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Added heatmaps showing relationship between gamma and omega

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Andreas Tsouchlos 2023-04-11 15:24:09 +02:00
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latex/thesis/chapters/appendix.tex
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@ -1,11 +1,11 @@
\appendix
\chapter{A Comparison of the Behaviour of Various Codes}
\chapter{A Comparison of the Behaviour of Proximal Decoding for Various Codes}
\begin{figure}[H]
\centering
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[view={75}{30},
@ -38,7 +38,7 @@
\cite[\text{96.3.965}]{mackay_enc}}
\end{subfigure}%
\hfill
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[view={75}{30},
@ -70,7 +70,9 @@
\caption{BCH code with $n=31, k=26$\\[2\baselineskip]}
\end{subfigure}
\begin{subfigure}[c]{0.48\textwidth}
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[view={75}{30},
@ -81,29 +83,29 @@
width=\textwidth,
height=0.75\textwidth,]
\addplot3[surf,
mesh/rows=17, mesh/cols=14,
mesh/rows=17, mesh/cols=10,
colormap/viridis] table [col sep=comma,
x=SNR, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_20433484.csv};
{res/proximal/2d_ber_fer_dfr_20433484_fewer_SNR.csv};
\addplot3[RedOrange, line width=1.5] table[col sep=comma,
discard if not={gamma}{0.05},
x=SNR, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_20433484.csv};
{res/proximal/2d_ber_fer_dfr_20433484_fewer_SNR.csv};
\addplot3[NavyBlue, line width=1.5] table[col sep=comma,
discard if not={gamma}{0.01},
x=SNR, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_20433484.csv};
{res/proximal/2d_ber_fer_dfr_20433484_fewer_SNR.csv};
\addplot3[ForestGreen, line width=1.5] table[col sep=comma,
discard if not={gamma}{0.15},
x=SNR, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_20433484.csv};
{res/proximal/2d_ber_fer_dfr_20433484_fewer_SNR.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=204, k=102$
\cite[\text{204.33.484}]{mackay_enc}}
\end{subfigure}%
\hfill
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[view={75}{30},
@ -136,7 +138,9 @@
\cite[\text{204.55.187}]{mackay_enc}}
\end{subfigure}%
\begin{subfigure}[c]{0.48\textwidth}
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[view={75}{30},
@ -169,7 +173,7 @@
\cite[\text{408.33.844}]{mackay_enc}}
\end{subfigure}%
\hfill
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[view={75}{30},
@ -202,9 +206,9 @@
\cite[\text{PEGReg252x504}]{mackay_enc}}
\end{subfigure}%
\vspace{1cm}
\vspace{5mm}
\begin{subfigure}[c]{\textwidth}
\begin{subfigure}[t]{\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[hide axis,
@ -231,7 +235,7 @@
\begin{figure}[H]
\centering
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
@ -273,7 +277,7 @@
\cite[\text{96.3.965}]{mackay_enc}}
\end{subfigure}%
\hfill%
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
@ -315,7 +319,9 @@
\caption{BCH code with $n=31, k=26$\\[\baselineskip]}
\end{subfigure}%
\begin{subfigure}[c]{0.48\textwidth}
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
@ -369,7 +375,7 @@
\cite[\text{204.33.484}]{mackay_enc}}
\end{subfigure}%
\hfill%
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
@ -412,7 +418,9 @@
\cite[\text{204.55.187}]{mackay_enc}}
\end{subfigure}%
\begin{subfigure}[c]{0.48\textwidth}
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
@ -455,7 +463,7 @@
\cite[\text{408.33.844}]{mackay_enc}}
\end{subfigure}%
\hfill%
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
@ -497,9 +505,9 @@
\label{fig:prox:improved:comp:504}
\end{subfigure}%
\vspace{1cm}
\vspace{5mm}
\begin{subfigure}[c]{\textwidth}
\begin{subfigure}[t]{\textwidth}
\centering
\begin{tikzpicture}
@ -534,172 +542,401 @@
\label{fig:prox:improved:comp}
\end{figure}
\chapter{\acs{LP} Decoding using \acs{ADMM} as a Proximal Algorithm}%
\label{chapter:LD Decoding using ADMM as a Proximal Algorithm}
\begin{figure}[H]
\centering
\begin{subfigure}[t]{0.48\textwidth}
\centering
\todo{Find out how to properly title and section appendix}
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=\textwidth,
height=0.75\textwidth,
point meta min=-5.7,
point meta max=-0.5,
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_963965.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=96, k=48$
\cite[\text{96.3.965}]{mackay_enc}}
\end{subfigure}%
\hfill
\begin{subfigure}[t]{0.48\textwidth}
\centering
%For problems of the form%
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=\textwidth,
height=0.75\textwidth,
point meta min=-5.7,
point meta max=-0.5,
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_bch_31_26.csv};
\end{axis}
\end{tikzpicture}
\caption{BCH code with $n=31, k=26$\\[2\baselineskip]}
\end{subfigure}%
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=\textwidth,
height=0.75\textwidth,
point meta min=-5.7,
point meta max=-0.5,
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_20433484.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=204, k=102$
\cite[\text{204.33.484}]{mackay_enc}}
\end{subfigure}%
\hfill
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=\textwidth,
height=0.75\textwidth,
point meta min=-5.7,
point meta max=-0.5,
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_20455187.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 5, 10 \right)$-regular \ac{LDPC} code with $n=204, k=102$
\cite[\text{204.55.187}]{mackay_enc}}
\end{subfigure}%
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=\textwidth,
height=0.75\textwidth,
point meta min=-5.7,
point meta max=-0.5,
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_40833844.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=408, k=204$
\cite[\text{408.33.844}]{mackay_enc}}
\end{subfigure}%
\hfill
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=\textwidth,
height=0.75\textwidth,
point meta min=-5.7,
point meta max=-0.5,
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_pegreg252x504.csv};
\end{axis}
\end{tikzpicture}
\caption{LDPC code (Progressive Edge Growth Construction) with $n=504, k=252$
\cite[\text{PEGReg252x504}]{mackay_enc}}
\end{subfigure}%
\vspace{5mm}
\begin{subfigure}{\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
hide axis,
scale only axis,
height=0pt,
width=0pt,
colormap/viridis,
colorbar horizontal,
point meta min=-5.7,
point meta max=-0.5,
colorbar style={
title={BER},
width=10cm,
xtick={-5,-4,...,-1},
xticklabels={$10^{-5}$,$10^{-4}$,$10^{-3}$,$10^{-2}$,$10^{-1}$}
}]
\addplot [draw=none] coordinates {(0,0)};
\end{axis}
\end{tikzpicture}
\end{subfigure}%
\caption{}
\label{fig:prox:gamma_omega_multiple}
\end{figure}
%\chapter{\acs{LP} Decoding using \acs{ADMM} as a Proximal Algorithm}%
%\label{chapter:LD Decoding using ADMM as a Proximal Algorithm}
%
%\todo{Find out how to properly title and section appendix}
%
%%For problems of the form%
%%\begin{align*}
%% \text{minimize}\hspace{2mm} & f\left( \boldsymbol{x} \right)
%% + g\left( \boldsymbol{A}\boldsymbol{x} \right) \\
%% \text{subject to}\hspace{2mm} & \boldsymbol{x} \in \mathbb{R}^n
%%,\end{align*}%
%%%
%%a version of \ac{ADMM}, \textit{linearized \ac{ADMM}}, can be expressed
%%as a proximal algorithm \cite[Sec. 4.4.2]{proximal_algorithms}:
%%%
%%\begin{align*}
%% \boldsymbol{x} &\leftarrow \textbf{prox}_{\mu f}\left( \boldsymbol{x}
%% - \frac{\mu}{\lambda}\boldsymbol{A}^\text{T}\left( \boldsymbol{A}\boldsymbol{x}
%% - \boldsymbol{z} + \boldsymbol{u} \right) \right) \\
%% \boldsymbol{z} &\leftarrow \textbf{prox}_{\lambda g}\left( \boldsymbol{A}\boldsymbol{x}
%% + \boldsymbol{u} \right) \\
%% \boldsymbol{u} &\leftarrow \boldsymbol{u} + \boldsymbol{A} \boldsymbol{x} - \boldsymbol{z}
%%.\end{align*}
%
%In order to express \ac{LP} decoding using \ac{ADMM} through proximal operators,
%it can be rewritten to fit the template for \textit{linearized \ac{ADMM}} given
%in \cite[Sec. 4.4.2]{proximal_algorithms}.
%We start with the general formulation of the \ac{LP} decoding problem:%
%%
%\begin{align*}
% \text{minimize}\hspace{2mm} & f\left( \boldsymbol{x} \right)
% + g\left( \boldsymbol{A}\boldsymbol{x} \right) \\
% \text{subject to}\hspace{2mm} & \boldsymbol{x} \in \mathbb{R}^n
% \text{minimize}\hspace{2mm} & \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}} \\
% \text{subject to}\hspace{2mm} & \boldsymbol{T}_j \tilde{\boldsymbol{c}} \in \mathcal{P}_{d_j}
% \hspace{5mm} \forall j \in \mathcal{J}
%.\end{align*}
%%
%The constraints can be moved into the objective function: %
%%
%\begin{align}
% \begin{aligned}
% \text{minimize}\hspace{2mm} & \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
% + \sum_{j\in \mathcal{J}} I_{P_{d_j}}\left(
% \boldsymbol{T}_j\tilde{\boldsymbol{c}} \right) \\
% \text{subject to}\hspace{2mm} & \tilde{\boldsymbol{c}} \in \mathbb{R}^n,
% \end{aligned}
% \label{eq:app:sum_reformulated}
%\end{align}%
%%
%using the \textit{indicator functions}
%$I_{\mathcal{P}_{d_j}} : \mathbb{R}^{d_j} \rightarrow \left\{ 0, +\infty \right\},
%\hspace{3mm} j\in \mathcal{J}$, defined as%
%%
%\begin{align*}
% I_{\mathcal{P}_{d_j}}\left( \boldsymbol{t} \right) :=
% \begin{cases}
% 0 & \boldsymbol{t} \in \mathcal{P}_{d_j} \\
% +\infty & \boldsymbol{t} \not\in \mathcal{P}_{d_j}
% \end{cases}
%.\end{align*}%
%%
%Further defining
%%
%\begin{align*}
% \boldsymbol{T} := \begin{bmatrix}
% \boldsymbol{T}_1 \\
% \boldsymbol{T}_2 \\
% \vdots \\
% \boldsymbol{T}_m
% \end{bmatrix}
% \hspace{5mm}\text{and}\hspace{5mm}
% g\left( \boldsymbol{t} \right) = \sum_{j\in\mathcal{J}} I_{\mathcal{P}_{d_j}}\left(
% \boldsymbol{B}_j \boldsymbol{t} \right)
%,\end{align*}%
%%
%a version of \ac{ADMM}, \textit{linearized \ac{ADMM}}, can be expressed
%as a proximal algorithm \cite[Sec. 4.4.2]{proximal_algorithms}:
%\todo{Define $\boldsymbol{B}_j$}%
%problem (\ref{eq:app:sum_reformulated}) becomes%
%%
%\begin{align}
% \begin{aligned}
% \text{minimize}\hspace{2mm} & \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
% + g\left( \boldsymbol{T}\tilde{\boldsymbol{c}} \right) \\
% \text{subject to}\hspace{2mm} & \tilde{\boldsymbol{c}} \in \mathbb{R}^n.
% \end{aligned}
% \label{eq:app:func_reformulated}
%\end{align}
%%
%\todo{Fix $\mu f$ and $\lambda g$ in the steps below}%
%In this form, it fits the template for linearized \ac{ADMM}.
%The iterative algorithm can then be expressed as%
%%
%\begin{align}
% \begin{aligned}
% \tilde{\boldsymbol{c}} &\leftarrow \textbf{prox}_{\mu f}\left( \tilde{\boldsymbol{c}}
% - \frac{\mu}{\lambda}\boldsymbol{T}^\text{T}\left( \boldsymbol{T}\tilde{\boldsymbol{c}}
% - \boldsymbol{z} + \boldsymbol{u} \right) \right) \\
% \boldsymbol{z} &\leftarrow \textbf{prox}_{\lambda g}\left(\boldsymbol{T}\tilde{\boldsymbol{c}}
% + \boldsymbol{u} \right) \\
% \boldsymbol{u} &\leftarrow \boldsymbol{u} + \boldsymbol{T} \tilde{\boldsymbol{c}}
% - \boldsymbol{z}.
% \end{aligned}
% \label{eq:app:admm_prox}
%\end{align}
%%
%
%Using the definition of the proximal operator, the $\tilde{\boldsymbol{c}}$ update step
%can be rewritten to match the definition given in section \ref{sec:lp:Decoding Algorithm}:%
%%
%\begin{align*}
% \boldsymbol{x} &\leftarrow \textbf{prox}_{\mu f}\left( \boldsymbol{x}
% - \frac{\mu}{\lambda}\boldsymbol{A}^\text{T}\left( \boldsymbol{A}\boldsymbol{x}
% \tilde{\boldsymbol{c}} &\leftarrow \textbf{prox}_{\mu f}\left( \tilde{\boldsymbol{c}}
% - \frac{\mu}{\lambda}\boldsymbol{T}^\text{T}\left( \boldsymbol{T}\tilde{\boldsymbol{c}}
% - \boldsymbol{z} + \boldsymbol{u} \right) \right) \\
% \boldsymbol{z} &\leftarrow \textbf{prox}_{\lambda g}\left( \boldsymbol{A}\boldsymbol{x}
% + \boldsymbol{u} \right) \\
% \boldsymbol{u} &\leftarrow \boldsymbol{u} + \boldsymbol{A} \boldsymbol{x} - \boldsymbol{z}
% &= \argmin_{\tilde{\boldsymbol{c}}}\left( \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
% - \frac{\mu}{2} \left\Vert \boldsymbol{T}^\text{T}
% \left( \boldsymbol{T}\tilde{\boldsymbol{c}}
% - \boldsymbol{z} + \boldsymbol{u} \right) \right\Vert_2^2 \right) \\
% &\overset{\text{(a)}}{=} \argmin_{\tilde{\boldsymbol{c}}}\left( \boldsymbol{\gamma}^\text{T}
% \tilde{\boldsymbol{c}}
% - \frac{\mu}{2} \left\Vert \boldsymbol{T}\tilde{\boldsymbol{c}}
% - \boldsymbol{z} + \boldsymbol{u} \right\Vert_2^2 \right) \\
% &= \argmin_{\tilde{\boldsymbol{c}}}\left( \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
% - \frac{\mu}{2} \left\Vert \begin{bmatrix}
% \boldsymbol{T}_1 \\
% \boldsymbol{T}_2 \\
% \vdots \\
% \boldsymbol{T}_m
% \end{bmatrix}
% \tilde{\boldsymbol{c}}
% - \begin{bmatrix}
% \boldsymbol{z}_1 \\
% \boldsymbol{z}_2 \\
% \vdots \\
% \boldsymbol{z}_m
% \end{bmatrix}
% + \begin{bmatrix}
% \boldsymbol{u}_1 \\
% \boldsymbol{u}_2 \\
% \vdots \\
% \boldsymbol{u}_m
% \end{bmatrix} \right\Vert_2^2 \right),
% \hspace{5mm}\boldsymbol{z}_j,\boldsymbol{u}_j \in \mathbb{F}_2^{d_j},
% \hspace{2mm} j\in\mathcal{J}\\
% &= \argmin_{\tilde{\boldsymbol{c}}} \left( \boldsymbol{\gamma}^\text{T} \tilde{\boldsymbol{c}}
% - \frac{\mu}{2} \sum_{j \in J} \left\Vert \boldsymbol{T}_j \tilde{\boldsymbol{c}}
% - \boldsymbol{z}_j + \boldsymbol{u}_j \right\Vert_2^2 \right)
%.\end{align*}
In order to express \ac{LP} decoding using \ac{ADMM} through proximal operators,
it can be rewritten to fit the template for \textit{linearized \ac{ADMM}} given
in \cite[Sec. 4.4.2]{proximal_algorithms}.
We start with the general formulation of the \ac{LP} decoding problem:%
%
\begin{align*}
\text{minimize}\hspace{2mm} & \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}} \\
\text{subject to}\hspace{2mm} & \boldsymbol{T}_j \tilde{\boldsymbol{c}} \in \mathcal{P}_{d_j}
\hspace{5mm} \forall j \in \mathcal{J}
.\end{align*}
%
The constraints can be moved into the objective function: %
%
\begin{align}
\begin{aligned}
\text{minimize}\hspace{2mm} & \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
+ \sum_{j\in \mathcal{J}} I_{P_{d_j}}\left(
\boldsymbol{T}_j\tilde{\boldsymbol{c}} \right) \\
\text{subject to}\hspace{2mm} & \tilde{\boldsymbol{c}} \in \mathbb{R}^n,
\end{aligned}
\label{eq:app:sum_reformulated}
\end{align}%
%
using the \textit{indicator functions}
$I_{\mathcal{P}_{d_j}} : \mathbb{R}^{d_j} \rightarrow \left\{ 0, +\infty \right\},
\hspace{3mm} j\in \mathcal{J}$, defined as%
%
\begin{align*}
I_{\mathcal{P}_{d_j}}\left( \boldsymbol{t} \right) :=
\begin{cases}
0 & \boldsymbol{t} \in \mathcal{P}_{d_j} \\
+\infty & \boldsymbol{t} \not\in \mathcal{P}_{d_j}
\end{cases}
.\end{align*}%
%
Further defining
%
\begin{align*}
\boldsymbol{T} := \begin{bmatrix}
\boldsymbol{T}_1 \\
\boldsymbol{T}_2 \\
\vdots \\
\boldsymbol{T}_m
\end{bmatrix}
\hspace{5mm}\text{and}\hspace{5mm}
g\left( \boldsymbol{t} \right) = \sum_{j\in\mathcal{J}} I_{\mathcal{P}_{d_j}}\left(
\boldsymbol{B}_j \boldsymbol{t} \right)
,\end{align*}%
%
\todo{Define $\boldsymbol{B}_j$}%
problem (\ref{eq:app:sum_reformulated}) becomes%
%
\begin{align}
\begin{aligned}
\text{minimize}\hspace{2mm} & \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
+ g\left( \boldsymbol{T}\tilde{\boldsymbol{c}} \right) \\
\text{subject to}\hspace{2mm} & \tilde{\boldsymbol{c}} \in \mathbb{R}^n.
