# Minimum norm solution of a least squares using SVD

Let $A$ and $B$ be any real matrices. I would like to find the solution of a linear system $Ax=B$ using the SVD decomposition of $A$ given by $A = U S V^t$. If I am not very wrong, I believe I can obtain this solution using

$\ \ \ X = VL^tU^tB$

in which

$\ \ \ L_{ij} = \begin{cases} 1/S_{ij} && \text{if} S_{ij} \not= 0 \\\\ 0 && \text{otherwise} \end{cases}$

This seems to work. However, for the case when $A$ has more columns than rows, I would like to find not only the best solution (in the least squares sense), but also the solution which has minimum norm. How can I do that?

$\\\\$

For example, by taking

$\ \ \ A = \begin{pmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \end{pmatrix} \ \ \ \ \text{and} \ \ \ B = \begin{pmatrix} 7 & 8 & 9 \\\\ 10 & 11 & 12 \end{pmatrix}$

The aforementioned method gives the solution

$\ \ \ \hat{X} = \begin{pmatrix} -2.1667 & -2.6667 & -3.1667 \\\\ 0.3333 & 0.3333 & 0.3333 \\\\ 2.8333 & 3.3333 & 3.8333 \\\\ \end{pmatrix}$

I can verify this is a valid solution by taking

$\ \ \ A\hat{X} = \begin{pmatrix} 7 & 8 & 9 \\\\ 10 & 11 & 12 \end{pmatrix}$

However, I am not sure this is the minimum norm solution.

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You're missing a transpose in $X = VL^tU^tB$. –  Federico Poloni Jun 26 '12 at 7:20
Thanks for pointing it out. By the way, I just realized I should have asked this on math.stackexchange instead. Didn't realized there was a different site for more trivial questions. –  César Jun 27 '12 at 16:51