\newpage
\begin{appendices}
  \counterwithin{lstfloat}{section}
  \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}
  
\input{Appendix_code.tex}
  
\end{appendices}






%%% Local Variables:
%%% mode: latex
%%% TeX-master: "main"
%%% End: