Lære Identity and Inverse Matrices

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To build a strong foundation in linear algebra, you need to understand two powerful concepts: the identity matrix and the inverse matrix. The identity matrix is a special square matrix where all the entries on the main diagonal (from top left to bottom right) are 1, and all other entries are 0. For an $n x n$ matrix, the identity matrix is usually denoted as $I_n$ . Its most important property is that multiplying any matrix $A$ (of compatible size) by the identity matrix leaves $A$ unchanged:

AI = IA = A

The inverse matrix is another key concept. If a square matrix $A$ has an inverse, denoted as $A^{-1}$ , it means there exists a matrix such that $AA^{-1} = A^{-1}A = I$ . Not all matrices have inverses – only those that are invertible or non-singular. A matrix is invertible if there exists another matrix that, when multiplied with the original, yields the identity matrix. In practical terms, the inverse matrix "undoes" the transformation applied by the original matrix, which is especially useful when solving matrix equations like $AX = B$ for $X$ .


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import numpy as np

# Create a 3x3 identity matrix
I3 = np.identity(3)
print("3x3 Identity matrix:\n", I3)

# Define a 2x2 matrix
A = np.array([[4, 7],
              [2, 6]])

# Invert the matrix using numpy's built-in function
A_inv = np.linalg.inv(A)
print("Inverse of A:\n", A_inv)

You can quickly create identity matrices in Python using numpy.identity. To find the inverse of a matrix, use numpy.linalg.inv, which handles the inversion for you. For example, numpy.identity(3) creates a 3x3 identity matrix, and numpy.linalg.inv(A) computes the inverse of a square matrix A if it is invertible. Remember, a matrix must be square and have a nonzero determinant to be invertible. If the determinant is zero, numpy.linalg.inv will raise an error, indicating that the matrix does not have an inverse. These built-in functions make it easy to work with identity and inverse matrices when solving equations like $AX = B$ .

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