ba-thesis/latex/presentations/midterm/sections/appendix.tex

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\appendix
\section{Proximal Decoding Time Complexity}%
\label{app:Proximal Decoding Time Complexity}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[t]
\frametitle{Proximal Decoding: Time Complexity}
\begin{figure}[H]
\centering
\begin{tikzpicture}[scale=0.7]
\begin{axis}[
grid=both,
xlabel={$n$}, ylabel={time per frame (s)},
legend style={at={(0.05,0.77)},anchor=south west},
]
\addplot[only marks, red] table [col sep=comma,
x=n, y=spf] {res/fps_vs_n_proximal.csv};
\addlegendentry{proximal}
\addplot[only marks, blue] table [col sep=comma,
x=n, y=spf] {res/fps_vs_n_hybrid.csv};
\addlegendentry{hybrid prox \& ML ($\SI{12}{\bit}$)}
\end{axis}
\end{tikzpicture}
\caption{Time Complexity of Proximal Decoding and Modified Implementation\footnotemark}
\label{fig:fps_vs_n}
\end{figure}
\footnotetext{The points shown were calculated by evaluating the metadata
of BER simulation results from the following codes:
BCH $\left( 31, 11 \right)$;
BCH $\left( 31, 26 \right)$;
\cite[\text{96.3.965; 204.33.484;
204.55.187; 408.33.844; PEGReg252x504}]{mackay_enc}
}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[t]
\frametitle{Proximal Decoding: Visualization of Gradients}
\begin{figure}[H]
\centering
\begin{subfigure}[c]{0.5\textwidth}
\centering
\begin{tikzpicture}[scale=0.8]
\begin{axis}[xmin = -1.25, xmax=1.25,
ymin = -1.25, ymax=1.25,
xlabel={$x_1$}, ylabel={$x_2$},
grid=major, grid style={dotted},
view={0}{90}]
\addplot3[point meta=\thisrow{grad_norm},
point meta min=1,
point meta max=3,
quiver={u=\thisrow{grad_0},
v=\thisrow{grad_1},
scale arrows=.05,
every arrow/.append style={%
line width=.3+\pgfplotspointmetatransformed/1000,
-{Latex[length=0pt 5,width=0pt 3]}
},
},
quiver/colored = {mapped color},
colormap/rocket,
-stealth,
]
table[col sep=comma] {res/2d_grad_L.csv};
\end{axis}
\end{tikzpicture}
\caption{$\nabla L \left(\boldsymbol{y} \mid \boldsymbol{x} \right) $ for a repetition code with $n=2$
\footnotemark}
\end{subfigure}%
\begin{subfigure}[c]{0.5\textwidth}
\centering
\begin{tikzpicture}[scale=0.8]
\begin{axis}[xmin = -1.25, xmax=1.25,
ymin = -1.25, ymax=1.25,
xlabel={$x_1$}, ylabel={$x_2$},
grid=major, grid style={dotted},
view={0}{90}]
\addplot3[point meta=\thisrow{grad_norm},
point meta min=1,
point meta max=4,
quiver={u=\thisrow{grad_0},
v=\thisrow{grad_1},
scale arrows=.03,
every arrow/.append style={%
line width=.3+\pgfplotspointmetatransformed/1000,
-{Latex[length=0pt 5,width=0pt 3]}
},
},
quiver/colored = {mapped color},
colormap/rocket,
-stealth,
]
table[col sep=comma] {res/2d_grad_rep.csv};
\end{axis}
\end{tikzpicture}
\caption{$\nabla h \left( \boldsymbol{x} \right) $ for a repetition code with $n=2$}
\end{subfigure}%
\end{figure}
\footnotetext{In an AWGN Channel $\nabla L\left( \boldsymbol{y} \mid \boldsymbol{x}\right)
\propto \left( \boldsymbol{x} - \boldsymbol{y} \right)$
\cite[Sec. 4.1]{proximal_paper}}
\end{frame}