import numpy as np

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

# numpy.linalg.det()
matrix = np.array([[1, 2], [3, 4]])
determinant = np.linalg.det(matrix)
print(determinant)

# numpy.linalg.eig()
matrix = np.array([[1, 2], [3, 4]])
eigenvalues, eigenvectors = np.linalg.eig(matrix)
print("Eigenvalues:", eigenvalues)
print("Eigenvectors:", eigenvectors)

# numpy.linalg.svd()
matrix = np.array([[1, 2], [3, 4], [5, 6]])
U, S, VT = np.linalg.svd(matrix)
print("U:", U)
print("S:", S)
print("VT:", VT)

# numpy.linalg.norm()
matrix = np.array([[1, 2], [3, 4]])
matrix_norm = np.linalg.norm(matrix)
print(matrix_norm)