\end{aligned}
\label{eq:app:func_reformulated}
\end{align}
%
\todo{Fix $\mu f$ and $\lambda g$ in the steps below}%
In this form, it fits the template for linearized \ac{ADMM}.
The iterative algorithm can then be expressed as%
%
\begin{align}
\begin{aligned}
\tilde{\boldsymbol{c}} &\leftarrow \textbf{prox}_{\mu f}\left( \tilde{\boldsymbol{c}}
- \frac{\mu}{\lambda}\boldsymbol{T}^\text{T}\left( \boldsymbol{T}\tilde{\boldsymbol{c}}
- \boldsymbol{z} + \boldsymbol{u} \right) \right) \\
\boldsymbol{z} &\leftarrow \textbf{prox}_{\lambda g}\left(\boldsymbol{T}\tilde{\boldsymbol{c}}
+ \boldsymbol{u} \right) \\
\boldsymbol{u} &\leftarrow \boldsymbol{u} + \boldsymbol{T} \tilde{\boldsymbol{c}}
- \boldsymbol{z}.
\end{aligned}
\label{eq:app:admm_prox}
\end{align}
%
Using the definition of the proximal operator, the $\tilde{\boldsymbol{c}}$ update step
can be rewritten to match the definition given in section \ref{sec:lp:Decoding Algorithm}:%
%
\begin{align*}
\tilde{\boldsymbol{c}} &\leftarrow \textbf{prox}_{\mu f}\left( \tilde{\boldsymbol{c}}
- \frac{\mu}{\lambda}\boldsymbol{T}^\text{T}\left( \boldsymbol{T}\tilde{\boldsymbol{c}}
- \boldsymbol{z} + \boldsymbol{u} \right) \right) \\
&= \argmin_{\tilde{\boldsymbol{c}}}\left( \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
- \frac{\mu}{2} \left\Vert \boldsymbol{T}^\text{T}
\left( \boldsymbol{T}\tilde{\boldsymbol{c}}
- \boldsymbol{z} + \boldsymbol{u} \right) \right\Vert_2^2 \right) \\
&\overset{\text{(a)}}{=} \argmin_{\tilde{\boldsymbol{c}}}\left( \boldsymbol{\gamma}^\text{T}
\tilde{\boldsymbol{c}}
- \frac{\mu}{2} \left\Vert \boldsymbol{T}\tilde{\boldsymbol{c}}
- \boldsymbol{z} + \boldsymbol{u} \right\Vert_2^2 \right) \\
&= \argmin_{\tilde{\boldsymbol{c}}}\left( \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
- \frac{\mu}{2} \left\Vert \begin{bmatrix}
\boldsymbol{T}_1 \\
\boldsymbol{T}_2 \\
\vdots \\
\boldsymbol{T}_m
\end{bmatrix}
\tilde{\boldsymbol{c}}
- \begin{bmatrix}
\boldsymbol{z}_1 \\
\boldsymbol{z}_2 \\
\vdots \\
\boldsymbol{z}_m
\end{bmatrix}
+ \begin{bmatrix}
\boldsymbol{u}_1 \\
\boldsymbol{u}_2 \\
\vdots \\
\boldsymbol{u}_m
\end{bmatrix} \right\Vert_2^2 \right),
\hspace{5mm}\boldsymbol{z}_j,\boldsymbol{u}_j \in \mathbb{F}_2^{d_j},
\hspace{2mm} j\in\mathcal{J}\\
&= \argmin_{\tilde{\boldsymbol{c}}} \left( \boldsymbol{\gamma}^\text{T} \tilde{\boldsymbol{c}}
- \frac{\mu}{2} \sum_{j \in J} \left\Vert \boldsymbol{T}_j \tilde{\boldsymbol{c}}
- \boldsymbol{z}_j + \boldsymbol{u}_j \right\Vert_2^2 \right)
.\end{align*}
%
Step (a) can be justified by observing that multiplication with $\boldsymbol{T}^\text{T}$
only reorders components, leaving their values unchanged.
Similarly to the $\boldsymbol{c}$ update, the $\boldsymbol{z}$ update step can be rewritten.
Since $g\left( \cdot \right)$ is separable, so is its proximal operator
\cite[Sec. 2.1]{proximal_algorithms}. The $\boldsymbol{z}$ update step can then
be expressed as a number of smaller steps:%
%
\begin{gather*}
\boldsymbol{z} \leftarrow \textbf{prox}_{\lambda g} \left(\boldsymbol{T}\tilde{\boldsymbol{c}}
+ \boldsymbol{u} \right) \\[0.5em]
\iff \\[0.5em]
\begin{alignedat}{3}
\boldsymbol{z}_j &\leftarrow \textbf{prox}_{\lambda I_{\mathcal{P}_{d_j}}}\left(
\boldsymbol{T}_j \tilde{\boldsymbol{c}} + \boldsymbol{u}_j \right),
\hspace{5mm} && \forall j\in\mathcal{J} \\
& \overset{\text{(b)}}{=} \Pi_{\mathcal{P}_{d_j}}\left( \boldsymbol{T}_j
\tilde{\boldsymbol{c}}
+ \boldsymbol{u}_j \right), \hspace{5mm} && \forall j\in\mathcal{J}
,\end{alignedat}
\end{gather*}
%
where (b) results from the fact that appying the proximal operator on the
indicator function of a convex set amounts to a projection onto the set
\cite[Sec. 1.2]{proximal_algorithms}.
%%
%Step (a) can be justified by observing that multiplication with $\boldsymbol{T}^\text{T}$
%only reorders components, leaving their values unchanged.
%Similarly to the $\boldsymbol{c}$ update, the $\boldsymbol{z}$ update step can be rewritten.
%Since $g\left( \cdot \right)$ is separable, so is its proximal operator
%\cite[Sec. 2.1]{proximal_algorithms}. The $\boldsymbol{z}$ update step can then
%be expressed as a number of smaller steps:%
%%
%\begin{gather*}
% \boldsymbol{z} \leftarrow \textbf{prox}_{\lambda g} \left(\boldsymbol{T}\tilde{\boldsymbol{c}}
% + \boldsymbol{u} \right) \\[0.5em]
% \iff \\[0.5em]
% \begin{alignedat}{3}
% \boldsymbol{z}_j &\leftarrow \textbf{prox}_{\lambda I_{\mathcal{P}_{d_j}}}\left(
% \boldsymbol{T}_j \tilde{\boldsymbol{c}} + \boldsymbol{u}_j \right),
% \hspace{5mm} && \forall j\in\mathcal{J} \\
% & \overset{\text{(b)}}{=} \Pi_{\mathcal{P}_{d_j}}\left( \boldsymbol{T}_j
% \tilde{\boldsymbol{c}}
% + \boldsymbol{u}_j \right), \hspace{5mm} && \forall j\in\mathcal{J}
% ,\end{alignedat}
%\end{gather*}
%%
%where (b) results from the fact that appying the proximal operator on the
%indicator function of a convex set amounts to a projection onto the set
%\cite[Sec. 1.2]{proximal_algorithms}.

54
latex/thesis/chapters/proximal_decoding.tex
View File

@ -260,7 +260,7 @@ It was subsequently reimplemented in C++ using the Eigen%
linear algebra library to achieve higher performance.
The focus has been set on a fast implementation, sometimes at the expense of
memory usage, somewhat limiting the size of the codes the implemenation can be
used with \todo{Is this a appropriate for a bachelor's thesis?}.
used with \todo{Is this sentence appropriate for a bachelor's thesis?}.
The evaluation of the simulation results has been wholly realized in Python.
The gradient of the code-constraint polynomial \cite[Sec. 2.3]{proximal_paper}
@ -498,16 +498,60 @@ The parameter $\gamma$ describes the step-size for the optimization step
dealing with the code-constraint polynomial;
the parameter $\omega$ describes the step-size for the step dealing with the
negative-log likelihood.
The relationship between $\omega$ and $\gamma$ is studied in figure
\ref{TODO}.
The relationship between $\omega$ and $\gamma$ is portrayed in figure
\ref{fig:prox:gamma_omega}.
The \ac{SNR} is kept constant at $\SI{4}{dB}$.
Similar behaviour to $\gamma$ is exhibited: the \ac{BER} is minimized when
keeping the value within certain bounds, without displaying a clear
optimum.
It is noteworthy that the decoder seems to achieve the best performance for
similar values of the two step sizes.
Again, this consideration applies to a multitude of different codes, depicted
in figure \ref{TODO}.
Again, this consideration applies to a multitude of different codes, as
depicted in figure \ref{fig:prox:gamma_omega_multiple}.
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
colormap/viridis,
colorbar,
xlabel={$\omega$}, ylabel={$\gamma$},
at={(0,0)}, view={0}{90},
zmode=log,
ytick={0, 0.05, 0.1, 0.15},
yticklabels={0, 0.05, 0.1, 0.15},
xtick={0.05, 0.1, 0.15, 0.2},
xticklabels={0.05, 0.1, 0.15, 0.2},
width=0.6\textwidth,
height=0.45\textwidth,
point meta min=-5.7,
point meta max=-0.5,
colorbar style={
title={BER},
ytick={-5,-4,...,-1},
yticklabels={$10^{-5}$,$10^{-4}$,$10^{-3}$,$10^{-2}$,$10^{-1}$}
}]
]
\addplot3[
surf,
shader=flat,
mesh/rows=17, mesh/cols=10,
]
table [col sep=comma, x=omega, y=gamma, z=BER]
{res/proximal/2d_ber_fer_dfr_gamma_omega_20433484.csv};
\end{axis}
\end{tikzpicture}
\caption{The \ac{BER} as a function of the two step sizes\protect\footnotemark{}}
\label{fig:prox:gamma_omega}
\end{figure}%
%
\footnotetext{(3,6) regular \ac{LDPC} code with n = 204, k = 102
\cite[\text{204.33.484}]{mackay_enc}; $SNR=\SI{4}{dB}, K=100, \eta=1.5$
}%
%
To better understand how to determine the optimal value for the parameter $K$,
the average error is inspected.

171
latex/thesis/res/proximal/2d_ber_fer_dfr_20433484_fewer_SNR.csv Normal file
View File

@ -0,0 +1,171 @@
SNR,gamma,BER,FER,DFR,num_iterations
1.0,0.0,0.13016889924286545,1.0,0.5,101.0
1.5,0.0,0.11390992040380508,1.0,0.5,101.0
2.0,0.0,0.10430013589594253,1.0,0.5,101.0
2.5,0.0,0.09017666472529606,1.0,0.5,101.0
3.0,0.0,0.08182877111240536,1.0,0.5,101.0
3.5,0.0,0.06887012230634829,1.0,0.5,101.0
4.0,0.0,0.057173364395263056,1.0,0.5,101.0
4.5,0.0,0.04610755193166376,1.0,0.5,101.0
5.0,0.0,0.03601242477188896,1.0,0.5,101.0
5.5,0.0,0.03195886197616302,0.9901960784313726,0.4975369458128079,102.0
1.0,0.01,0.09410794020578528,1.0,0.5,101.0
1.5,0.01,0.06997308727412534,0.9901960784313726,0.4975369458128079,102.0
2.0,0.01,0.05644393679802018,0.9805825242718447,0.4950980392156863,103.0
2.5,0.01,0.03820780648708082,0.9439252336448598,0.4855769230769231,107.0
3.0,0.01,0.025490196078431372,0.9181818181818182,0.4786729857819905,110.0
3.5,0.01,0.014961954931226222,0.753731343283582,0.4297872340425532,134.0
4.0,0.01,0.007832183152590644,0.5642458100558659,0.3607142857142857,179.0
4.5,0.01,0.003332051774958349,0.3300653594771242,0.24815724815724816,306.0
5.0,0.01,0.0017192128650021159,0.18165467625899281,0.1537290715372907,556.0
5.5,0.01,0.0007640035685135623,0.09777347531461762,0.0890652557319224,1033.0
1.0,0.02,0.09030180699730873,0.9901960784313726,0.4975369458128079,102.0
1.5,0.02,0.06522331154684095,0.9351851851851852,0.48325358851674644,108.0
2.0,0.02,0.049919050188882895,0.926605504587156,0.48095238095238096,109.0
2.5,0.02,0.028994576554025864,0.7163120567375887,0.41735537190082644,141.0
3.0,0.02,0.01815435476516188,0.5872093023255814,0.36996336996337,172.0
3.5,0.02,0.009278711484593838,0.4007936507936508,0.28611898016997167,252.0
4.0,0.02,0.003515158519203359,0.19842829076620824,0.16557377049180327,509.0
4.5,0.02,0.0013487523022509087,0.08395677472984206,0.07745398773006135,1203.0
5.0,0.02,0.0004013143719026072,0.027747252747252746,0.026998128842555466,3640.0
5.5,0.02,0.00016504731356322146,0.011449948985375808,0.011320331764178435,8821.0
1.0,0.03,0.09037692747001713,0.9805825242718447,0.4950980392156863,103.0
1.5,0.03,0.06437619295505813,0.8938053097345132,0.4719626168224299,113.0
2.0,0.03,0.04964273845131273,0.8559322033898306,0.4611872146118721,118.0
2.5,0.03,0.026193913148081317,0.6196319018404908,0.38257575757575757,163.0
3.0,0.03,0.01603641456582633,0.48095238095238096,0.3247588424437299,210.0
3.5,0.03,0.007452113793494276,0.29190751445086704,0.22595078299776286,346.0
4.0,0.03,0.0028573605608046325,0.13082901554404144,0.1156930126002291,772.0
4.5,0.03,0.0010247036667774454,0.05706214689265537,0.053981827899518976,1770.0
5.0,0.03,0.0003044266400985235,0.017819336626676077,0.01750736696134512,5668.0
5.5,0.03,0.00012169749196725593,0.007310364794441228,0.007257311202126895,13816.0
1.0,0.04,0.09207362190159082,0.9528301886792453,0.48792270531400966,106.0
1.5,0.04,0.06675191815856778,0.8782608695652174,0.4675925925925926,115.0
2.0,0.04,0.04750233426704015,0.8015873015873016,0.44493392070484583,126.0
2.5,0.04,0.026018099547511313,0.5976331360946746,0.37407407407407406,169.0
3.0,0.04,0.012881896944824441,0.39147286821705424,0.28133704735376047,258.0
3.5,0.04,0.0065840061514802,0.24754901960784315,0.19842829076620824,408.0
4.0,0.04,0.0026100649280614337,0.1111111111111111,0.1,909.0
4.5,0.04,0.0009278379283402166,0.04791271347248577,0.045722046174739704,2108.0
5.0,0.04,0.0002976899261728983,0.016356275303643725,0.016093052899936264,6175.0
5.5,0.04,0.00011358050437191844,0.006394023803494555,0.0063534000125809904,15796.0
1.0,0.05,0.0947558268590455,0.9528301886792453,0.48792270531400966,106.0
1.5,0.05,0.06581415174765558,0.8782608695652174,0.4675925925925926,115.0
2.0,0.05,0.047244094488188976,0.7952755905511811,0.44298245614035087,127.0
2.5,0.05,0.02558940242763772,0.6011904761904762,0.3754646840148699,168.0
3.0,0.05,0.012649629140045532,0.3782771535580524,0.27445652173913043,267.0
3.5,0.05,0.00588833094213295,0.20528455284552846,0.1703204047217538,492.0
4.0,0.05,0.002538604818842757,0.10316649642492338,0.09351851851851851,979.0
4.5,0.05,0.0009202565784678184,0.046933085501858735,0.044829116733244564,2152.0
5.0,0.05,0.0002940220301384533,0.01641742522756827,0.01615224692147769,6152.0
5.5,0.05,0.00011692575119219662,0.006564409203171715,0.006521598760250533,15386.0
1.0,0.06,0.0957732149463559,0.9528301886792453,0.48792270531400966,106.0
1.5,0.06,0.06699346405228758,0.8632478632478633,0.463302752293578,117.0
2.0,0.06,0.05048633626679018,0.7952755905511811,0.44298245614035087,127.0
2.5,0.06,0.026059274284426413,0.5804597701149425,0.36727272727272725,174.0
3.0,0.06,0.012814863773224645,0.3782771535580524,0.27445652173913043,267.0
3.5,0.06,0.006304073735924525,0.22394678492239467,0.18297101449275363,451.0
4.0,0.06,0.0025645685668706205,0.10401647785787847,0.09421641791044776,971.0
4.5,0.06,0.0008676426545460951,0.04537286612758311,0.04340352385045122,2226.0
5.0,0.06,0.0003190777623385312,0.017672790901137356,0.01736588720770289,5715.0
5.5,0.06,0.00012802567341504216,0.00732733604178758,0.00727403673028448,13784.0
1.0,0.07,0.09734554199038106,0.9528301886792453,0.48792270531400966,106.0
1.5,0.07,0.06668356997971603,0.8706896551724138,0.46543778801843316,116.0
2.0,0.07,0.0509344362745098,0.7890625,0.4410480349344978,128.0
2.5,0.07,0.026284651791751185,0.5804597701149425,0.36727272727272725,174.0
3.0,0.07,0.01309025482852317,0.3782771535580524,0.27445652173913043,267.0
3.5,0.07,0.006422119409561578,0.22696629213483147,0.184981684981685,445.0
4.0,0.07,0.002640349738129247,0.11147902869757174,0.1002979145978153,906.0
4.5,0.07,0.0009502336090686274,0.04931640625,0.04699860400186133,2048.0
5.0,0.07,0.0003310118602339736,0.018090632276553824,0.01776917663617171,5583.0
5.5,0.07,0.00011619651767974708,0.0065772336545975515,0.006534256323995601,15356.0
1.0,0.08,0.09487813817115631,0.9439252336448598,0.4855769230769231,107.0
1.5,0.08,0.06786680189317106,0.8706896551724138,0.46543778801843316,116.0
2.0,0.08,0.05188536953242836,0.7769230769230769,0.43722943722943725,130.0
2.5,0.08,0.027359781121751026,0.5872093023255814,0.36996336996337,172.0
3.0,0.08,0.01338979171435111,0.39299610894941633,0.28212290502793297,257.0
3.5,0.08,0.006565126050420168,0.22544642857142858,0.18397085610200364,448.0
4.0,0.08,0.0026592336280917596,0.11002178649237472,0.09911678115799803,918.0
4.5,0.08,0.0010749450642325896,0.05441810344827586,0.0516096065406234,1856.0
5.0,0.08,0.00036965605914496947,0.019479267116682738,0.01910707529322739,5185.0
5.5,0.08,0.00012361614795691875,0.007035876001393243,0.0069867183176535695,14355.0
