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Handling the $\ell^2$ norm of a matrix expression in a linear regression

I am reading a scientific article in which matrices are handled (which I do not use often). We consider a matrix $X\in\mathbb R^{n\times p}$ and a vector $y\in\mathbb R^n$. The authors show that the o …
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Handling the $\ell^2$ norm of a matrix expression in a linear regression

I finally obtained the result using $||v||_2^2=v^Tv$, $(AB)^T=B^TA^T$, $(A+B)^T=A^T+B^T$, $(A^{-1})^T=(A^T)^{-1}$
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