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I was faced with the task of decomposing the following matrix via SVD, i.e. decomposing the matrix into the form $USV^T$.

\begin{align*} A = \begin{bmatrix}3&2&2\\2&3&-2\end{bmatrix} \end{align*}

To approach this question, I first attempted to find the SVD decomposition of $A^T$, i.e. finding $USV^T$, then transposing it into $VS^TU^T$. I have renamed these variables back to $USV^T$ in the end.

To find the matrix $U$ for $A^T$, I found $u_1$ and $u_2$, which are calculated from $\frac{1}{\sigma_1}A^Tv_1$ and $\frac{1}{\sigma_2}A^Tv_2$. Then I calculated the cross product of those two vectors to make $u_3$. However, there seems to be an alternate solution (second one below) which does not involve finding $u_3$ (which I was always told was a necessary step). Therefore, I am wondering why this is valid and whether it is always okay to do so, any help would be appreciated.

\begin{align*} U &= \begin{bmatrix}\frac{1}{\sqrt2}&\frac{1}{\sqrt2} \\ \frac{1}{\sqrt2}&\frac{-1}{\sqrt2}\end{bmatrix} & S &= \begin{bmatrix}5&0&0 \\ 0&3&0\end{bmatrix} & V^T &= \begin{bmatrix}\frac{1}{\sqrt2}&\frac{1}{3\sqrt2}&2 \\ \frac{1}{\sqrt2}&\frac{-1}{3\sqrt2}&-\sqrt2 \\ 0&\frac{4}{3\sqrt2}&\frac{-1}{3\sqrt2}\end{bmatrix} \end{align*}

\begin{align*} U &= \begin{bmatrix}\frac{1}{\sqrt2}&\frac{1}{\sqrt2} \\ \frac{1}{\sqrt2}&\frac{-1}{\sqrt2}\end{bmatrix} & S &= \begin{bmatrix}5&0 \\ 0&3\end{bmatrix} & V^T &= \begin{bmatrix}\frac{1}{\sqrt2}&\frac{1}{\sqrt2}&0 \\ \frac{1}{3\sqrt2}&\frac{-1}{3\sqrt2}&\frac{4}{3\sqrt2}\end{bmatrix} \end{align*}

MPP
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  • SVD decomposition is not unique, however if $A$ is an $m\times n$ matrix, the matrices $U$ and $V$ in the SVD are unitary and of dimension $m\times m$ and $n\times n$ respectively, the diagonal matrix $S$ is of dimension $m\times n$. – Mittens Feb 27 '23 at 16:45
  • @OliverDíaz Thanks, so the second solution wouldn't qualify as an SVD decomposition since the matrix $S$ is not of the same dimensions of the input matrix? – MPP Feb 27 '23 at 23:24
  • Not in the sense I mentioned earlier. However, common implementations of SVD (mostly based on the rubust libraries in Fortran) will give you the values the last values you presented. The reason is that there is a choice (not unique) to obtain a complete orthonormal basis in $R^3$. One can complete the basis and make out choice to arrive to a particular SVD decomposition. The important part ususally is the S matrix. This posting explains how and why choices to complete orthonormal systems are made – Mittens Feb 27 '23 at 23:37

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