Added conclusion slide; Fixed Boyd book citation

latex/presentations/midterm/presentation.bib

@@ -77,9 +77,9 @@
     institution = {KIT},
 }
 
-@BOOK{distr_opt_book,
+@book{distr_opt_book,
   author    = {Boyd, Stephen and Parikh, Neal and Chu, Eric and Peleato, Borja and Eckstein, Jonathan},
-  booktitle = {Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers},
+  title = {Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers},
   year      = {2011},
   volume    = {},
   number    = {},

latex/presentations/midterm/sections/examination_results.tex

@@ -1504,74 +1504,23 @@ $\textcolor{KITblue}{\text{Output }\boldsymbol{\tilde{c}}_n\text{ with lowest }d
 \end{frame}
 
 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%\begin{frame}[t, fragile]
-%    \frametitle{Proximal Decoding: Improvement using ``ML-on-List''}
-%    \setcounter{footnote}{0} 
-%
-%    \begin{itemize}
-%        \item Improvement of proximal decoding by adding an ``ML-on-list'' step after iterating
-%    \end{itemize}
-%
-%    \begin{minipage}[t]{.48\textwidth}
-%        \centering
-%
-%        \begin{figure}
-%           \centering
-%
-%            \begin{algorithm}[caption={}, label={},
-%            basicstyle=\fontsize{7.5}{9.5}\selectfont
-%            ]
-%$\boldsymbol{s}^{\left( 0 \right)} = \boldsymbol{0}$
-%for $k=0$ to $K-1$ do
-%    $\boldsymbol{r}^{\left( k+1 \right)} = \boldsymbol{s}^{(k)} - \omega \nabla L \left( \boldsymbol{s}^{(k)}; \boldsymbol{y} \right) $
-%    Compute $\nabla h\left( \boldsymbol{r}^{\left( k+1 \right) } \right)$
-%    $\boldsymbol{s}^{\left( k+1 \right)} = \boldsymbol{r}^{(k+1)} - \gamma \nabla h\left( \boldsymbol{r}^{\left( k+1 \right) } \right) $
-%    $\boldsymbol{\hat{x}} = \text{sign}\left( \boldsymbol{s}^{\left( k+1 \right) } \right) $
-%    If $\boldsymbol{\hat{x}}$ passes the parity check condition, break the loop.
-%end for
-%Output $\boldsymbol{\hat{x}}$
-%            \end{algorithm}
-%
-%            \caption{Proximal decoding algorithm \cite{proximal_paper}}
-%        \end{figure}
-%
-%    \end{minipage}%
-%    \hfill\begin{minipage}[t]{.48\textwidth}
-%        \centering
-%        \begin{figure}
-%           \centering 
-%
-%            \begin{algorithm}[caption={}, label={},
-%            basicstyle=\fontsize{7.5}{9.5}\selectfont
-%            ]
-%$\boldsymbol{s}^{\left( 0 \right)} = \boldsymbol{0}$
-%for $k=0$ to $K-1$ do
-%    $\boldsymbol{r}^{\left( k+1 \right)} = \boldsymbol{s}^{(k)} - \omega \nabla L \left( \boldsymbol{s}^{(k)}; \boldsymbol{y} \right) $
-%    Compute $\nabla h\left( \boldsymbol{r}^{\left( k+1 \right) } \right)$
-%    $\boldsymbol{s}^{\left( k+1 \right)} = \boldsymbol{r}^{(k+1)} - \gamma \nabla h\left( \boldsymbol{r}^{\left( k+1 \right) } \right) $
-%    $\boldsymbol{\hat{x}} = \text{sign}\left( \boldsymbol{s}^{\left( k+1 \right) } \right) $
-%    $\textcolor{KITblue}{\text{If }\boldsymbol{\hat{x}}\text{ passes the parity check condition, output }\boldsymbol{\hat{x}}}$
-%end for
-%$\textcolor{KITblue}{\text{Find }N\text{ most probably wrong bits.}}$
-%$\textcolor{KITblue}{\text{Generate variations } \boldsymbol{\tilde{x}}_n\text{ of }\boldsymbol{\hat{x}}\text{ with the }N\text{ bits modified.}}$
-%$\textcolor{KITblue}{\text{Compute }\langle \boldsymbol{ \tilde{x}}_n, \boldsymbol{\hat{x}} \rangle \forall n \in \left[ 1 : N-1 \right]}$
-%$\textcolor{KITblue}{\text{Output }\boldsymbol{\tilde{x}}_n\text{ with lowest }\langle \boldsymbol{ \tilde{x}}_n, \boldsymbol{\hat{x}} \rangle}$
-%            \end{algorithm}
-%
-%            \caption{Hybrid proximal \& ML decoding algorithm}
-%        \end{figure}
-%    \end{minipage} 
-%\end{frame}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%\subsection{ADMM: Examination Results}%
-%\label{sub:Ex ADMM}
-%
-%\begin{frame}[t]
-%    \frametitle{ADMM}
-%
-%    \todo{TODO}
-%\end{frame}
-
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}[t]
+    \frametitle{Conclusion}
+   
+    \begin{itemize}
+        \item Analysis of proximal decoding for AWGN channels:
+            \begin{itemize}
+                \item Error coding performance (BER, FER, decoding failures)
+                \item Computational performance ($\mathcal{O}\left( n \right) $ time complexity,
+                                                 fast implementation possible)
+                \item Number of iterations required independant of SNR
+                \item Operation during iteration (oscillation of estimate)
+            \end{itemize}
+        \item Suggestion for improvement of proximal decoding:
+            \begin{itemize}
+                \item Addidion of ``ML-on-list'' step
+                \item $\sim\SI{1}{dB}$ gain under certain conditions
+            \end{itemize}
+    \end{itemize}
+\end{frame}

latex/presentations/midterm/sections/forthcoming_examination.tex

@@ -16,7 +16,7 @@
                 \item ADMM is intended to blend the decomposability
                     of dual ascent with the superior convergence properties of the method
                     of multipliers \cite{distr_opt_book}
-                \item Recently, ADMM has been proposed for efficient LP Decoding
+                \item ADMM has been proposed for efficient LP Decoding
                     \cite{efficient_lp_dec_admm}
             \end{itemize}
         \item Compare ADMM implementation with Proximal Decoding implementation with respect to