1.0,0.09,0.09762301146873843,0.9528301886792453,0.48792270531400966,106.0
1.5,0.09,0.06987444100447196,0.8859649122807017,0.4697674418604651,114.0
2.0,0.09,0.0531496062992126,0.7952755905511811,0.44298245614035087,127.0
2.5,0.09,0.028342245989304814,0.6121212121212121,0.37969924812030076,165.0
3.0,0.09,0.015068990559186637,0.4156378600823045,0.2936046511627907,243.0
3.5,0.09,0.006645114942528735,0.21767241379310345,0.17876106194690267,464.0
4.0,0.09,0.0026683622451197774,0.11197339246119734,0.10069790628115653,902.0
4.5,0.09,0.0011544262675484394,0.059763313609467454,0.056393076493579004,1690.0
5.0,0.09,0.000323066434669353,0.017471025774087528,0.01717103026181571,5781.0
5.5,0.09,0.00013994339191245457,0.008122235625251308,0.008056796426292279,12435.0
1.0,0.1,0.09863478101520982,0.9439252336448598,0.4855769230769231,107.0
1.5,0.1,0.07107843137254902,0.9017857142857143,0.47417840375586856,112.0
2.0,0.1,0.0548406862745098,0.7890625,0.4410480349344978,128.0
2.5,0.1,0.030971479500891266,0.6558441558441559,0.396078431372549,154.0
3.0,0.1,0.013721912122513238,0.3686131386861314,0.2693333333333333,274.0
3.5,0.1,0.007117601138816182,0.23653395784543327,0.19128787878787878,427.0
4.0,0.1,0.003256827969932857,0.1377899045020464,0.1211031175059952,733.0
4.5,0.1,0.0010676841344484178,0.04834849210148397,0.046118721461187215,2089.0
5.0,0.1,0.000391496273499491,0.02001585414189457,0.01962308140664465,5046.0
5.5,0.1,0.00013110636765910645,0.007300852970941159,0.007247936849659132,13834.0
1.0,0.11,0.09868059373282023,0.9439252336448598,0.4855769230769231,107.0
1.5,0.11,0.07357317927170869,0.9017857142857143,0.47417840375586856,112.0
2.0,0.11,0.05273321449792038,0.7651515151515151,0.4334763948497854,132.0
2.5,0.11,0.033249484004127965,0.6644736842105263,0.39920948616600793,152.0
3.0,0.11,0.014938457134678689,0.3686131386861314,0.2693333333333333,274.0
3.5,0.11,0.00837662033787262,0.24105011933174225,0.19423076923076923,419.0
4.0,0.11,0.0035059537469083863,0.13133940182054615,0.11609195402298851,769.0
4.5,0.11,0.0011208468207017151,0.04791271347248577,0.045722046174739704,2108.0
5.0,0.11,0.00043153577768256107,0.021019771071800208,0.02058703628210355,4805.0
5.5,0.11,0.00015734593947451842,0.00792654214409041,0.007864206182356148,12742.0
1.0,0.12,0.12740384615384615,0.9711538461538461,0.4926829268292683,104.0
1.5,0.12,0.10430283224400871,0.9351851851851852,0.48325358851674644,108.0
2.0,0.12,0.08233632436025258,0.8559322033898306,0.4611872146118721,118.0
2.5,0.12,0.06005809731299928,0.7481481481481481,0.4279661016949153,135.0
3.0,0.12,0.03891090114802398,0.5233160621761658,0.3435374149659864,193.0
3.5,0.12,0.022793232816592342,0.36462093862815886,0.2671957671957672,277.0
4.0,0.12,0.010509303903710847,0.18397085610200364,0.15538461538461537,549.0
4.5,0.12,0.006082062454611474,0.10390946502057613,0.09412861136999068,972.0
5.0,0.12,0.0030410982709184117,0.04972919743968488,0.0473733583489681,2031.0
5.5,0.12,0.0012914918514713017,0.021002287377833228,0.020370747606437156,4809.0
1.0,0.13,0.20258202290817318,1.0,0.5,101.0
1.5,0.13,0.1892350999805863,1.0,0.5,101.0
2.0,0.13,0.1705788084464555,0.9711538461538461,0.4926829268292683,104.0
2.5,0.13,0.16332233828110684,0.9439252336448598,0.4855769230769231,107.0
3.0,0.13,0.13447583757912615,0.7952755905511811,0.44298245614035087,127.0
3.5,0.13,0.12821350762527234,0.7481481481481481,0.4279661016949153,135.0
4.0,0.13,0.09359325125398997,0.5872093023255814,0.36996336996337,172.0
4.5,0.13,0.07852299798424042,0.4719626168224299,0.32063492063492066,214.0
5.0,0.13,0.061255361519607844,0.39453125,0.28291316526610644,256.0
5.5,0.13,0.057405013287366226,0.36996336996337,0.2700534759358289,273.0
1.0,0.14,0.30188312948941953,1.0,0.5,101.0
1.5,0.14,0.2989225393127548,1.0,0.5,101.0
2.0,0.14,0.2915938652688798,1.0,0.5,101.0
2.5,0.14,0.2830161476355248,0.9901960784313726,0.4975369458128079,102.0
3.0,0.14,0.26663838612368024,0.9711538461538461,0.4926829268292683,104.0
3.5,0.14,0.26035367417995237,0.9439252336448598,0.4855769230769231,107.0
4.0,0.14,0.2349137931034483,0.8706896551724138,0.46543778801843316,116.0
4.5,0.14,0.22372227579556414,0.8278688524590164,0.452914798206278,122.0
5.0,0.14,0.22702965483714146,0.8347107438016529,0.45495495495495497,121.0
5.5,0.14,0.20850433196534426,0.7829457364341085,0.4391304347826087,129.0
1.0,0.15,0.37036497767423804,1.0,0.5,101.0
1.5,0.15,0.36036691904484563,1.0,0.5,101.0
2.0,0.15,0.35255290234905845,1.0,0.5,101.0
2.5,0.15,0.3493981751116288,1.0,0.5,101.0
3.0,0.15,0.3480392156862745,0.9901960784313726,0.4975369458128079,102.0
3.5,0.15,0.34475537787930705,0.9805825242718447,0.4950980392156863,103.0
4.0,0.15,0.34837235865219873,0.9805825242718447,0.4950980392156863,103.0
4.5,0.15,0.3258258258258258,0.9099099099099099,0.47641509433962265,111.0
5.0,0.15,0.3237076648841355,0.9181818181818182,0.4786729857819905,110.0
5.5,0.15,0.30232947880006705,0.8632478632478633,0.463302752293578,117.0
1.0,0.16,0.40870704717530576,1.0,0.5,101.0
1.5,0.16,0.4099204038050864,1.0,0.5,101.0
2.0,0.16,0.39845251826220685,0.9901960784313726,0.4975369458128079,102.0
2.5,0.16,0.39511745292176276,1.0,0.5,101.0
3.0,0.16,0.4057949912638323,1.0,0.5,101.0
3.5,0.16,0.40695981362842165,1.0,0.5,101.0
4.0,0.16,0.4024842946887493,0.9805825242718447,0.4950980392156863,103.0
4.5,0.16,0.3882447209653092,0.9711538461538461,0.4926829268292683,104.0
5.0,0.16,0.4009515570934256,0.9901960784313726,0.4975369458128079,102.0
5.5,0.16,0.37219251336898396,0.9181818181818182,0.4786729857819905,110.0
1 SNR gamma BER FER DFR num_iterations
2 1.0 0.0 0.13016889924286545 1.0 0.5 101.0
3 1.5 0.0 0.11390992040380508 1.0 0.5 101.0
4 2.0 0.0 0.10430013589594253 1.0 0.5 101.0
5 2.5 0.0 0.09017666472529606 1.0 0.5 101.0
6 3.0 0.0 0.08182877111240536 1.0 0.5 101.0
7 3.5 0.0 0.06887012230634829 1.0 0.5 101.0
8 4.0 0.0 0.057173364395263056 1.0 0.5 101.0
9 4.5 0.0 0.04610755193166376 1.0 0.5 101.0
10 5.0 0.0 0.03601242477188896 1.0 0.5 101.0
11 5.5 0.0 0.03195886197616302 0.9901960784313726 0.4975369458128079 102.0
12 1.0 0.01 0.09410794020578528 1.0 0.5 101.0
13 1.5 0.01 0.06997308727412534 0.9901960784313726 0.4975369458128079 102.0
14 2.0 0.01 0.05644393679802018 0.9805825242718447 0.4950980392156863 103.0
15 2.5 0.01 0.03820780648708082 0.9439252336448598 0.4855769230769231 107.0
16 3.0 0.01 0.025490196078431372 0.9181818181818182 0.4786729857819905 110.0
17 3.5 0.01 0.014961954931226222 0.753731343283582 0.4297872340425532 134.0
18 4.0 0.01 0.007832183152590644 0.5642458100558659 0.3607142857142857 179.0
19 4.5 0.01 0.003332051774958349 0.3300653594771242 0.24815724815724816 306.0
20 5.0 0.01 0.0017192128650021159 0.18165467625899281 0.1537290715372907 556.0
21 5.5 0.01 0.0007640035685135623 0.09777347531461762 0.0890652557319224 1033.0
22 1.0 0.02 0.09030180699730873 0.9901960784313726 0.4975369458128079 102.0
23 1.5 0.02 0.06522331154684095 0.9351851851851852 0.48325358851674644 108.0
24 2.0 0.02 0.049919050188882895 0.926605504587156 0.48095238095238096 109.0
25 2.5 0.02 0.028994576554025864 0.7163120567375887 0.41735537190082644 141.0
26 3.0 0.02 0.01815435476516188 0.5872093023255814 0.36996336996337 172.0
27 3.5 0.02 0.009278711484593838 0.4007936507936508 0.28611898016997167 252.0
28 4.0 0.02 0.003515158519203359 0.19842829076620824 0.16557377049180327 509.0
29 4.5 0.02 0.0013487523022509087 0.08395677472984206 0.07745398773006135 1203.0
30 5.0 0.02 0.0004013143719026072 0.027747252747252746 0.026998128842555466 3640.0
31 5.5 0.02 0.00016504731356322146 0.011449948985375808 0.011320331764178435 8821.0
32 1.0 0.03 0.09037692747001713 0.9805825242718447 0.4950980392156863 103.0
33 1.5 0.03 0.06437619295505813 0.8938053097345132 0.4719626168224299 113.0
34 2.0 0.03 0.04964273845131273 0.8559322033898306 0.4611872146118721 118.0
35 2.5 0.03 0.026193913148081317 0.6196319018404908 0.38257575757575757 163.0
36 3.0 0.03 0.01603641456582633 0.48095238095238096 0.3247588424437299 210.0
37 3.5 0.03 0.007452113793494276 0.29190751445086704 0.22595078299776286 346.0
38 4.0 0.03 0.0028573605608046325 0.13082901554404144 0.1156930126002291 772.0
39 4.5 0.03 0.0010247036667774454 0.05706214689265537 0.053981827899518976 1770.0
40 5.0 0.03 0.0003044266400985235 0.017819336626676077 0.01750736696134512 5668.0
41 5.5 0.03 0.00012169749196725593 0.007310364794441228 0.007257311202126895 13816.0
42 1.0 0.04 0.09207362190159082 0.9528301886792453 0.48792270531400966 106.0
43 1.5 0.04 0.06675191815856778 0.8782608695652174 0.4675925925925926 115.0
44 2.0 0.04 0.04750233426704015 0.8015873015873016 0.44493392070484583 126.0
45 2.5 0.04 0.026018099547511313 0.5976331360946746 0.37407407407407406 169.0
46 3.0 0.04 0.012881896944824441 0.39147286821705424 0.28133704735376047 258.0
47 3.5 0.04 0.0065840061514802 0.24754901960784315 0.19842829076620824 408.0
48 4.0 0.04 0.0026100649280614337 0.1111111111111111 0.1 909.0
49 4.5 0.04 0.0009278379283402166 0.04791271347248577 0.045722046174739704 2108.0
50 5.0 0.04 0.0002976899261728983 0.016356275303643725 0.016093052899936264 6175.0
51 5.5 0.04 0.00011358050437191844 0.006394023803494555 0.0063534000125809904 15796.0
52 1.0 0.05 0.0947558268590455 0.9528301886792453 0.48792270531400966 106.0
53 1.5 0.05 0.06581415174765558 0.8782608695652174 0.4675925925925926 115.0
54 2.0 0.05 0.047244094488188976 0.7952755905511811 0.44298245614035087 127.0
55 2.5 0.05 0.02558940242763772 0.6011904761904762 0.3754646840148699 168.0
56 3.0 0.05 0.012649629140045532 0.3782771535580524 0.27445652173913043 267.0
57 3.5 0.05 0.00588833094213295 0.20528455284552846 0.1703204047217538 492.0
58 4.0 0.05 0.002538604818842757 0.10316649642492338 0.09351851851851851 979.0
59 4.5 0.05 0.0009202565784678184 0.046933085501858735 0.044829116733244564 2152.0
60 5.0 0.05 0.0002940220301384533 0.01641742522756827 0.01615224692147769 6152.0
61 5.5 0.05 0.00011692575119219662 0.006564409203171715 0.006521598760250533 15386.0
62 1.0 0.06 0.0957732149463559 0.9528301886792453 0.48792270531400966 106.0
63 1.5 0.06 0.06699346405228758 0.8632478632478633 0.463302752293578 117.0
64 2.0 0.06 0.05048633626679018 0.7952755905511811 0.44298245614035087 127.0
65 2.5 0.06 0.026059274284426413 0.5804597701149425 0.36727272727272725 174.0
66 3.0 0.06 0.012814863773224645 0.3782771535580524 0.27445652173913043 267.0
67 3.5 0.06 0.006304073735924525 0.22394678492239467 0.18297101449275363 451.0
68 4.0 0.06 0.0025645685668706205 0.10401647785787847 0.09421641791044776 971.0
69 4.5 0.06 0.0008676426545460951 0.04537286612758311 0.04340352385045122 2226.0
70 5.0 0.06 0.0003190777623385312 0.017672790901137356 0.01736588720770289 5715.0
71 5.5 0.06 0.00012802567341504216 0.00732733604178758 0.00727403673028448 13784.0
72 1.0 0.07 0.09734554199038106 0.9528301886792453 0.48792270531400966 106.0
73 1.5 0.07 0.06668356997971603 0.8706896551724138 0.46543778801843316 116.0
74 2.0 0.07 0.0509344362745098 0.7890625 0.4410480349344978 128.0
75 2.5 0.07 0.026284651791751185 0.5804597701149425 0.36727272727272725 174.0
76 3.0 0.07 0.01309025482852317 0.3782771535580524 0.27445652173913043 267.0
77 3.5 0.07 0.006422119409561578 0.22696629213483147 0.184981684981685 445.0
78 4.0 0.07 0.002640349738129247 0.11147902869757174 0.1002979145978153 906.0
79 4.5 0.07 0.0009502336090686274 0.04931640625 0.04699860400186133 2048.0
80 5.0 0.07 0.0003310118602339736 0.018090632276553824 0.01776917663617171 5583.0
81 5.5 0.07 0.00011619651767974708 0.0065772336545975515 0.006534256323995601 15356.0
82 1.0 0.08 0.09487813817115631 0.9439252336448598 0.4855769230769231 107.0
83 1.5 0.08 0.06786680189317106 0.8706896551724138 0.46543778801843316 116.0
84 2.0 0.08 0.05188536953242836 0.7769230769230769 0.43722943722943725 130.0
85 2.5 0.08 0.027359781121751026 0.5872093023255814 0.36996336996337 172.0
86 3.0 0.08 0.01338979171435111 0.39299610894941633 0.28212290502793297 257.0
87 3.5 0.08 0.006565126050420168 0.22544642857142858 0.18397085610200364 448.0
88 4.0 0.08 0.0026592336280917596 0.11002178649237472 0.09911678115799803 918.0
89 4.5 0.08 0.0010749450642325896 0.05441810344827586 0.0516096065406234 1856.0
90 5.0 0.08 0.00036965605914496947 0.019479267116682738 0.01910707529322739 5185.0
91 5.5 0.08 0.00012361614795691875 0.007035876001393243 0.0069867183176535695 14355.0
92 1.0 0.09 0.09762301146873843 0.9528301886792453 0.48792270531400966 106.0
93 1.5 0.09 0.06987444100447196 0.8859649122807017 0.4697674418604651 114.0
94 2.0 0.09 0.0531496062992126 0.7952755905511811 0.44298245614035087 127.0
95 2.5 0.09 0.028342245989304814 0.6121212121212121 0.37969924812030076 165.0
96 3.0 0.09 0.015068990559186637 0.4156378600823045 0.2936046511627907 243.0
97 3.5 0.09 0.006645114942528735 0.21767241379310345 0.17876106194690267 464.0
98 4.0 0.09 0.0026683622451197774 0.11197339246119734 0.10069790628115653 902.0
99 4.5 0.09 0.0011544262675484394 0.059763313609467454 0.056393076493579004 1690.0
100 5.0 0.09 0.000323066434669353 0.017471025774087528 0.01717103026181571 5781.0
101 5.5 0.09 0.00013994339191245457 0.008122235625251308 0.008056796426292279 12435.0
102 1.0 0.1 0.09863478101520982 0.9439252336448598 0.4855769230769231 107.0
103 1.5 0.1 0.07107843137254902 0.9017857142857143 0.47417840375586856 112.0
104 2.0 0.1 0.0548406862745098 0.7890625 0.4410480349344978 128.0
105 2.5 0.1 0.030971479500891266 0.6558441558441559 0.396078431372549 154.0
106 3.0 0.1 0.013721912122513238 0.3686131386861314 0.2693333333333333 274.0
107 3.5 0.1 0.007117601138816182 0.23653395784543327 0.19128787878787878 427.0
108 4.0 0.1 0.003256827969932857 0.1377899045020464 0.1211031175059952 733.0
109 4.5 0.1 0.0010676841344484178 0.04834849210148397 0.046118721461187215 2089.0
110 5.0 0.1 0.000391496273499491 0.02001585414189457 0.01962308140664465 5046.0
111 5.5 0.1 0.00013110636765910645 0.007300852970941159 0.007247936849659132 13834.0
112 1.0 0.11 0.09868059373282023 0.9439252336448598 0.4855769230769231 107.0
113 1.5 0.11 0.07357317927170869 0.9017857142857143 0.47417840375586856 112.0
114 2.0 0.11 0.05273321449792038 0.7651515151515151 0.4334763948497854 132.0
115 2.5 0.11 0.033249484004127965 0.6644736842105263 0.39920948616600793 152.0
116 3.0 0.11 0.014938457134678689 0.3686131386861314 0.2693333333333333 274.0
