrix

Inverse of a Matrix using NumPy

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

Syntax:

numpy.linalg.inv(a)

Parameters:

a: Matrix to be inverted

Returns: 

Inverse of the matrix a.



Example 1:

Python




# Python program to inverse
# a matrix using numpy
  
# Import required package
import numpy as np
  
# Taking a 3 * 3 matrix
A = np.array([[6, 1, 1],
              [4, -2, 5],
              [2, 8, 7]])
  
# Calculating the inverse of the matrix
print(np.linalg.inv(A))

Output:

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

Example 2:

Python




# Python program to inverse
# a matrix using numpy
  
# Import required package
import numpy as np
  
# Taking a 4 * 4 matrix
A = np.array([[6, 1, 1, 3],
              [4, -2, 5, 1],
              [2, 8, 7, 6],
              [3, 1, 9, 7]])
  
# Calculating the inverse of the matrix
print(np.linalg.inv(A))

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:

Python




# Python program to inverse
# a matrix using numpy
  
# Import required package
import numpy as np
  
# Inverses of several matrices can
# be computed at once
A = np.array([[[1., 2.], [3., 4.]],
              [[1, 3], [3, 5]]])
  
# Calculating the inverse of the matrix
print(np.linalg.inv(A))

Output:

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

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

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