Mastering Numerical Computing with NumPy

Matrix decomposition

Matrix decomposition, or factorization methods, involves calculating the constituents of a matrix so that they can be used to simplify more demanding matrix operations. In practice, this means breaking the matrix you have into more than one matrix so that, when you calculate the product of these smaller matrices, you get your original matrix back. Some examples of matrix decomposition methods are singular-value decomposition (SVD), eigenvalue decomposition, Cholesky decomposition, lower–upper (LU), and QR decomposition.