117 3.5 0.11 0.00837662033787262 0.24105011933174225 0.19423076923076923 419.0
118 4.0 0.11 0.0035059537469083863 0.13133940182054615 0.11609195402298851 769.0
119 4.5 0.11 0.0011208468207017151 0.04791271347248577 0.045722046174739704 2108.0
120 5.0 0.11 0.00043153577768256107 0.021019771071800208 0.02058703628210355 4805.0
121 5.5 0.11 0.00015734593947451842 0.00792654214409041 0.007864206182356148 12742.0
122 1.0 0.12 0.12740384615384615 0.9711538461538461 0.4926829268292683 104.0
123 1.5 0.12 0.10430283224400871 0.9351851851851852 0.48325358851674644 108.0
124 2.0 0.12 0.08233632436025258 0.8559322033898306 0.4611872146118721 118.0
125 2.5 0.12 0.06005809731299928 0.7481481481481481 0.4279661016949153 135.0
126 3.0 0.12 0.03891090114802398 0.5233160621761658 0.3435374149659864 193.0
127 3.5 0.12 0.022793232816592342 0.36462093862815886 0.2671957671957672 277.0
128 4.0 0.12 0.010509303903710847 0.18397085610200364 0.15538461538461537 549.0
129 4.5 0.12 0.006082062454611474 0.10390946502057613 0.09412861136999068 972.0
130 5.0 0.12 0.0030410982709184117 0.04972919743968488 0.0473733583489681 2031.0
131 5.5 0.12 0.0012914918514713017 0.021002287377833228 0.020370747606437156 4809.0
132 1.0 0.13 0.20258202290817318 1.0 0.5 101.0
133 1.5 0.13 0.1892350999805863 1.0 0.5 101.0
134 2.0 0.13 0.1705788084464555 0.9711538461538461 0.4926829268292683 104.0
135 2.5 0.13 0.16332233828110684 0.9439252336448598 0.4855769230769231 107.0
136 3.0 0.13 0.13447583757912615 0.7952755905511811 0.44298245614035087 127.0
137 3.5 0.13 0.12821350762527234 0.7481481481481481 0.4279661016949153 135.0
138 4.0 0.13 0.09359325125398997 0.5872093023255814 0.36996336996337 172.0
139 4.5 0.13 0.07852299798424042 0.4719626168224299 0.32063492063492066 214.0
140 5.0 0.13 0.061255361519607844 0.39453125 0.28291316526610644 256.0
141 5.5 0.13 0.057405013287366226 0.36996336996337 0.2700534759358289 273.0
142 1.0 0.14 0.30188312948941953 1.0 0.5 101.0
143 1.5 0.14 0.2989225393127548 1.0 0.5 101.0
144 2.0 0.14 0.2915938652688798 1.0 0.5 101.0
145 2.5 0.14 0.2830161476355248 0.9901960784313726 0.4975369458128079 102.0
146 3.0 0.14 0.26663838612368024 0.9711538461538461 0.4926829268292683 104.0
147 3.5 0.14 0.26035367417995237 0.9439252336448598 0.4855769230769231 107.0
148 4.0 0.14 0.2349137931034483 0.8706896551724138 0.46543778801843316 116.0
149 4.5 0.14 0.22372227579556414 0.8278688524590164 0.452914798206278 122.0
150 5.0 0.14 0.22702965483714146 0.8347107438016529 0.45495495495495497 121.0
151 5.5 0.14 0.20850433196534426 0.7829457364341085 0.4391304347826087 129.0
152 1.0 0.15 0.37036497767423804 1.0 0.5 101.0
153 1.5 0.15 0.36036691904484563 1.0 0.5 101.0
154 2.0 0.15 0.35255290234905845 1.0 0.5 101.0
155 2.5 0.15 0.3493981751116288 1.0 0.5 101.0
156 3.0 0.15 0.3480392156862745 0.9901960784313726 0.4975369458128079 102.0
157 3.5 0.15 0.34475537787930705 0.9805825242718447 0.4950980392156863 103.0
158 4.0 0.15 0.34837235865219873 0.9805825242718447 0.4950980392156863 103.0
159 4.5 0.15 0.3258258258258258 0.9099099099099099 0.47641509433962265 111.0
160 5.0 0.15 0.3237076648841355 0.9181818181818182 0.4786729857819905 110.0
161 5.5 0.15 0.30232947880006705 0.8632478632478633 0.463302752293578 117.0
162 1.0 0.16 0.40870704717530576 1.0 0.5 101.0
163 1.5 0.16 0.4099204038050864 1.0 0.5 101.0
164 2.0 0.16 0.39845251826220685 0.9901960784313726 0.4975369458128079 102.0
165 2.5 0.16 0.39511745292176276 1.0 0.5 101.0
166 3.0 0.16 0.4057949912638323 1.0 0.5 101.0
167 3.5 0.16 0.40695981362842165 1.0 0.5 101.0
168 4.0 0.16 0.4024842946887493 0.9805825242718447 0.4950980392156863 103.0
169 4.5 0.16 0.3882447209653092 0.9711538461538461 0.4926829268292683 104.0
170 5.0 0.16 0.4009515570934256 0.9901960784313726 0.4975369458128079 102.0
171 5.5 0.16 0.37219251336898396 0.9181818181818182 0.4786729857819905 110.0

171
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_20433484.csv Normal file
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@ -0,0 +1,171 @@
gamma,omega,BER,FER,DFR,num_iterations
0.0,0.01,0.05605707629586488,1.0,0.5,101.0
0.0,0.03111111111111111,0.05605707629586488,1.0,0.5,101.0
0.0,0.052222222222222225,0.05605707629586488,1.0,0.5,101.0
0.0,0.07333333333333333,0.05605707629586488,1.0,0.5,101.0
0.0,0.09444444444444444,0.05605707629586488,1.0,0.5,101.0
0.0,0.11555555555555555,0.05605707629586488,1.0,0.5,101.0
0.0,0.1366666666666667,0.05605707629586488,1.0,0.5,101.0
0.0,0.1577777777777778,0.05605707629586488,1.0,0.5,101.0
0.0,0.1788888888888889,0.05605707629586488,1.0,0.5,101.0
0.0,0.2,0.05605707629586488,1.0,0.5,101.0
0.01,0.01,0.010263480392156863,0.63125,0.38697318007662834,160.0
0.01,0.03111111111111111,0.009983261597321855,0.6158536585365854,0.38113207547169814,164.0
0.01,0.052222222222222225,0.01201661220043573,0.7013888888888888,0.4122448979591837,144.0
0.01,0.07333333333333333,0.01569451309935739,0.8487394957983193,0.4590909090909091,119.0
0.01,0.09444444444444444,0.020372369131138695,0.926605504587156,0.48095238095238096,109.0
0.01,0.11555555555555555,0.02503331429659242,0.9805825242718447,0.4950980392156863,103.0
0.01,0.1366666666666667,0.028246942341292953,1.0,0.5,101.0
0.01,0.1577777777777778,0.029751504562220928,1.0,0.5,101.0
0.01,0.1788888888888889,0.0320326150262085,1.0,0.5,101.0
0.01,0.2,0.03344010871675403,1.0,0.5,101.0
0.02,0.01,0.005370975550714112,0.3117283950617284,0.2376470588235294,324.0
0.02,0.03111111111111111,0.005261342073486286,0.2961876832844575,0.22850678733031674,341.0
0.02,0.052222222222222225,0.005845293443838016,0.3470790378006873,0.2576530612244898,291.0
0.02,0.07333333333333333,0.007680491551459293,0.46543778801843316,0.31761006289308175,217.0
0.02,0.09444444444444444,0.010345242391435102,0.6196319018404908,0.38257575757575757,163.0
0.02,0.11555555555555555,0.012752202330207445,0.7318840579710145,0.4225941422594142,138.0
0.02,0.1366666666666667,0.01598972922502334,0.8015873015873016,0.44493392070484583,126.0
0.02,0.1577777777777778,0.015645424836601307,0.8416666666666667,0.45701357466063347,120.0
0.02,0.1788888888888889,0.01845963610669493,0.9099099099099099,0.47641509433962265,111.0
0.02,0.2,0.02106900452488688,0.9711538461538461,0.4926829268292683,104.0
0.03,0.01,0.0043133891635556455,0.1732418524871355,0.1476608187134503,583.0
0.03,0.03111111111111111,0.0038214210415348933,0.18100358422939067,0.15326251896813353,558.0
0.03,0.052222222222222225,0.0045674986412475435,0.21535181236673773,0.17719298245614035,469.0
0.03,0.07333333333333333,0.0048556065452185606,0.23877068557919623,0.19274809160305342,423.0
0.03,0.09444444444444444,0.005489622749684669,0.2953216374269006,0.22799097065462753,342.0
0.03,0.11555555555555555,0.0064074318866123225,0.3782771535580524,0.27445652173913043,267.0
0.03,0.1366666666666667,0.008169934640522876,0.5024875621890548,0.3344370860927152,201.0
0.03,0.1577777777777778,0.009523809523809525,0.5771428571428572,0.36594202898550726,175.0
0.03,0.1788888888888889,0.010731319554848967,0.6824324324324325,0.40562248995983935,148.0
0.03,0.2,0.011946259985475672,0.7481481481481481,0.4279661016949153,135.0
0.04,0.01,0.003099387903309472,0.1335978835978836,0.11785297549591599,756.0
0.04,0.03111111111111111,0.0026726771110454233,0.12688442211055276,0.11259754738015608,796.0
0.04,0.052222222222222225,0.0027389545896687657,0.13185378590078328,0.11649365628604383,766.0
0.04,0.07333333333333333,0.003074602622113934,0.14940828402366865,0.12998712998713,676.0
0.04,0.09444444444444444,0.0032184591008120418,0.17003367003367004,0.14532374100719425,594.0
0.04,0.11555555555555555,0.004746532759445241,0.24634146341463414,0.19765166340508805,410.0
0.04,0.1366666666666667,0.005296371422132071,0.29022988505747127,0.22494432071269488,348.0
0.04,0.1577777777777778,0.005001324854266031,0.34121621621621623,0.25440806045340053,296.0
0.04,0.1788888888888889,0.00546448087431694,0.4139344262295082,0.2927536231884058,244.0
0.04,0.2,0.006238859180035651,0.48325358851674644,0.3258064516129032,209.0
0.05,0.01,0.0029508713250442327,0.12484548825710753,0.11098901098901098,809.0
0.05,0.03111111111111111,0.0025587157940099117,0.11098901098901098,0.09990108803165183,910.0
0.05,0.052222222222222225,0.0025485604077696653,0.11826697892271663,0.10575916230366492,854.0
0.05,0.07333333333333333,0.002834868887313962,0.13520749665327977,0.11910377358490566,747.0
0.05,0.09444444444444444,0.002904865649963689,0.14387464387464388,0.12577833125778332,702.0
0.05,0.11555555555555555,0.003455091714104997,0.1629032258064516,0.14008321775312066,620.0
0.05,0.1366666666666667,0.0035884256615267467,0.19611650485436893,0.16396103896103897,515.0
0.05,0.1577777777777778,0.005423445974134335,0.3069908814589666,0.23488372093023255,329.0
0.05,0.1788888888888889,0.005476673427991886,0.3482758620689655,0.25831202046035806,290.0
0.05,0.2,0.006066176470588235,0.42083333333333334,0.2961876832844575,240.0
0.06,0.01,0.003799539617827329,0.14285714285714285,0.125,707.0
0.06,0.03111111111111111,0.0035610656818781325,0.15097159940209268,0.13116883116883116,669.0
0.06,0.052222222222222225,0.0026533018867924527,0.11910377358490566,0.10642781875658588,848.0
0.06,0.07333333333333333,0.002868798526121858,0.13557046979865772,0.11938534278959811,745.0
0.06,0.09444444444444444,0.00360244769048559,0.16946308724832215,0.1449067431850789,596.0
0.06,0.11555555555555555,0.00398463478958835,0.1968810916179337,0.16449511400651465,513.0
0.06,0.1366666666666667,0.004498013013668719,0.22494432071269488,0.18363636363636363,449.0
0.06,0.1577777777777778,0.005537400145243282,0.2671957671957672,0.21085594989561587,378.0
0.06,0.1788888888888889,0.005809731299927378,0.3117283950617284,0.2376470588235294,324.0
0.06,0.2,0.00437871777924653,0.28370786516853935,0.2210065645514223,356.0
0.07,0.01,0.003593386507145231,0.1426553672316384,0.12484548825710753,708.0
0.07,0.03111111111111111,0.0036045997346306944,0.1518796992481203,0.13185378590078328,665.0
0.07,0.052222222222222225,0.003368306386882238,0.1476608187134503,0.1286624203821656,684.0
0.07,0.07333333333333333,0.003258233248590047,0.15052160953800298,0.13082901554404144,671.0
0.07,0.09444444444444444,0.0027373803149796203,0.12423124231242312,0.11050328227571116,813.0
0.07,0.11555555555555555,0.0033504227379025004,0.154434250764526,0.1337748344370861,654.0
0.07,0.1366666666666667,0.0028061949417448138,0.1463768115942029,0.1276864728192162,690.0
0.07,0.1577777777777778,0.004031153379515479,0.20159680638722555,0.16777408637873753,501.0
0.07,0.1788888888888889,0.003642356300121762,0.21627408993576017,0.17781690140845072,467.0
0.07,0.2,0.0047292629789042985,0.27371273712737126,0.2148936170212766,369.0
0.08,0.01,0.0035304980718048992,0.13988919667590027,0.1227217496962333,722.0
0.08,0.03111111111111111,0.0028546712802768165,0.1188235294117647,0.10620399579390116,850.0
0.08,0.052222222222222225,0.003199444915580911,0.13237221494102228,0.11689814814814815,763.0
0.08,0.07333333333333333,0.0032657293686705452,0.13873626373626374,0.1218335343787696,728.0
0.08,0.09444444444444444,0.003349673202614379,0.14027777777777778,0.12302070645554203,720.0
0.08,0.11555555555555555,0.0032287950441107333,0.14245416078984485,0.12469135802469136,709.0
0.08,0.1366666666666667,0.0033313612801442417,0.14511494252873564,0.12672521957340024,696.0
0.08,0.1577777777777778,0.003219198127011999,0.16749585406301823,0.1434659090909091,603.0
0.08,0.1788888888888889,0.004221132897603486,0.2003968253968254,0.1669421487603306,504.0
0.08,0.2,0.004492541937768335,0.22799097065462753,0.18566176470588236,443.0
0.09,0.01,0.004228041462084015,0.16530278232405893,0.14185393258426968,611.0
0.09,0.03111111111111111,0.0034993766292644225,0.14595375722543352,0.1273644388398487,692.0
0.09,0.052222222222222225,0.0027051793828927217,0.1293213828425096,0.1145124716553288,781.0
0.09,0.07333333333333333,0.0026554687680007375,0.11868390129259694,0.10609243697478991,851.0
0.09,0.09444444444444444,0.003309692671394799,0.14326241134751774,0.12531017369727046,705.0
0.09,0.11555555555555555,0.0034654345121734494,0.15256797583081572,0.13237221494102228,662.0
0.09,0.1366666666666667,0.003610750293808375,0.15930599369085174,0.13741496598639455,634.0
0.09,0.1577777777777778,0.0032784313725490195,0.1616,0.13911845730027547,625.0
0.09,0.1788888888888889,0.0034852593519677376,0.17907801418439717,0.1518796992481203,564.0
0.09,0.2,0.003954441737247167,0.1992110453648915,0.16611842105263158,507.0
0.1,0.01,0.003872977762533585,0.14723032069970846,0.12833545108005082,686.0
0.1,0.03111111111111111,0.0034564190639759552,0.14744525547445256,0.12849872773536897,685.0
0.1,0.052222222222222225,0.003060047548431137,0.1212484993997599,0.10813704496788008,833.0
0.1,0.07333333333333333,0.002834639677345712,0.13065976714100905,0.11556064073226545,773.0
0.1,0.09444444444444444,0.003000338066260987,0.14511494252873564,0.12672521957340024,696.0
0.1,0.11555555555555555,0.002985965959988056,0.1281725888324873,0.11361079865016872,788.0
0.1,0.1366666666666667,0.0035962634673351837,0.1537290715372907,0.13324538258575197,657.0
0.1,0.1577777777777778,0.003800032409658078,0.1669421487603306,0.14305949008498584,605.0
0.1,0.1788888888888889,0.004281459419210722,0.18264014466546113,0.154434250764526,553.0
0.1,0.2,0.003902102357188363,0.18231046931407943,0.15419847328244274,554.0
0.11,0.01,0.0056122627942053385,0.1806797853309481,0.15303030303030302,559.0
0.11,0.03111111111111111,0.0036155863619333085,0.1286624203821656,0.11399548532731377,785.0
0.11,0.052222222222222225,0.003906775330159499,0.13854595336076816,0.1216867469879518,729.0
0.11,0.07333333333333333,0.0028435859469645984,0.11556064073226545,0.10358974358974359,874.0
0.11,0.09444444444444444,0.0035318062095937427,0.13704206241519673,0.12052505966587113,737.0
0.11,0.11555555555555555,0.0028003282761068227,0.11425339366515837,0.10253807106598985,884.0
0.11,0.1366666666666667,0.004059673404965701,0.1563467492260062,0.13520749665327977,646.0
0.11,0.1577777777777778,0.003967909172531642,0.14918759231905465,0.12982005141388175,677.0
0.11,0.1788888888888889,0.004008681785967399,0.15210843373493976,0.13202614379084968,664.0
0.11,0.2,0.004847393502855688,0.18738404452690166,0.1578125,539.0
0.12,0.01,0.05422016522344979,0.5126903553299492,0.3389261744966443,197.0
0.12,0.03111111111111111,0.01857856003293706,0.2650918635170604,0.2095435684647303,381.0
0.12,0.052222222222222225,0.010622578970488399,0.20570264765784113,0.17060810810810811,491.0
0.12,0.07333333333333333,0.005262501040019968,0.14285714285714285,0.125,707.0
0.12,0.09444444444444444,0.006019951840385277,0.16108452950558214,0.13873626373626374,627.0
0.12,0.11555555555555555,0.004924242424242424,0.15303030303030302,0.13272010512483573,660.0
0.12,0.1366666666666667,0.004550182007280291,0.15756630265210608,0.13611859838274934,641.0
0.12,0.1577777777777778,0.004542923297869222,0.15097159940209268,0.13116883116883116,669.0
0.12,0.1788888888888889,0.005475768902878877,0.1688963210702341,0.14449213161659513,598.0
0.12,0.2,0.005478140623337943,0.18264014466546113,0.154434250764526,553.0
0.13,0.01,0.15323304894476875,0.7709923664122137,0.4353448275862069,131.0
0.13,0.03111111111111111,0.11764705882352941,0.7890625,0.4410480349344978,128.0
0.13,0.052222222222222225,0.060421994884910485,0.5489130434782609,0.3543859649122807,184.0
0.13,0.07333333333333333,0.03125234768236797,0.38697318007662834,0.27900552486187846,261.0
0.13,0.09444444444444444,0.014289890268033819,0.231651376146789,0.18808193668528864,436.0
0.13,0.11555555555555555,0.007499581028992793,0.17264957264957265,0.14723032069970846,585.0
0.13,0.1366666666666667,0.007408285895003162,0.1629032258064516,0.14008321775312066,620.0
0.13,0.1577777777777778,0.008108579400139503,0.19960474308300397,0.16639209225700163,506.0
0.13,0.1788888888888889,0.006423786363219235,0.15756630265210608,0.13611859838274934,641.0
0.13,0.2,0.008233762254901961,0.197265625,0.16476345840130505,512.0
0.14,0.01,0.28930322128851543,0.9017857142857143,0.47417840375586856,112.0
0.14,0.03111111111111111,0.23302139037433156,0.9181818181818182,0.4786729857819905,110.0
0.14,0.052222222222222225,0.1891891891891892,0.9099099099099099,0.47641509433962265,111.0
0.14,0.07333333333333333,0.09920116194625998,0.7481481481481481,0.4279661016949153,135.0
0.14,0.09444444444444444,0.05906329923273657,0.5489130434782609,0.3543859649122807,184.0
0.14,0.11555555555555555,0.030531688330763015,0.3782771535580524,0.27445652173913043,267.0
0.14,0.1366666666666667,0.01474056603773585,0.23820754716981132,0.19238095238095237,424.0
0.14,0.1577777777777778,0.01051953628166595,0.1843065693430657,0.15562403697996918,548.0
0.14,0.1788888888888889,0.012323529411764705,0.202,0.16805324459234608,500.0
0.14,0.2,0.010869155354449472,0.19423076923076923,0.16264090177133655,520.0
0.15,0.01,0.39162821991541713,0.9901960784313726,0.4975369458128079,102.0
0.15,0.03111111111111111,0.32715874811463047,0.9711538461538461,0.4926829268292683,104.0
0.15,0.052222222222222225,0.26841671247938426,0.9439252336448598,0.4855769230769231,107.0
0.15,0.07333333333333333,0.2164724380318165,0.9528301886792453,0.48792270531400966,106.0
0.15,0.09444444444444444,0.14056272915670334,0.8211382113821138,0.45089285714285715,123.0
0.15,0.11555555555555555,0.09142726858185135,0.7829457364341085,0.4391304347826087,129.0
0.15,0.1366666666666667,0.05813605930176949,0.6158536585365854,0.38113207547169814,164.0
0.15,0.1577777777777778,0.024019607843137256,0.33666666666666667,0.2518703241895262,300.0
0.15,0.1788888888888889,0.01903114186851211,0.3126934984520124,0.23820754716981132,323.0
0.15,0.2,0.017055582563603955,0.27823691460055094,0.21767241379310345,363.0
0.16,0.01,0.4491360900795962,1.0,0.5,101.0
0.16,0.03111111111111111,0.3931372549019608,0.9619047619047619,0.49029126213592233,105.0
0.16,0.052222222222222225,0.33913084464555054,0.9711538461538461,0.4926829268292683,104.0
0.16,0.07333333333333333,0.28846153846153844,0.9711538461538461,0.4926829268292683,104.0
0.16,0.09444444444444444,0.23878858474082704,1.0,0.5,101.0
0.16,0.11555555555555555,0.1638655462184874,0.9619047619047619,0.49029126213592233,105.0
0.16,0.1366666666666667,0.11498108015135879,0.8859649122807017,0.4697674418604651,114.0
0.16,0.1577777777777778,0.07070813937085625,0.7266187050359713,0.42083333333333334,139.0
0.16,0.1788888888888889,0.06163884472708002,0.6824324324324325,0.40562248995983935,148.0
0.16,0.2,0.05149976657329598,0.6011904761904762,0.3754646840148699,168.0
1 gamma omega BER FER DFR num_iterations
2 0.0 0.01 0.05605707629586488 1.0 0.5 101.0
3 0.0 0.03111111111111111 0.05605707629586488 1.0 0.5 101.0
4 0.0 0.052222222222222225 0.05605707629586488 1.0 0.5 101.0
5 0.0 0.07333333333333333 0.05605707629586488 1.0 0.5 101.0
6 0.0 0.09444444444444444 0.05605707629586488 1.0 0.5 101.0
7 0.0 0.11555555555555555 0.05605707629586488 1.0 0.5 101.0
8 0.0 0.1366666666666667 0.05605707629586488 1.0 0.5 101.0
9 0.0 0.1577777777777778 0.05605707629586488 1.0 0.5 101.0
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8
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_20433484_metadata.json Normal file
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@ -0,0 +1,8 @@
{
"duration": 24.236127668002155,
"name": "2d_BER_FER_DFR_gamma_omega_20433484",
"platform": "Linux-6.2.9-arch1-1-x86_64-with-glibc2.37",
"omega": 0.2,
"K": 100,
"end_time": "2023-04-11 14:43:01.047186"
}

171
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_20455187.csv Normal file
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0.1,0.01,0.45564439367980203,0.9805825242718447,0.4950980392156863,103.0
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0.16,0.2,0.22039409823335274,1.0,0.5,101.0
1 gamma omega BER FER DFR num_iterations
2 0.0 0.01 0.05727043292564551 1.0 0.5 101.0
3 0.0 0.03111111111111111 0.05727043292564551 1.0 0.5 101.0
4 0.0 0.052222222222222225 0.05727043292564551 1.0 0.5 101.0
5 0.0 0.07333333333333333 0.05727043292564551 1.0 0.5 101.0
6 0.0 0.09444444444444444 0.05727043292564551 1.0 0.5 101.0
7 0.0 0.11555555555555555 0.05727043292564551 1.0 0.5 101.0
8 0.0 0.1366666666666667 0.05727043292564551 1.0 0.5 101.0
9 0.0 0.1577777777777778 0.05591147350029121 1.0 0.5 101.0
10 0.0 0.1788888888888889 0.05591147350029121 1.0 0.5 101.0
11 0.0 0.2 0.05591147350029121 1.0 0.5 101.0
12 0.01 0.01 0.015902872777017785 0.5872093023255814 0.36996336996337 172.0
13 0.01 0.03111111111111111 0.012218680575678547 0.4975369458128079 0.33223684210526316 203.0
14 0.01 0.052222222222222225 0.01411764705882353 0.5771428571428572 0.36594202898550726 175.0
15 0.01 0.07333333333333333 0.016250335750738653 0.6917808219178082 0.4089068825910931 146.0
16 0.01 0.09444444444444444 0.023618538324420676 0.8347107438016529 0.45495495495495497 121.0
17 0.01 0.11555555555555555 0.025997899159663867 0.9017857142857143 0.47417840375586856 112.0
18 0.01 0.1366666666666667 0.027970011534025375 0.9901960784313726 0.4975369458128079 102.0
19 0.01 0.1577777777777778 0.03323996265172736 0.9619047619047619 0.49029126213592233 105.0
20 0.01 0.1788888888888889 0.032487504805843906 0.9901960784313726 0.4975369458128079 102.0
21 0.01 0.2 0.03330449826989619 0.9901960784313726 0.4975369458128079 102.0
22 0.02 0.01 0.008835633018639554 0.24938271604938272 0.19960474308300397 405.0
23 0.02 0.03111111111111111 0.0065020347761746205 0.19056603773584907 0.1600633914421553 530.0
24 0.02 0.052222222222222225 0.007230392156862745 0.21041666666666667 0.1738382099827883 480.0
25 0.02 0.07333333333333333 0.007727652464494569 0.2531328320802005 0.202 399.0
26 0.02 0.09444444444444444 0.009287101422038464 0.3226837060702875 0.24396135265700483 313.0
27 0.02 0.11555555555555555 0.011719106247150023 0.4697674418604651 0.31962025316455694 215.0
28 0.02 0.1366666666666667 0.015576625386996903 0.6644736842105263 0.39920948616600793 152.0
29 0.02 0.1577777777777778 0.016013071895424835 0.7481481481481481 0.4279661016949153 135.0
30 0.02 0.1788888888888889 0.020844373785550256 0.9099099099099099 0.47641509433962265 111.0
31 0.02 0.2 0.021200980392156864 0.8416666666666667 0.45701357466063347 120.0
32 0.03 0.01 0.008084908171413361 0.22850678733031674 0.1860036832412523 442.0
33 0.03 0.03111111111111111 0.00662105119825708 0.1753472222222222 0.14918759231905465 576.0
34 0.03 0.052222222222222225 0.006201841759067845 0.16134185303514376 0.13892709766162312 626.0
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8
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_20455187_metadata.json Normal file
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@ -0,0 +1,8 @@
{
"duration": 15.688874234008836,
"name": "2d_BER_FER_DFR_gamma_omega_20455187",
"platform": "Linux-6.2.9-arch1-1-x86_64-with-glibc2.37",
"omega": 0.2,
"K": 100,
"end_time": "2023-04-11 14:38:19.211955"
}

171
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_40833844.csv Normal file
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@ -0,0 +1,171 @@
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0.07,0.2,0.004321613702952446,0.46206896551724136,0.3160377358490566,435.0
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0.08,0.2,0.00457445909398242,0.4331896551724138,0.3022556390977444,464.0
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0.09,0.03111111111111111,0.002510470207500476,0.19514563106796118,0.16328188464662877,1030.0
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0.09,0.1577777777777778,0.0035289747399702824,0.2986627043090639,0.2299771167048055,673.0
0.09,0.1788888888888889,0.0032863746099040217,0.30180180180180183,0.23183391003460208,666.0
0.09,0.2,0.0042750754147812976,0.38653846153846155,0.2787794729542302,520.0
0.1,0.01,0.002633400688519683,0.191793893129771,0.16092874299439552,1048.0
0.1,0.03111111111111111,0.002335409252669039,0.17882562277580072,0.15169811320754717,1124.0
0.1,0.052222222222222225,0.0022536742782980814,0.17978533094812166,0.15238817285822592,1118.0
0.1,0.07333333333333333,0.0021895424836601307,0.17866666666666667,0.1515837104072398,1125.0
0.1,0.09444444444444444,0.0024094797281462386,0.20019920318725098,0.16680497925311202,1004.0
0.1,0.11555555555555555,0.002564427277907934,0.2163616792249731,0.17787610619469027,929.0
0.1,0.1366666666666667,0.002891739928048984,0.244228432563791,0.1962890625,823.0
0.1,0.1577777777777778,0.0033908549869812424,0.27235772357723576,0.21405750798722045,738.0
0.1,0.1788888888888889,0.0038150676937441642,0.29910714285714285,0.23024054982817868,672.0
0.1,0.2,0.004031519149715961,0.3130841121495327,0.23843416370106763,642.0
0.11,0.01,0.0039674349271872494,0.23508771929824562,0.1903409090909091,855.0
0.11,0.03111111111111111,0.003186004280433232,0.22160970231532526,0.18140794223826714,907.0
0.11,0.052222222222222225,0.0027327839348299707,0.19420289855072465,0.16262135922330098,1035.0
0.11,0.07333333333333333,0.0027907434484309977,0.1921606118546845,0.161186848436247,1046.0
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0.11,0.11555555555555555,0.0037528727201603836,0.2506234413965087,0.20039880358923232,802.0
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0.12,0.01,0.053483893557422967,0.8973214285714286,0.47294117647058825,224.0
0.12,0.03111111111111111,0.02188282227784731,0.5345744680851063,0.3483535528596187,376.0
0.12,0.052222222222222225,0.008591898297780651,0.36813186813186816,0.26907630522088355,546.0
0.12,0.07333333333333333,0.005797635676962077,0.26517150395778366,0.20959332638164754,758.0
0.12,0.09444444444444444,0.005476918699765275,0.2430471584038694,0.19552529182879377,827.0
0.12,0.11555555555555555,0.005837617823479006,0.2583547557840617,0.20531154239019409,778.0
0.12,0.1366666666666667,0.005816993464052287,0.268,0.2113564668769716,750.0
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0.12,0.1788888888888889,0.006150308042872817,0.2883787661406026,0.22383073496659242,697.0
0.12,0.2,0.008250316255534473,0.3241935483870968,0.24482338611449453,620.0
0.13,0.01,0.1625856653340948,0.9757281553398058,0.49385749385749383,206.0
0.13,0.03111111111111111,0.10832847990681421,0.995049504950495,0.4987593052109181,202.0
0.13,0.052222222222222225,0.06770255271920089,0.9481132075471698,0.48668280871670705,212.0
0.13,0.07333333333333333,0.03828566999922499,0.7944664031620553,0.44273127753303965,253.0
0.13,0.09444444444444444,0.021908624173903546,0.5598885793871866,0.35892857142857143,359.0
0.13,0.11555555555555555,0.011153803626713843,0.37781954887218044,0.2742155525238745,532.0
0.13,0.1366666666666667,0.009513260432378079,0.32211538461538464,0.24363636363636362,624.0
0.13,0.1577777777777778,0.009041310022460997,0.3111455108359133,0.23730814639905548,646.0
0.13,0.1788888888888889,0.008986928104575163,0.3087557603686636,0.23591549295774647,651.0
0.13,0.2,0.010640841702534673,0.32682926829268294,0.24632352941176472,615.0
0.14,0.01,0.2847990681421083,0.995049504950495,0.4987593052109181,202.0
0.14,0.03111111111111111,0.22174665886255,1.0,0.5,201.0
0.14,0.052222222222222225,0.15387788778877887,0.995049504950495,0.4987593052109181,202.0
0.14,0.07333333333333333,0.10268762677484787,0.9901477832512315,0.4975247524752475,203.0
0.14,0.09444444444444444,0.061569535221496004,0.9305555555555556,0.48201438848920863,216.0
0.14,0.11555555555555555,0.038532626165220185,0.8237704918032787,0.451685393258427,244.0
0.14,0.1366666666666667,0.027772257551669316,0.6790540540540541,0.4044265593561368,296.0
0.14,0.1577777777777778,0.024174931600547196,0.5843023255813954,0.3688073394495413,344.0
0.14,0.1788888888888889,0.022825167802970246,0.5417789757412399,0.3513986013986014,371.0
0.14,0.2,0.02494146912496342,0.6,0.375,335.0
0.15,0.01,0.37219539557116377,1.0,0.5,201.0
0.15,0.03111111111111111,0.3050068286020876,1.0,0.5,201.0
0.15,0.052222222222222225,0.23707443176275486,1.0,0.5,201.0
0.15,0.07333333333333333,0.17860609590370802,0.995049504950495,0.4987593052109181,202.0
0.15,0.09444444444444444,0.10801217038539554,0.9901477832512315,0.4975247524752475,203.0
0.15,0.11555555555555555,0.072039600153787,0.9852941176470589,0.4962962962962963,204.0
0.15,0.1366666666666667,0.05345022624434389,0.9663461538461539,0.49144254278728605,208.0
0.15,0.1577777777777778,0.04859817230059445,0.9095022624434389,0.476303317535545,221.0
0.15,0.1788888888888889,0.04346760443307758,0.8739130434782608,0.46635730858468677,230.0
0.15,0.2,0.041559167526659786,0.881578947368421,0.46853146853146854,228.0
0.16,0.01,0.4465905765291191,1.0,0.5,201.0
0.16,0.03111111111111111,0.3802311969563945,1.0,0.5,201.0
0.16,0.052222222222222225,0.31822505121451566,1.0,0.5,201.0
