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## Solution to Mathematics for Machine Learning Exercise 4.12

Solution: By SVD (Singular Value Decomposition), we have $A=U\Sigma V^T$. Let $y=V^T x$, then $\|y\|_2^2=y^Ty=(V^Tx)^TV^Tx=x^TVV^Tx=x^Tx=\|x\|_2^2.$Then we have\begin{align*}\|A x\|_2^2=&\ (Ax)^T(Ax)=x^TA^TAx\\ = &\ x^T V \begin{bmatrix}\sigma_1^2 & 0 & 0\\ 0 & \ddots & 0\\ 0 & 0 &\sigma_n^2\end{bmatrix}V^Tx \\ =&\ y^T \begin{bmatrix}\sigma_1^2 & 0 & 0\\ 0 & \ddots & 0\\ 0 & 0 &\sigma_n^2\end{bmatrix} y \\ = &\ \sigma_1^2 y_1^2+\cdots+\sigma_n^2y_n^2 \\ \leqslant &\ \sigma_1^2 y_1^2+\cdots+\sigma_1^2y_n^2\\ =&\ \sigma_1^2(y_1^2+\cdots+y_n^2)\\ =&\ \sigma_1^2 \|y\|_2^2= \sigma_1^2 \|x\|_2^2.\end{align*}Hence we have $\max_{x\ne 0}\frac{\|Ax\|_2}{\|x\|_2}\leqslant \sigma_1^2.$Moreover, it is possible to choose $x$ such that the inequality above becomes equality. Namely, setting $$x=V(1,0,\dots,0)^T,$$then $y=(1,0,\dots,0)^T$ and $$\|A x\|_2^2=\sigma_1^2,\quad \|x\|_2=1.$$Hence we proved Theorem 4.24.

#### Linearity

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