svd(G)

NAME
svd - singular value decomposition

CALLING SEQUENCE
s=svd(X)
[U,S,V]=svd(X)
[U,S,V,rk]=svd(X [,tol])

PARAMETERS

X   : a real or complex matrix

s   : real vector (singular values)

S   : real diagonal matrix (singular values)

U,V : orthogonal or unitary square matrices (singular vectors).

tol : real number

DESCRIPTION
produces a diagonal matrix S , of the same
dimension as X and with nonnegative diagonal elements in
decreasing order, and unitary matrices U and V so that
X = U*S*V'.

by itself, returns a vector s containing the singular values.

gives in addition rk, the numerical rank of X i.e. the number of
singular values larger than tol.

The default value of tol is the same as in rank.

SEE ALSO
rank, qr, colcomp, rowcomp.