0.16,0.07333333333333333,0.25965759438103597,1.0,0.5,201.0
0.16,0.09444444444444444,0.18392108087015901,1.0,0.5,201.0
0.16,0.11555555555555555,0.1284143010438006,1.0,0.5,201.0
0.16,0.1366666666666667,0.08505267778753292,1.0,0.5,201.0
0.16,0.1577777777777778,0.07155164045816347,0.995049504950495,0.4987593052109181,202.0
0.16,0.1788888888888889,0.06805717336439526,0.995049504950495,0.4987593052109181,202.0
0.16,0.2,0.06529940407535563,0.9852941176470589,0.4962962962962963,204.0
1 gamma omega BER FER DFR num_iterations
2 0.0 0.01 0.055860403863037755 1.0 0.5 201.0
3 0.0 0.03111111111111111 0.055860403863037755 1.0 0.5 201.0
4 0.0 0.052222222222222225 0.055860403863037755 1.0 0.5 201.0
5 0.0 0.07333333333333333 0.055860403863037755 1.0 0.5 201.0
6 0.0 0.09444444444444444 0.055860403863037755 1.0 0.5 201.0
7 0.0 0.11555555555555555 0.055860403863037755 1.0 0.5 201.0
8 0.0 0.1366666666666667 0.055860403863037755 1.0 0.5 201.0
9 0.0 0.1577777777777778 0.055860403863037755 1.0 0.5 201.0
10 0.0 0.1788888888888889 0.05605550677982636 1.0 0.5 201.0
11 0.0 0.2 0.05605550677982636 1.0 0.5 201.0
12 0.01 0.01 0.009684845598158291 0.8137651821862348 0.4486607142857143 247.0
13 0.01 0.03111111111111111 0.00930969095457113 0.8271604938271605 0.4527027027027027 243.0
14 0.01 0.052222222222222225 0.011482070305599718 0.9054054054054054 0.475177304964539 222.0
15 0.01 0.07333333333333333 0.015622025509232819 0.9757281553398058 0.49385749385749383 206.0
16 0.01 0.09444444444444444 0.01958357600465929 0.995049504950495 0.4987593052109181 202.0
17 0.01 0.11555555555555555 0.02375752281110464 0.995049504950495 0.4987593052109181 202.0
18 0.01 0.1366666666666667 0.026594966344746854 1.0 0.5 201.0
19 0.01 0.1577777777777778 0.029070334601502292 1.0 0.5 201.0
20 0.01 0.1788888888888889 0.030594576138913275 1.0 0.5 201.0
21 0.01 0.2 0.03235050239001073 1.0 0.5 201.0
22 0.02 0.01 0.00413431591876124 0.44966442953020136 0.3101851851851852 447.0
23 0.02 0.03111111111111111 0.004099145081025902 0.4360086767895879 0.30362537764350456 461.0
24 0.02 0.052222222222222225 0.004636648473822519 0.5180412371134021 0.34125636672325976 388.0
25 0.02 0.07333333333333333 0.005877515479876161 0.6611842105263158 0.39801980198019804 304.0
26 0.02 0.09444444444444444 0.007596093993152817 0.7976190476190477 0.44370860927152317 252.0
27 0.02 0.11555555555555555 0.009533989807376695 0.8854625550660793 0.4696261682242991 227.0
28 0.02 0.1366666666666667 0.012278024417314095 0.9481132075471698 0.48668280871670705 212.0
29 0.02 0.1577777777777778 0.015507164404223228 0.9663461538461539 0.49144254278728605 208.0
30 0.02 0.1788888888888889 0.016720054358377014 0.995049504950495 0.4987593052109181 202.0
31 0.02 0.2 0.020046824700029265 1.0 0.5 201.0
32 0.03 0.01 0.003166461195256546 0.3008982035928144 0.23130034522439585 668.0
33 0.03 0.03111111111111111 0.003215066146940704 0.30271084337349397 0.2323699421965318 664.0
34 0.03 0.052222222222222225 0.0033283428313854277 0.33004926108374383 0.24814814814814815 609.0
35 0.03 0.07333333333333333 0.003935660506502395 0.4127310061601643 0.2921511627906977 487.0
36 0.03 0.09444444444444444 0.004673122320181144 0.49385749385749383 0.3305921052631579 407.0
37 0.03 0.11555555555555555 0.005541659479040883 0.5894428152492669 0.37084870848708484 341.0
38 0.03 0.1366666666666667 0.007061374735767913 0.7472118959107806 0.4276595744680851 269.0
39 0.03 0.1577777777777778 0.008921978833374354 0.8410041841004184 0.45681818181818185 239.0
40 0.03 0.1788888888888889 0.011134770650341585 0.9095022624434389 0.476303317535545 221.0
41 0.03 0.2 0.013509432208902519 0.95260663507109 0.4878640776699029 211.0
42 0.04 0.01 0.002370795646502876 0.21919302071973829 0.17978533094812166 917.0
43 0.04 0.03111111111111111 0.0021605680065693283 0.20531154239019409 0.17033898305084746 979.0
44 0.04 0.052222222222222225 0.002589829035062301 0.24753694581280788 0.19842053307008883 812.0
45 0.04 0.07333333333333333 0.0030437249083609705 0.29821958456973297 0.2297142857142857 674.0
46 0.04 0.09444444444444444 0.003419681330794669 0.3424190800681431 0.2550761421319797 587.0
47 0.04 0.11555555555555555 0.004172743477556312 0.4152892561983471 0.2934306569343066 484.0
48 0.04 0.1366666666666667 0.0046682487175799415 0.49144254278728605 0.3295081967213115 409.0
49 0.04 0.1577777777777778 0.005604422454339991 0.6261682242990654 0.3850574712643678 321.0
50 0.04 0.1788888888888889 0.00720876585928489 0.7389705882352942 0.4249471458773784 272.0
51 0.04 0.2 0.008807257342771133 0.8340248962655602 0.45475113122171945 241.0
52 0.05 0.01 0.002436140954868325 0.20282542885973764 0.1686241610738255 991.0
53 0.05 0.03111111111111111 0.002065052540353158 0.1850828729281768 0.1561771561771562 1086.0
54 0.05 0.052222222222222225 0.0021563675975440682 0.20303030303030303 0.16876574307304787 990.0
55 0.05 0.07333333333333333 0.0024135388585516462 0.23619271445358403 0.19106463878326996 851.0
56 0.05 0.09444444444444444 0.002919343024566148 0.28879310344827586 0.22408026755852842 696.0
57 0.05 0.11555555555555555 0.0033150822108085408 0.3311367380560132 0.24876237623762376 607.0
58 0.05 0.1366666666666667 0.00393296853625171 0.4249471458773784 0.29821958456973297 473.0
59 0.05 0.1577777777777778 0.004607602845059592 0.49264705882352944 0.33004926108374383 408.0
60 0.05 0.1788888888888889 0.0053364836239204825 0.556786703601108 0.35765124555160144 361.0
61 0.05 0.2 0.00593681917211329 0.638095238095238 0.38953488372093026 315.0
62 0.06 0.01 0.0025452488687782806 0.20893970893970895 0.17282889079965605 962.0
63 0.06 0.03111111111111111 0.002332742142082964 0.2020100502512563 0.16806020066889632 995.0
64 0.06 0.052222222222222225 0.002310119450078882 0.21003134796238246 0.17357512953367876 957.0
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8
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_40833844_metadata.json Normal file
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@ -0,0 +1,8 @@
{
"duration": 455.0601770649955,
"name": "2d_BER_FER_DFR_gamma_omega_40833844",
"platform": "Linux-6.2.9-arch1-1-x86_64-with-glibc2.37",
"omega": 0.2,
"K": 100,
"end_time": "2023-04-10 23:23:46.093614"
}

171
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_963965.csv Normal file
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0.07,0.2,0.005889836108908274,0.15939730372720062,0.1374829001367989,1261.0
0.08,0.01,0.006375194476857254,0.11726954492415402,0.10402509147935181,1714.0
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0.08,0.052222222222222225,0.00516545601291364,0.0973365617433414,0.08789752650176678,2065.0
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0.08,0.09444444444444444,0.004979423868312757,0.09925925925925926,0.08906882591093117,2025.0
0.08,0.11555555555555555,0.004792846114256305,0.10344827586206896,0.0912067352666043,1943.0
0.08,0.1366666666666667,0.0048148391045820214,0.10545645330535153,0.09281294621608757,1906.0
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0.08,0.1788888888888889,0.005027228217426059,0.12050359712230216,0.10515021459227468,1668.0
0.08,0.2,0.005208333333333333,0.1401673640167364,0.12024539877300613,1434.0
0.09,0.01,0.005950697586726998,0.11368778280542986,0.098419173890872,1768.0
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0.09,0.052222222222222225,0.004390881913303438,0.09013452914798206,0.07965332232769294,2230.0
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0.09,0.1788888888888889,0.0044405923643211775,0.10323574730354391,0.09315323707498836,1947.0
0.09,0.2,0.0046514675052410906,0.11492281303602059,0.10261672652642381,1749.0
0.1,0.01,0.005661114003123373,0.10463300364393545,0.0938679245283019,1921.0
0.1,0.03111111111111111,0.005501688076416337,0.09930830039525691,0.08951866846603689,2024.0
0.1,0.052222222222222225,0.004990287028227085,0.0956232159847764,0.08688097306689835,2102.0
0.1,0.07333333333333333,0.004872796474358974,0.09663461538461539,0.08811924594476107,2080.0
0.1,0.09444444444444444,0.004969045291625937,0.09824046920821114,0.08945260347129506,2046.0
0.1,0.11555555555555555,0.004904662747019435,0.0984811366976972,0.0896520963425513,2041.0
0.1,0.1366666666666667,0.004690982171283031,0.09598853868194843,0.08758169934640522,2094.0
0.1,0.1577777777777778,0.004813926348547718,0.10425311203319503,0.09441052137153594,1928.0
0.1,0.1788888888888889,0.00581268221574344,0.11720116618075802,0.10490605427974947,1715.0
0.1,0.2,0.0056764613450659964,0.12633563796354494,0.11166945840312674,1591.0
0.11,0.01,0.01036447148651873,0.14387974230493916,0.12523481527864747,1397.0
0.11,0.03111111111111111,0.006901840490797546,0.12331288343558282,0.1092896174863388,1630.0
0.11,0.052222222222222225,0.005310247167868177,0.1035015447991761,0.09337068160597572,1942.0
0.11,0.07333333333333333,0.005294155509783728,0.1035015447991761,0.09337068160597572,1942.0
0.11,0.09444444444444444,0.005773305084745763,0.1135593220338983,0.10152284263959391,1770.0
0.11,0.11555555555555555,0.005545603326256193,0.10668789808917198,0.09640287769784173,1884.0
0.11,0.1366666666666667,0.005319148936170213,0.10691489361702128,0.09615384615384616,1880.0
0.11,0.1577777777777778,0.005288974906567005,0.10731446876668446,0.0964785335262904,1873.0
0.11,0.1788888888888889,0.005815623205054566,0.1154508902929351,0.10303967027305512,1741.0
0.11,0.2,0.006130475015422579,0.12399753238741518,0.10982976386600769,1621.0
0.12,0.01,0.0432893634840871,0.25251256281407036,0.19919517102615694,796.0
0.12,0.03111111111111111,0.021640897212543555,0.1750871080139373,0.14583333333333334,1148.0
0.12,0.052222222222222225,0.012234515589035364,0.12617702448210924,0.10955841252096143,1593.0
0.12,0.07333333333333333,0.007843335127377109,0.10818083961248655,0.0963035019455253,1858.0
0.12,0.09444444444444444,0.0068051890941073,0.10606860158311346,0.09460105112279026,1895.0
0.12,0.11555555555555555,0.005843881856540084,0.10177215189873418,0.09111826967326277,1975.0
0.12,0.1366666666666667,0.006222943722943723,0.10441558441558442,0.09411764705882353,1925.0
0.12,0.1577777777777778,0.0063083692528735635,0.10829741379310345,0.09771511910549344,1856.0
0.12,0.1788888888888889,0.0058741044494720965,0.11368778280542986,0.1016260162601626,1768.0
0.12,0.2,0.00675019425019425,0.11713286713286714,0.10485133020344288,1716.0
0.13,0.01,0.1266993290960452,0.4258474576271186,0.2965722801788376,472.0
0.13,0.03111111111111111,0.08874687304565353,0.3771106941838649,0.2728512960436562,533.0
0.13,0.052222222222222225,0.05693829891838741,0.29646017699115046,0.22779043280182232,678.0
0.13,0.07333333333333333,0.02815573075618854,0.20447609359104782,0.1676545300592718,983.0
0.13,0.09444444444444444,0.015969551282051284,0.15461538461538463,0.1321762349799733,1300.0
0.13,0.11555555555555555,0.010876999158249159,0.1268939393939394,0.11160964666292765,1584.0
0.13,0.1366666666666667,0.007922041503084688,0.11273135165451487,0.09994952044422009,1783.0
0.13,0.1577777777777778,0.007782058094226001,0.10680127523910733,0.09606147934678194,1882.0
0.13,0.1788888888888889,0.007098182145428366,0.10853131749460043,0.09746588693957114,1852.0
0.13,0.2,0.007040676847662142,0.11368778280542986,0.10208227526663281,1768.0
0.14,0.01,0.24378822629969418,0.6146788990825688,0.3806818181818182,327.0
0.14,0.03111111111111111,0.18070409982174687,0.5374331550802139,0.34956521739130436,374.0
0.14,0.052222222222222225,0.12947733918128654,0.4407894736842105,0.3059360730593607,456.0
0.14,0.07333333333333333,0.09663259958071278,0.42138364779874216,0.2943786982248521,477.0
0.14,0.09444444444444444,0.05544689426268374,0.28591749644381226,0.22148394241417496,703.0
0.14,0.11555555555555555,0.030640636497424576,0.22185430463576158,0.1786038077969175,906.0
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0.14,0.1577777777777778,0.012205317982456141,0.13223684210526315,0.1157649796393252,1520.0
0.14,0.1788888888888889,0.00972515590424462,0.12130356065178033,0.10770059235325795,1657.0
0.14,0.2,0.01073602957164601,0.13111545988258316,0.1154068090017311,1533.0
0.15,0.01,0.3570374015748031,0.7913385826771654,0.44175824175824174,254.0
0.15,0.03111111111111111,0.24600103199174406,0.6222910216718266,0.3800383877159309,323.0
0.15,0.052222222222222225,0.20842524509803922,0.5911764705882353,0.37037037037037035,340.0
0.15,0.07333333333333333,0.1732142857142857,0.5742857142857143,0.36363636363636365,350.0
0.15,0.09444444444444444,0.11079545454545454,0.43506493506493504,0.3031674208144796,462.0
0.15,0.11555555555555555,0.07807614133833646,0.3771106941838649,0.2718579234972678,533.0
0.15,0.1366666666666667,0.046538757136583224,0.2648221343873518,0.20855057351407716,759.0
0.15,0.1577777777777778,0.02928806798473812,0.20915712799167535,0.17226528854435832,961.0
0.15,0.1788888888888889,0.0170169481155164,0.14757709251101322,0.12804097311139565,1362.0
0.15,0.2,0.01348714416896235,0.1384297520661157,0.12106537530266344,1452.0
0.16,0.01,0.3928996598639456,0.8204081632653061,0.45067264573991034,245.0
0.16,0.03111111111111111,0.36723090277777776,0.8375,0.4557823129251701,240.0
0.16,0.052222222222222225,0.3003920664206642,0.7416974169741697,0.42462845010615713,271.0
0.16,0.07333333333333333,0.2731351435705368,0.7528089887640449,0.4282655246252677,267.0
0.16,0.09444444444444444,0.2010152953586498,0.6360759493670886,0.3875968992248062,316.0
0.16,0.11555555555555555,0.15429311175337188,0.5809248554913294,0.36745886654478976,346.0
0.16,0.1366666666666667,0.10813867845117846,0.5075757575757576,0.33668341708542715,396.0
0.16,0.1577777777777778,0.056818181818181816,0.3383838383838384,0.24905183312262957,594.0
0.16,0.1788888888888889,0.03314284336419753,0.2326388888888889,0.187206020696143,864.0
0.16,0.2,0.02358906525573192,0.17724867724867724,0.15056179775280898,1134.0
1 gamma omega BER FER DFR num_iterations
2 0.0 0.01 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
3 0.0 0.03111111111111111 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
4 0.0 0.052222222222222225 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
5 0.0 0.07333333333333333 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
6 0.0 0.09444444444444444 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
7 0.0 0.11555555555555555 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
