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IFIP21

The singular value decomposition of a matrix

is defined as

$\begin{displaymath} A = U \Sigma V^H \end{displaymath}$

(1)

where

and

are both matrices with orthonormal columns, $\{\cdot\}^H$ indicates a complex-conjugate transpose, and $\Sigma$ is a diagonal matrix with singular values

$\sigma_1 > \sigma_2 > \cdots > \sigma_n \geq 0$

along the main diagonal. Eq. (1) is just one of the many matrix decompositions that exists for matrix

-- DoaitseSwierstra - 04 Apr 2009