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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
$\sigma_1 &gt; \sigma_2 &gt; \cdots &gt; \sigma_n \geq 0$
along the main diagonal. Eq. (1) is just one of the many matrix decompositions that exists for matrix .

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Topic revision: r2 - 2016-02-19 - TWikiGuest