Numpy.linalg.inv - Python NumPy linalg.inv() Function

NumPy linalg.inv() Function:

Numpy linalg inv: The (multiplicative) inverse of a matrix is calculated using the linalg.inv() function of the NumPy module.

The inverse of a matrix is such that if it is multiplied by the original matrix, the result is an identity matrix.

Given a square matrix ‘a’, It returns the matrix ainv satisfying:

dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])

Syntax:

numpy.linalg.inv(a)

Parameters

a: This is required. It is a square matrix for which the inverse is to be calculated.

Return Value:

The (Multiplicative) inverse of the matrix ‘a’ is returned.

NOTE: If “a” is not a square matrix or inversion fails, the LinAlgError exception is thrown.

NumPy linalg.inv() Function in Python

Example1:

Approach:

Import numpy module using the import keyword.
Pass some random matrix values as an argument to the array() function to create an array.
Store it in a variable.
Print the above-given array.
Pass the given array as an argument to the linalg.inv() function of numpy module to calculate the inverse of the given array(matrix).
Store it in another variable.
Print the Inverse of the given array(matrix).
The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Pass some random matrix values as an argument to the array() function to
# create an array. 
# Store it in a variable.
gvn_arry = np.array([[5,6],[4, 3]])
# Print the above given array.
print("The above given array is:")
print(gvn_arry)
# Pass the given array as an argument to the linalg.inv() function of numpy module 
# to calculate the inverse of the given array(matrix).
# Store it in another variable.
rslt_inverse = np.linalg.inv(gvn_arry)
# Print the Inverse of the given array(matrix).
print("The Inverse of the given array(matrix) =\n", rslt_inverse)

Output:

The above given array is:
[[5 6]
 [4 3]]
The Inverse of the given array(matrix) =
[[-0.33333333 0.66666667]
 [ 0.44444444 -0.55555556]]

Example2:

numpy.linalg.inv: The Inverse of a stack of matrices can also be calculated using this function.

Approach:

Import numpy module using the import keyword.
Pass some random stack of matrices as the arguments to the array() function to create an array.
Store it in a variable.
Print the above-given array.
Pass the given array as an argument to the linalg.inv() function of numpy module to calculate the inverse of the given array.
Store it in another variable.
Print the inverse of the given array(stack of matrices).
The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Pass some random stack of matrices as the arguments to the array() function to
# create an array. 
# Store it in a variable.
gvn_arry = np.array([ [[5, 1], [6, 4]], [[2, 8], [4, 1]], [[1, 5], [2, 6]] ])
# Print the above given array.
print("The above given array is:")
print(gvn_arry)
print()
# Pass the given array as an argument to the linalg.inv() function of numpy module 
# to calculate the Inverse of the given array.
# Store it in another variable.
rslt_inverse = np.linalg.inv(gvn_arry)
# Print the Inverse of the given array(stack of matrices).
print("The Inverse of the given array(stack of matrices) =\n", rslt_inverse)

Output:

The above given array is:
[[[5 1]
  [6 4]]

[[2 8]
 [4 1]]

[[1 5]
 [2 6]]]

The Inverse of the given array(stack of matrices) =
[[[ 0.28571429 -0.07142857]
 [-0.42857143 0.35714286]]

[[-0.03333333 0.26666667]
 [ 0.13333333 -0.06666667]]

[[-1.5 1.25 ]
 [ 0.5 -0.25 ]]]

Numpy.linalg.inv – Python NumPy linalg.inv() Function

NumPy linalg.inv() Function in Python

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