What is matrix decomposition in NumPy?

What is matrix decomposition in NumPy?

Matrix decomposition is a technique to factor a matrix into multiple matrices, simplifying operations like solving linear equations.

Can you give me an example of how to perform Singular Value Decomposition (SVD) in NumPy?

Sure! You can use numpy.linalg.svd() to perform SVD. Here's a quick example:

import numpy as np

A = np.array([[1, 2], [3, 4]])
U, S, VT = np.linalg.svd(A)
print(U, S, VT)

What are the applications of matrix decomposition?

Matrix decomposition is used in various applications like image compression, recommendation systems, and solving systems of equations.

Are there other types of matrix decompositions I should know about?

Yes, other common types include LU decomposition, QR decomposition, and Cholesky decomposition, each serving different purposes.