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How to compute the inverse of a square matrix in PyTorch

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  • Last Updated : 13 Jun, 2022

In this article, we are going to cover how to compute the inverse of a square matrix in PyTorch

torch.linalg.inv() method

we can compute the inverse of the matrix by using torch.linalg.inv() method. It accepts a square matrix and a batch of the square matrices as input. If the input is a batch of the square matrices then the output will also have the same batch dimensions. This method returns the inverse matrix.

Syntax: torch.linalg.inv(M)

Parameters:

  • M – This is our square matrix or a batch of square matrix.

Returns: it will returns the inverse matrix.

Example 1:

In this example, we will understand how to compute the inverse of a 4×4 square matrix in PyTorch.

Python3




# import required library
import torch
 
# define a 4x4 square matrix
mat = torch.tensor([[ 1.00, -0.000, -0.000.00],
        [ 4.001.0002.000.00],
        [ -9.00-3.001.008.00],
        [ -2.00, -0.00, -0.001.00]])
print("Input Matrix M: \n", mat)
 
# compute the inverse of matrix
Mat_inv = torch.linalg.inv(mat)
 
# display result
print("\nInverse Matrix: \n", Mat_inv)

Output:

How to compute the inverse of a square matrix in PyTorch

 

Example 2:

In this example, we will compute the inverse of a batch of square matrices in PyTorch.

Python3




# import required library
import torch
 
# define a batch of two 3x3 square matrix
mat = torch.tensor([[[1.0, 2.0, 3.0], [4.0, 1.0, 6.0],
                     [1.0, 1.0, 1.0]],
                    [[2.0, 2.0, 3.0], [4.0, 5.0, 6.0],
                     [2.0, 2.0, 2.0]]])
print("Input Matrix M: \n", mat)
 
# compute the inverse of matrix
Mat_inv = torch.linalg.inv(mat)
 
# display result
print("\nInverse Matrix: \n", Mat_inv)

Output:

How to compute the inverse of a square matrix in PyTorch

 


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