8 0.0 0.1366666666666667 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
9 0.0 0.1577777777777778 0.05726600985221675 0.9901477832512315 0.4975247524752475 203.0
10 0.0 0.1788888888888889 0.05648839137645108 1.0 0.5 201.0
11 0.0 0.2 0.05648839137645108 1.0 0.5 201.0
12 0.01 0.01 0.009809176672384219 0.34476843910806176 0.25637755102040816 583.0
13 0.01 0.03111111111111111 0.009445621468926553 0.34067796610169493 0.25410872313527183 590.0
14 0.01 0.052222222222222225 0.01133578431372549 0.45475113122171945 0.31259720062208396 442.0
15 0.01 0.07333333333333333 0.01494956772334294 0.579250720461095 0.36678832116788324 347.0
16 0.01 0.09444444444444444 0.018493357487922704 0.7282608695652174 0.42138364779874216 276.0
17 0.01 0.11555555555555555 0.022839822404371584 0.8237704918032787 0.451685393258427 244.0
18 0.01 0.1366666666666667 0.02585891812865497 0.881578947368421 0.46853146853146854 228.0
19 0.01 0.1577777777777778 0.02834855403348554 0.9178082191780822 0.4785714285714286 219.0
20 0.01 0.1788888888888889 0.030237268518518517 0.9305555555555556 0.48201438848920863 216.0
21 0.01 0.2 0.03206983024691358 0.9305555555555556 0.48201438848920863 216.0
22 0.02 0.01 0.006260611205432937 0.17062818336162988 0.14451706608569354 1178.0
23 0.02 0.03111111111111111 0.0062798622821810006 0.16947723440134907 0.14368231046931407 1186.0
24 0.02 0.052222222222222225 0.006574923547400612 0.18440366972477065 0.15438324282389448 1090.0
25 0.02 0.07333333333333333 0.007597305389221557 0.2407185628742515 0.19245647969052224 835.0
26 0.02 0.09444444444444444 0.008954678362573099 0.32057416267942584 0.24092009685230023 627.0
27 0.02 0.11555555555555555 0.012163835810332625 0.4267515923566879 0.29806259314456035 471.0
28 0.02 0.1366666666666667 0.014441906005221931 0.5248041775456919 0.3441780821917808 383.0
29 0.02 0.1577777777777778 0.01801075268817204 0.6483870967741936 0.3933463796477495 310.0
30 0.02 0.1788888888888889 0.01838235294117647 0.6955017301038062 0.41020408163265304 289.0
31 0.02 0.2 0.02300185873605948 0.7472118959107806 0.4276595744680851 269.0
32 0.03 0.01 0.005826600372902424 0.12492231199502797 0.11055831951354339 1609.0
33 0.03 0.03111111111111111 0.005153764066744277 0.1169965075669383 0.10427528675703858 1718.0
34 0.03 0.052222222222222225 0.005224068479355489 0.12145015105740181 0.10829741379310345 1655.0
35 0.03 0.07333333333333333 0.005720441854819131 0.14639475600874 0.12770012706480305 1373.0
36 0.03 0.09444444444444444 0.006545673825085589 0.1876750700280112 0.1580188679245283 1071.0
37 0.03 0.11555555555555555 0.00666000666000666 0.2007992007992008 0.16722129783693843 1001.0
38 0.03 0.1366666666666667 0.007941143747218514 0.2683578104138852 0.21157894736842106 749.0
39 0.03 0.1577777777777778 0.010007122507122507 0.3435897435897436 0.25572519083969464 585.0
40 0.03 0.1788888888888889 0.011066398390342052 0.4044265593561368 0.28796561604584525 497.0
41 0.03 0.2 0.013762953367875648 0.5207253886010362 0.3424190800681431 386.0
42 0.04 0.01 0.004656747891283974 0.09418931583880037 0.08333333333333333 2134.0
43 0.04 0.03111111111111111 0.004369643662607127 0.09066305818673884 0.08122668876916701 2217.0
44 0.04 0.052222222222222225 0.004334356525693497 0.09140518417462483 0.08183716075156576 2199.0
45 0.04 0.07333333333333333 0.0043778756147866095 0.09566872917658258 0.0861244019138756 2101.0
46 0.04 0.09444444444444444 0.004664008067473414 0.11056105610561057 0.09821428571428571 1818.0
47 0.04 0.11555555555555555 0.005201351653261841 0.13471849865951743 0.11768184506209343 1492.0
48 0.04 0.1366666666666667 0.005719940955448202 0.16183574879227053 0.13809854267869534 1242.0
49 0.04 0.1577777777777778 0.006861342351716961 0.20915712799167535 0.17155172413793104 961.0
50 0.04 0.1788888888888889 0.006769451812555261 0.26657824933687 0.21047120418848167 754.0
51 0.04 0.2 0.008777680652680652 0.3513986013986014 0.26002587322121606 572.0
52 0.05 0.01 0.004950007872775941 0.0949456778460085 0.08513396715643906 2117.0
53 0.05 0.03111111111111111 0.004472477064220184 0.09220183486238533 0.08249158249158249 2180.0
54 0.05 0.052222222222222225 0.0041358922709638915 0.08997314234556848 0.08103661044837515 2234.0
55 0.05 0.07333333333333333 0.004200660450660451 0.0937062937062937 0.08411614005123826 2145.0
56 0.05 0.09444444444444444 0.004315476190476191 0.09901477832512315 0.08886894075403949 2030.0
57 0.05 0.11555555555555555 0.004400045537340619 0.10983606557377049 0.09763313609467456 1830.0
58 0.05 0.1366666666666667 0.005150726523530687 0.13077423552374756 0.11463133640552996 1537.0
59 0.05 0.1577777777777778 0.00600700108754758 0.1639477977161501 0.1402524544179523 1226.0
60 0.05 0.1788888888888889 0.005696321321321321 0.1810810810810811 0.15267175572519084 1110.0
61 0.05 0.2 0.007712215320910973 0.2496894409937888 0.19900497512437812 805.0
62 0.06 0.01 0.005722295514511874 0.10606860158311346 0.09460105112279026 1895.0
63 0.06 0.03111111111111111 0.005266320850202429 0.10172064777327935 0.09191176470588236 1976.0
64 0.06 0.052222222222222225 0.005028375897179102 0.10065097646469705 0.09103322712790168 1997.0
65 0.06 0.07333333333333333 0.004865846728364994 0.09790550414028251 0.0887705281846427 2053.0
66 0.06 0.09444444444444444 0.00488129216398547 0.10430721328489881 0.09402914903620123 1927.0
67 0.06 0.11555555555555555 0.005010190217391304 0.1092391304347826 0.0971540726202159 1840.0
68 0.06 0.1366666666666667 0.005375529981829195 0.12174439733494852 0.10756756756756757 1651.0
69 0.06 0.1577777777777778 0.005551438296003513 0.1324110671936759 0.11589982527664532 1518.0
70 0.06 0.1788888888888889 0.005181307141092167 0.14899925871015568 0.1285529715762274 1349.0
71 0.06 0.2 0.006400966183574879 0.19420289855072465 0.16262135922330098 1035.0
72 0.07 0.01 0.00549223127865512 0.10239429444727458 0.0912037037037037 1963.0
73 0.07 0.03111111111111111 0.005026823168764118 0.09728944820909971 0.08745583038869258 2066.0
74 0.07 0.052222222222222225 0.004677268475210477 0.0940130963517306 0.08476027397260275 2138.0
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8
latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_963965_metadata.json Normal file
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{
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"name": "2d_BER_FER_DFR_gamma_omega_963965",
"platform": "Linux-6.2.9-arch1-1-x86_64-with-glibc2.37",
"omega": 0.2,
"K": 100,
"end_time": "2023-04-10 23:13:26.003727"
}

171
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0.09,0.2,0.014297606659729449,0.1296774193548387,0.08175355450236967,1550.0
0.1,0.01,0.09256160193894628,0.17978533094812166,0.07373653686826843,1118.0
0.1,0.03111111111111111,0.08567584989209943,0.1746307558644657,0.0747588424437299,1151.0
0.1,0.052222222222222225,0.07325599872874623,0.16502463054187191,0.06951871657754011,1218.0
0.1,0.07333333333333333,0.0716492910156814,0.17994628469113697,0.07762180016515277,1117.0
0.1,0.09444444444444444,0.04482021473350467,0.1496649292628444,0.06736111111111111,1343.0
0.1,0.11555555555555555,0.0433270082226439,0.1642156862745098,0.0755287009063444,1224.0
0.1,0.1366666666666667,0.03859403984678022,0.15011202389843167,0.0746371803731859,1339.0
0.1,0.1577777777777778,0.033716853721088916,0.14660831509846828,0.05837912087912088,1371.0
0.1,0.1788888888888889,0.02735483870967742,0.134,0.06890130353817504,1500.0
0.1,0.2,0.01849736955422837,0.11665699361578642,0.06256800870511425,1723.0
0.11,0.01,0.11370967741935484,0.2392857142857143,0.09967845659163987,840.0
0.11,0.03111111111111111,0.1088444977765229,0.21991247264770242,0.09415262636273539,914.0
0.11,0.052222222222222225,0.0930202964942419,0.19822485207100593,0.08730873087308731,1014.0
0.11,0.07333333333333333,0.08403910823265662,0.19401544401544402,0.08963093145869948,1036.0
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0.11,0.1788888888888889,0.03399950244630567,0.12917737789203085,0.061519903498190594,1556.0
0.11,0.2,0.03128708598554321,0.13444816053511705,0.058564231738035266,1495.0
0.12,0.01,0.09902629661522157,0.20636550308008214,0.09395348837209302,974.0
0.12,0.03111111111111111,0.10908054456441553,0.20893970893970895,0.0838095238095238,962.0
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0.12,0.09444444444444444,0.07283243647664815,0.1793041926851026,0.08039376538146022,1121.0
0.12,0.11555555555555555,0.07919519268139556,0.18628359592215013,0.07142857142857142,1079.0
0.12,0.1366666666666667,0.0807505798018132,0.1876750700280112,0.06950477845351868,1071.0
0.12,0.1577777777777778,0.057941725716916935,0.15939730372720062,0.05401350337584396,1261.0
0.12,0.1788888888888889,0.04590847043609455,0.1492204899777283,0.05804195804195804,1347.0
0.12,0.2,0.03912761511301131,0.1496649292628444,0.061495457721872815,1343.0
0.13,0.01,0.12272009982017688,0.22866894197952217,0.10488798370672098,879.0
0.13,0.03111111111111111,0.10760712566201251,0.21428571428571427,0.08843537414965986,938.0
0.13,0.052222222222222225,0.12202274515413776,0.22283813747228381,0.10159362549800798,902.0
0.13,0.07333333333333333,0.10187853744921699,0.2163616792249731,0.0892156862745098,929.0
0.13,0.09444444444444444,0.10423973054619952,0.2163616792249731,0.08562992125984252,929.0
0.13,0.11555555555555555,0.09665142260826862,0.1912464319695528,0.07482394366197183,1051.0
0.13,0.1366666666666667,0.07891229005598507,0.18457300275482094,0.07476635514018691,1089.0
0.13,0.1577777777777778,0.06815636200716846,0.17447916666666666,0.06035889070146819,1152.0
0.13,0.1788888888888889,0.05538312811641396,0.15851735015772872,0.05373134328358209,1268.0
0.13,0.2,0.05165682494753794,0.15204236006051436,0.04133430021754895,1322.0
0.14,0.01,0.1284457478005865,0.261038961038961,0.12300683371298406,770.0
0.14,0.03111111111111111,0.131699909710252,0.25572519083969464,0.10681818181818181,786.0
0.14,0.052222222222222225,0.13884100796191415,0.25572519083969464,0.10884353741496598,786.0
0.14,0.07333333333333333,0.1282416192283365,0.24632352941176472,0.11974110032362459,816.0
0.14,0.09444444444444444,0.12173946596697022,0.24043062200956938,0.10204081632653061,836.0
0.14,0.11555555555555555,0.10076704843963434,0.2182410423452769,0.08902077151335312,921.0
0.14,0.1366666666666667,0.0901300878636406,0.19345524542829645,0.07314897413024085,1039.0
0.14,0.1577777777777778,0.08156637416856634,0.19457889641819942,0.0702070207020702,1033.0
0.14,0.1788888888888889,0.0714029916531225,0.17164816396242527,0.052588996763754045,1171.0
0.14,0.2,0.06640895761130365,0.16611570247933885,0.03662420382165605,1210.0
0.15,0.01,0.1200246362306567,0.2398568019093079,0.11040339702760085,838.0
0.15,0.03111111111111111,0.1264404481837394,0.2484548825710754,0.10607734806629834,809.0
0.15,0.052222222222222225,0.12532354645547614,0.2407185628742515,0.1164021164021164,835.0
0.15,0.07333333333333333,0.1189998456551937,0.24043062200956938,0.10492505353319058,836.0
0.15,0.09444444444444444,0.12465060058925738,0.23536299765807964,0.10388247639034627,854.0
0.15,0.11555555555555555,0.11818080811821785,0.23076923076923078,0.08604407135362015,871.0
0.15,0.1366666666666667,0.10605683394387737,0.22160970231532526,0.08383838383838384,907.0
0.15,0.1577777777777778,0.10987271870159607,0.20981210855949894,0.06627680311890838,958.0
0.15,0.1788888888888889,0.09469749411601827,0.21566523605150215,0.05188199389623601,932.0
0.15,0.2,0.07725031694604874,0.17554585152838428,0.032121724429416736,1145.0
0.16,0.01,0.13433603799722937,0.24662576687116564,0.13205537806176784,815.0
0.16,0.03111111111111111,0.13267038564152317,0.2518796992481203,0.1172566371681416,798.0
0.16,0.052222222222222225,0.11506806568631275,0.2430471584038694,0.09716157205240175,827.0
0.16,0.07333333333333333,0.12611019262562959,0.2395709177592372,0.1149789029535865,839.0
0.16,0.09444444444444444,0.12415036238687145,0.2339930151338766,0.10706860706860707,859.0
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0.16,0.1366666666666667,0.1138223103261849,0.2245810055865922,0.09137055837563451,895.0
0.16,0.1577777777777778,0.104324707550514,0.22087912087912087,0.07801418439716312,910.0
0.16,0.1788888888888889,0.08600110179347493,0.19070208728652752,0.0580875781948168,1054.0
0.16,0.2,0.08638911864718317,0.1908831908831909,0.031278748850046,1053.0
1 gamma omega BER FER DFR num_iterations
2 0.0 0.01 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
3 0.0 0.03111111111111111 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
4 0.0 0.052222222222222225 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
5 0.0 0.07333333333333333 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
6 0.0 0.09444444444444444 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
7 0.0 0.11555555555555555 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
8 0.0 0.1366666666666667 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
9 0.0 0.1577777777777778 0.019139784946236558 0.44666666666666666 0.3076923076923077 450.0
10 0.0 0.1788888888888889 0.0192831541218638 0.44666666666666666 0.3087557603686636 450.0
11 0.0 0.2 0.0192831541218638 0.44666666666666666 0.3087557603686636 450.0
12 0.01 0.01 0.010325145898800745 0.19980119284294234 0.16026711185308848 1006.0
13 0.01 0.03111111111111111 0.009473254389546755 0.16962025316455695 0.13880813953488372 1185.0
14 0.01 0.052222222222222225 0.00976460331299041 0.1810810810810811 0.14746543778801843 1110.0
15 0.01 0.07333333333333333 0.010090655732453472 0.19960278053624628 0.16153205661948378 1007.0
16 0.01 0.09444444444444444 0.011558510236458413 0.23536299765807964 0.188212927756654 854.0
17 0.01 0.11555555555555555 0.0125117031236701 0.26517150395778366 0.20794148380355276 758.0
18 0.01 0.1366666666666667 0.013978494623655914 0.30454545454545456 0.23166472642607683 660.0
19 0.01 0.1577777777777778 0.01442722885445771 0.3165354330708661 0.23860911270983212 635.0
20 0.01 0.1788888888888889 0.01584998325705994 0.3477508650519031 0.25611325611325614 578.0
21 0.01 0.2 0.01740718198417529 0.37924528301886795 0.2729766803840878 530.0
22 0.02 0.01 0.009582100035117645 0.15629860031104198 0.12635869565217392 1286.0
23 0.02 0.03111111111111111 0.009294696555494806 0.13627118644067795 0.10876132930513595 1475.0
24 0.02 0.052222222222222225 0.008642806073095848 0.13102998696219034 0.10501750291715285 1534.0
