np.linalg.svd

np.linalg.svd is a function provided by the NumPy library in Python. It stands for Singular Value Decomposition and is used for matrix factorization. SVD decomposes a matrix into three separate matrices, U, S, and V.

Here's an example of how to use np.linalg.svd:

import numpy as np

# Create a matrix
A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Perform SVD
U, S, V = np.linalg.svd(A)

# U: Left singular vectors
# S: Singular values
# V: Right singular vectors

print("U:")
print(U)
print("S:")
print(S)
print("V:")
print(V)

The output will be the U, S, and V matrices. U contains the left singular vectors, S contains the singular values, and V contains the right singular vectors.