How to Fix numpy.linalg.linalgerror: singular matrix

The numpy.linalg.linalgerror: singular matrix error occurs in Python when you attempt to invert a singular matrix whose determinant is zero that cannot be inverted.

To fix the LinAlgError: Singular matrix error, create a matrix that is not singular, and the determinant is not 0.0.

Python code that generates numpy.linalg.linalgerror: singular matrix

import numpy as np

#create 2x2 matrix
main_matrix = np.array([[21., 21.], [21., 21.]]) 

# attempt to print the inverse of matrix
print(np.linalg.inv(main_matrix))

Output

You can see that we got the “LinAlgError: Singular matrix” error because the main_matrix is not invertible. It has the same value in all entries, making it a singular matrix with zero determinant.

The numpy.linalg.LinAlgError is an exception class for general purposes, derived from Python’s exception.

When a Linear Algebra-related condition prevents continued accurate execution of the function, this exception class is raised programmatically in linalg functions.

A matrix is invertible only if its determinant is non-zero.

If the determinant is zero, the matrix is said to be singular and has no inverse.

You can use the np.linalg.det() function from NumPy to calculate the determinant of a given matrix before you try to invert it.

import numpy as np 

# create 2x2 matrix 
main_matrix = np.array([[21., 21.], [21., 21.]]) 

# Printing the determinant
print(np.linalg.det(main_matrix))

Output

0.0

You can see that the determinant of the matrix is zero, which explains why we get into the error.

How to Fix numpy.linalg.linalgerror: singular matrix error

Code that fixes the error

import numpy as np 

# create 2x2 matrix 
main_matrix = np.array([[21., 19.], [19., 21.]]) 

# Printing the inverse of matrix
print(np.linalg.inv(main_matrix))

Output

[[ 0.2625 -0.2375]
 [-0.2375  0.2625]]

You can see that the code worked without errors and will print the inverse of the main_matrix.

In this case, the main_matrix is a 2×2 matrix with a non-zero determinant, which is invertible.

The np.linalg.inv() function from the NumPy library is used to find the inverse of the main_matrix.

I hope this solution will resolve your error.