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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.