Compute the inverse of a matrix using NumPy

In linear algebra, the inverse of a matrix is a fundamental concept. It is the matrix that, when multiplied with the original matrix, yields the identity matrix. In Python, the NumPy library provides a straightforward way to compute the inverse of a matrix using the numpy.linalg.inv() function.

Understanding Matrix Inversion

For a square matrix A, its inverse A-1 satisfies the condition:

A @ A-1 = A-1 @ A = I

where I is the identity matrix of the same size as A.

Prerequisites

Before computing the inverse, ensure that:

• The matrix is square (same number of rows and columns).
• The matrix is non-singular, meaning its determinant is not zero. A singular matrix does not have an inverse.

Using NumPy to Compute the Inverse

NumPy's numpy.linalg.inv() function computes the inverse of a square matrix. Here's how you can use it:

import numpy as np

matrix = np.array([[1., 2.], [3., 4.]])
inverse_matrix = np.linalg.inv(matrix)
print(inverse_matrix)

Output:

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

Handling Singular Matrices

If the matrix is singular (determinant is zero), attempting to compute its inverse will raise a LinAlgError. To handle this, you can check the determinant before attempting the inversion:

if np.linalg.det(matrix) != 0:
    inverse_matrix = np.linalg.inv(matrix)
else:
    print("Matrix is singular and cannot be inverted.")

Computing the Inverse of Multiple Matrices

NumPy allows you to compute the inverse of multiple matrices simultaneously. Here's an example:

matrices = np.array([[[1., 2.], [3., 4.]], [[2., 3.], [5., 6.]]])
inverses = np.linalg.inv(matrices)
print(inverses)

Output:

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

 [[-3.   2. ]
   [ 2.5 -1.5]]]

Conclusion

Computing the inverse of a matrix is a common operation in various fields such as physics, engineering, and computer science. With NumPy's numpy.linalg.inv() function, this task becomes straightforward. Always ensure that the matrix is square and non-singular before attempting to compute its inverse to avoid errors.