  svd
  Builtin Function

      SSyynnooppssiiss
        Singular Value Decomposition

      SSyynnttaaxx
        svd ( _A )

        svd ( _A, _T_Y_P_E )

      DDeessccrriippttiioonn
        Computes the singular values of the input matrix _A, as well as
        the right and left singular vectors in various forms. Where:

          A = U * diag (sigma) * Vt

     The output is a list containing the three afore-mentioned objects
     (_u, _s_i_g_m_a, _v_t).  Various forms of the right and left singular
     vectors can be computed, depending upon the value of the second,
     optional, string argument _T_Y_P_E:

        SS  A minimal version of U, and Vt are returned.  This is the
           default.

        AA  The full U, and Vt are returned.

        NN  U and Vt are not computed, empty U and Vt are returned.

     The LAPACK subroutine DGESVD, or ZGESVD is used to perform the
     computation.

     Example:

     > A = [0.96, 1.72; 2.28, 0.96];
     > Asvd = svd(A)
        sigma        u            vt
     > Asvd.vt
      matrix columns 1 thru 2
             -0.8        -0.6
              0.6        -0.8
     > Asvd.u
      matrix columns 1 thru 2
             -0.6        -0.8
             -0.8         0.6
     > Asvd.sigma
      vector elements 1 thru 2
                3           1
     > check = Asvd.u * diag(Asvd.sigma) * Asvd.vt
      check =
      matrix columns 1 thru 2
             0.96        1.72
             2.28        0.96

