# svd

Singular value decomposition

### s = svd(X)

• It returns the singular values of X.
• X should be a matrix, which should not be empty, and should contain no Inf or Nan values.
• If X has m rows and n columns, s is a vector of min(m, n) elements sorted in descending order.

### [U, S, V] = svd(X)

• U, S and V are such that U*S*V' is equal to X.
• If X is m-by-n, then U is m-by-m, S m-by-n, and V n-by-n.
• The first min(m, n) diagonal elements of S contains the singular values.
• U*U' and V*V' are identity arrays.