s = svd(A) 
   [U,S,V]=svd(A)

Calculates the single value decomposition of a matrix

For an m-by-n matrix A with m >= n, the singular value decomposition is an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and an n-by-n orthogonal matrix V so that A = U*S*V'. The singular values, sigma[k] = S[k][k], are ordered so that sigma[0] >= sigma[1] >= ... >= sigma[n-1]. The singular value decompostion always exists, so the constructor will never fail. The matrix condition number and the effective numerical rank can be computed from this decomposition.

SVD([1,2,3;4,5,6;7,8,9]) = [16.848; 1.068; 0]

lu, qr