CHAPTER 6 derived a number of algorithms for computing the eigenvalues and eigen-vectors of matrices A ∈ Rn×n. Using this machinery, we complete our initial discussion of numerical linear algebra by deriving and making use of one final matrix factorization that exists for any matrix A ∈ Rm×n, even if it is not symmetric or square: the singular value decomposition (SVD).