
\newpage
\begin{appendices}
  \section{Proofs for sone Lemmata in ...}
  In the following there will be proofs for some important Lemmata in
  Section~\ref{sec:theo38}. Further proofs not discussed here can be
  found in \textcite{heiss2019}
  \begin{Theorem}[Proof of Lemma~\ref{theo38}]
  \end{Theorem}
  
\begin{Lemma}[$\frac{w^{*,\tilde{\lambda}}_k}{v_k}\approx\mathcal{O}(\frac{1}{n})$]
  For any $\lambda > 0$ and training data $(x_i^{\text{train}},
  y_i^{\text{train}}) \in \mathbb{R}^2, \, i \in
  \left\{1,\dots,N\right\}$, we have
  \[
    \max_{k \in \left\{1,\dots,n\right\}} \frac{w^{*,
        \tilde{\lambda}}_k}{v_k} = \po_{n\to\infty}
  \]
  
  
\end{Lemma}
\end{appendices}






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