How do you define an inverse of a matrix in python?

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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

    How do you define an inverse of a matrix in python?

    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

    import numpy as np

    A = np.array([[6, 1, 1],

                  [4, -2, 5],

                  [2, 8, 7]])

    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

    import numpy as np

    A = np.array([[6, 1, 1, 3],

                  [4, -2, 5, 1],

                  [2, 8, 7, 6],

                  [3, 1, 9, 7]])

    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

    import numpy as np

    A = np.array([[[1., 2.], [3., 4.]],

                  [[1, 3], [3, 5]]])

    print(np.linalg.inv(A))

    Output:

    [[[-2.    1.  ]
      [ 1.5  -0.5 ]]
    
     [[-1.25  0.75]
      [ 0.75 -0.25]]]
    

    What is the inverse of a matrix in Python?

    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.

    How do you write inverse in Python?

    The acos() function in Python returns the inverse cosine of a number. To be more specific, it returns the inverse cosine of a number in the radians.

    How do you take the inverse of a NumPy matrix?

    We use numpy. linalg. inv() function to calculate the inverse of a matrix. The inverse of a matrix is such that if it is multiplied by the original matrix, it results in identity matrix.