25 0.02 0.07333333333333333 0.0082122552116235 0.1269740998104864 0.09903244166192374 1583.0
26 0.02 0.09444444444444444 0.008910036619784014 0.14533622559652928 0.11572890025575447 1383.0
27 0.02 0.11555555555555555 0.009104280787177926 0.1580188679245283 0.12637362637362637 1272.0
28 0.02 0.1366666666666667 0.009204599502000392 0.17432784041630528 0.14083457526080476 1153.0
29 0.02 0.1577777777777778 0.010275497868549978 0.19822485207100593 0.15850622406639003 1014.0
30 0.02 0.1788888888888889 0.01153727427072079 0.24043062200956938 0.18359375 836.0
31 0.02 0.2 0.012714230488381084 0.26764314247669774 0.20276008492569003 751.0
32 0.03 0.01 0.008657648283038501 0.1296774193548387 0.0972626674432149 1550.0
33 0.03 0.03111111111111111 0.008535436778902755 0.12226277372262774 0.09020475926950747 1644.0
34 0.03 0.052222222222222225 0.008215811769883915 0.11426947129050596 0.08098223615464994 1759.0
35 0.03 0.07333333333333333 0.008296554467448336 0.11802701115678214 0.08490059108006448 1703.0
36 0.03 0.09444444444444444 0.007809078966574223 0.11368778280542986 0.08441222164681512 1768.0
37 0.03 0.11555555555555555 0.007986598098800063 0.1213768115942029 0.09010989010989011 1656.0
38 0.03 0.1366666666666667 0.007991675338189386 0.1296774193548387 0.10404624277456648 1550.0
39 0.03 0.1577777777777778 0.008075890622867283 0.14174894217207334 0.1131957473420888 1418.0
40 0.03 0.1788888888888889 0.009295634371714713 0.17238421955403088 0.14201618837380428 1166.0
41 0.03 0.2 0.009988378301975688 0.19571567672833495 0.1588861588861589 1027.0
42 0.04 0.01 0.009235711417480925 0.13900414937759337 0.10575139146567718 1446.0
43 0.04 0.03111111111111111 0.008517362315489267 0.12007168458781362 0.09021739130434783 1674.0
44 0.04 0.052222222222222225 0.008548293159486726 0.11706464764123471 0.08767268862911796 1717.0
45 0.04 0.07333333333333333 0.008246971245073712 0.11368778280542986 0.08298755186721991 1768.0
46 0.04 0.09444444444444444 0.008946021639700994 0.12554653341661462 0.09343148357870895 1601.0
47 0.04 0.11555555555555555 0.009240482512073255 0.13026571613739468 0.09713282621416033 1543.0
48 0.04 0.1366666666666667 0.010349165657860381 0.1479028697571744 0.1100196463654224 1359.0
49 0.04 0.1577777777777778 0.010499779724900876 0.15250379362670713 0.11424731182795698 1318.0
50 0.04 0.1788888888888889 0.010457284650833037 0.1577708006279435 0.11772853185595568 1274.0
51 0.04 0.2 0.01137154792295196 0.18075539568345322 0.13463035019455252 1112.0
52 0.05 0.01 0.011001752035452953 0.16054313099041534 0.11644318983768526 1252.0
53 0.05 0.03111111111111111 0.01073681704558981 0.14833948339483394 0.10737812911725955 1355.0
54 0.05 0.052222222222222225 0.010447214076246334 0.14275568181818182 0.10318471337579618 1408.0
55 0.05 0.07333333333333333 0.010106792909831554 0.1372013651877133 0.10012285012285012 1465.0
56 0.05 0.09444444444444444 0.010065568737527685 0.13664174031271245 0.09975520195838433 1471.0
57 0.05 0.11555555555555555 0.010179707556646947 0.13910034602076124 0.10248447204968944 1445.0
58 0.05 0.1366666666666667 0.009283201220804544 0.13206307490144548 0.10047281323877069 1522.0
59 0.05 0.1577777777777778 0.009666558053654828 0.13535353535353536 0.09890776699029126 1485.0
60 0.05 0.1788888888888889 0.009426202174251351 0.13852515506547208 0.10707692307692308 1451.0
61 0.05 0.2 0.009859289748253086 0.14910979228486648 0.11548556430446194 1348.0
62 0.06 0.01 0.016003292105804624 0.14245216158752658 0.10865445356917246 1411.0
63 0.06 0.03111111111111111 0.011835847554283501 0.13408939292861907 0.09861695730607337 1499.0
64 0.06 0.052222222222222225 0.009863368097663514 0.12593984962406016 0.09369676320272573 1596.0
65 0.06 0.07333333333333333 0.009661249898940901 0.12593984962406016 0.09369676320272573 1596.0
66 0.06 0.09444444444444444 0.009856047073657665 0.13094462540716612 0.0975896531452087 1535.0
67 0.06 0.11555555555555555 0.011140789443645412 0.14225053078556263 0.102287166454892 1413.0
68 0.06 0.1366666666666667 0.011408043884560305 0.15124153498871332 0.10865191146881288 1329.0
69 0.06 0.1577777777777778 0.010941746425617394 0.14725274725274726 0.10725964682799215 1365.0
70 0.06 0.1788888888888889 0.01018919482203645 0.14429289303661164 0.10418006430868167 1393.0
71 0.06 0.2 0.010509963834073642 0.15124153498871332 0.11044176706827309 1329.0
72 0.07 0.01 0.044299978055738425 0.17091836734693877 0.12304250559284116 1176.0
73 0.07 0.03111111111111111 0.028812022282679102 0.1614457831325301 0.11071428571428571 1245.0
74 0.07 0.052222222222222225 0.021262002743484224 0.13786008230452676 0.08818011257035648 1458.0
75 0.07 0.07333333333333333 0.015357061984066533 0.12827058072750477 0.08255269320843091 1567.0
76 0.07 0.09444444444444444 0.013745941838778752 0.14346895074946467 0.09320388349514563 1401.0
77 0.07 0.11555555555555555 0.011094597140524586 0.13043478260869565 0.08870490833826139 1541.0
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8
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@ -0,0 +1,8 @@
{
"duration": 2.7652339239721186,
"name": "2d_BER_FER_DFR_gamma_omega_bch_31_26",
"platform": "Linux-6.2.9-arch1-1-x86_64-with-glibc2.37",
"omega": 0.2,
"K": 100,
"end_time": "2023-04-10 23:14:01.805865"
}

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latex/thesis/res/proximal/2d_ber_fer_dfr_gamma_omega_pegreg252x504.csv Normal file
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@ -0,0 +1,171 @@
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0.08,0.2,0.0038941579398010934,0.4190871369294606,0.2953216374269006,241.0
0.09,0.01,0.0015245519143069254,0.11247216035634744,0.1011011011011011,898.0
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0.09,0.1577777777777778,0.002767052767052767,0.27297297297297296,0.21443736730360935,370.0
0.09,0.1788888888888889,0.003568624151663021,0.3568904593639576,0.2630208333333333,283.0
0.09,0.2,0.003883823032759203,0.35815602836879434,0.26370757180156656,282.0
0.1,0.01,0.0016739646049990877,0.11609195402298851,0.10401647785787847,870.0
0.1,0.03111111111111111,0.0011893799877225292,0.09300184162062615,0.08508845829823083,1086.0
0.1,0.052222222222222225,0.001195050546635561,0.09702209414024976,0.08844133099824869,1041.0
0.1,0.07333333333333333,0.0014643003572892871,0.12423124231242312,0.11050328227571116,813.0
0.1,0.09444444444444444,0.0016656146994880427,0.1487481590574374,0.1294871794871795,679.0
0.1,0.11555555555555555,0.001984126984126984,0.17688266199649738,0.15029761904761904,571.0
0.1,0.1366666666666667,0.002216857584650825,0.20079522862823063,0.1672185430463576,503.0
0.1,0.1577777777777778,0.002228518408059118,0.21085594989561587,0.17413793103448275,479.0
0.1,0.1788888888888889,0.003373015873015873,0.29705882352941176,0.2290249433106576,340.0
0.1,0.2,0.004987984569657876,0.40239043824701193,0.2869318181818182,251.0
0.11,0.01,0.0022356360384529397,0.14225352112676057,0.12453760789149199,710.0
0.11,0.03111111111111111,0.0015264224390764145,0.11310190369540873,0.10160965794768612,893.0
0.11,0.052222222222222225,0.0015628236268805664,0.11981020166073547,0.10699152542372882,843.0
0.11,0.07333333333333333,0.0015828148679907473,0.12688442211055276,0.11259754738015608,796.0
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0.11,0.11555555555555555,0.002167723149866007,0.16396103896103897,0.14086471408647142,616.0
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0.11,0.2,0.006696428571428571,0.3607142857142857,0.2650918635170604,280.0
0.12,0.01,0.03197945845004668,0.8487394957983193,0.4590909090909091,119.0
0.12,0.03111111111111111,0.008995703544575726,0.37969924812030076,0.27520435967302453,266.0
0.12,0.052222222222222225,0.0040498522641379785,0.21861471861471862,0.17939609236234458,462.0
0.12,0.07333333333333333,0.003753985395454078,0.21814254859611232,0.17907801418439717,463.0
0.12,0.09444444444444444,0.0039115646258503405,0.19238095238095237,0.16134185303514376,525.0
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0.12,0.1366666666666667,0.00392156862745098,0.2376470588235294,0.1920152091254753,425.0
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0.12,0.1788888888888889,0.007080734317156043,0.3226837060702875,0.24396135265700483,313.0
0.12,0.2,0.006784628745115067,0.3069908814589666,0.23488372093023255,329.0
0.13,0.01,0.13816203048876316,1.0,0.5,101.0
0.13,0.03111111111111111,0.08464953638220965,1.0,0.5,101.0
0.13,0.052222222222222225,0.04782494758909853,0.9528301886792453,0.48792270531400966,106.0
0.13,0.07333333333333333,0.030575071558678116,0.8278688524590164,0.452914798206278,122.0
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0.13,0.1577777777777778,0.00896852029402365,0.3389261744966443,0.2531328320802005,298.0
0.13,0.1788888888888889,0.009506228651798272,0.31962025316455694,0.2422062350119904,316.0
0.13,0.2,0.01083748782304739,0.36462093862815886,0.2671957671957672,277.0
0.14,0.01,0.26604981926764104,1.0,0.5,101.0
0.14,0.03111111111111111,0.19921023965141613,0.9901960784313726,0.4975369458128079,102.0
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0.14,0.11555555555555555,0.036353241861716436,0.8559322033898306,0.4611872146118721,118.0
0.14,0.1366666666666667,0.02728559123907961,0.7829457364341085,0.4391304347826087,129.0
0.14,0.1577777777777778,0.024941211052322162,0.7481481481481481,0.4279661016949153,135.0
0.14,0.1788888888888889,0.02327687227016086,0.6778523489932886,0.404,149.0
0.14,0.2,0.024135681669928244,0.6917808219178082,0.4089068825910931,146.0
0.15,0.01,0.3680850227879931,1.0,0.5,101.0
0.15,0.03111111111111111,0.28084236995128087,1.0,0.5,101.0
0.15,0.052222222222222225,0.21257661480433757,1.0,0.5,101.0
0.15,0.07333333333333333,0.1581997485462832,1.0,0.5,101.0
0.15,0.09444444444444444,0.09183395580454404,0.9901960784313726,0.4975369458128079,102.0
0.15,0.11555555555555555,0.06272152874094622,0.9805825242718447,0.4950980392156863,103.0
0.15,0.1366666666666667,0.04627739984882842,0.9619047619047619,0.49029126213592233,105.0
0.15,0.1577777777777778,0.045310592185592184,0.9711538461538461,0.4926829268292683,104.0
0.15,0.1788888888888889,0.04101153039832285,0.9528301886792453,0.48792270531400966,106.0
0.15,0.2,0.042511757789535566,0.9351851851851852,0.48325358851674644,108.0
0.16,0.01,0.43464167845355967,1.0,0.5,101.0
0.16,0.03111111111111111,0.3620933521923621,1.0,0.5,101.0
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0.16,0.1788888888888889,0.056577086280056574,1.0,0.5,101.0
0.16,0.2,0.0575986170045576,1.0,0.5,101.0
1 gamma omega BER FER DFR num_iterations
2 0.0 0.01 0.0566949552098067 1.0 0.5 101.0
3 0.0 0.03111111111111111 0.0566949552098067 1.0 0.5 101.0
4 0.0 0.052222222222222225 0.0566949552098067 1.0 0.5 101.0
5 0.0 0.07333333333333333 0.0566949552098067 1.0 0.5 101.0
6 0.0 0.09444444444444444 0.0566949552098067 1.0 0.5 101.0
7 0.0 0.11555555555555555 0.0566949552098067 1.0 0.5 101.0
8 0.0 0.1366666666666667 0.0566949552098067 1.0 0.5 101.0
9 0.0 0.1577777777777778 0.05665566556655666 1.0 0.5 101.0
10 0.0 0.1788888888888889 0.05665566556655666 1.0 0.5 101.0
11 0.0 0.2 0.05665566556655666 1.0 0.5 101.0
12 0.01 0.01 0.009004884004884004 0.8632478632478633 0.463302752293578 117.0
13 0.01 0.03111111111111111 0.008654898741105638 0.8706896551724138 0.46543778801843316 116.0
14 0.01 0.052222222222222225 0.011163032191069575 0.9439252336448598 0.4855769230769231 107.0
15 0.01 0.07333333333333333 0.014910419613389911 1.0 0.5 101.0
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20 0.01 0.1788888888888889 0.030645921735030647 1.0 0.5 101.0
21 0.01 0.2 0.03202105924878202 1.0 0.5 101.0
22 0.02 0.01 0.003947146234380277 0.5372340425531915 0.3494809688581315 188.0
23 0.02 0.03111111111111111 0.003409344958640733 0.47417840375586856 0.321656050955414 213.0
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31 0.02 0.2 0.019703756089894702 1.0 0.5 101.0
32 0.03 0.01 0.002768987341772152 0.31962025316455694 0.2422062350119904 316.0
33 0.03 0.03111111111111111 0.0026540919398062254 0.32792207792207795 0.2469437652811736 308.0
34 0.03 0.052222222222222225 0.003213101160862355 0.376865671641791 0.27371273712737126 268.0
35 0.03 0.07333333333333333 0.004288920955587622 0.51010101010101 0.3377926421404682 198.0
36 0.03 0.09444444444444444 0.004538279400157853 0.5580110497237569 0.35815602836879434 181.0
37 0.03 0.11555555555555555 0.006107171000788022 0.7163120567375887 0.41735537190082644 141.0
38 0.03 0.1366666666666667 0.006990947420634921 0.7890625 0.4410480349344978 128.0
39 0.03 0.1577777777777778 0.008405483405483406 0.9181818181818182 0.4786729857819905 110.0
40 0.03 0.1788888888888889 0.012193712436430883 0.9805825242718447 0.4950980392156863 103.0
41 0.03 0.2 0.012414965986394558 0.9619047619047619 0.49029126213592233 105.0
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43 0.04 0.03111111111111111 0.0018143822801617478 0.1920152091254753 0.16108452950558214 526.0
44 0.04 0.052222222222222225 0.0021033653846153845 0.24278846153846154 0.195357833655706 416.0
45 0.04 0.07333333333333333 0.002760009476427387 0.30149253731343284 0.231651376146789 335.0
46 0.04 0.09444444444444444 0.0032545392257622473 0.36330935251798563 0.26649076517150394 278.0
47 0.04 0.11555555555555555 0.003886714503152859 0.4611872146118721 0.315625 219.0
48 0.04 0.1366666666666667 0.004636937647987372 0.5580110497237569 0.35815602836879434 181.0
49 0.04 0.1577777777777778 0.006065759637188209 0.7214285714285714 0.4190871369294606 140.0
50 0.04 0.1788888888888889 0.007051282051282051 0.7769230769230769 0.43722943722943725 130.0
51 0.04 0.2 0.008343962585034014 0.9017857142857143 0.47417840375586856 112.0
52 0.05 0.01 0.0013476602762317047 0.13116883116883116 0.11595866819747416 770.0
53 0.05 0.03111111111111111 0.001215738284703802 0.13395225464190982 0.11812865497076024 754.0
54 0.05 0.052222222222222225 0.0013085090474054136 0.13593539703903096 0.11966824644549763 743.0
55 0.05 0.07333333333333333 0.0016940499396639747 0.1968810916179337 0.16449511400651465 513.0
56 0.05 0.09444444444444444 0.002323517126148705 0.2657894736842105 0.20997920997921 380.0
57 0.05 0.11555555555555555 0.0030690803162713273 0.3782771535580524 0.27445652173913043 267.0
58 0.05 0.1366666666666667 0.0036490683229813666 0.4391304347826087 0.30513595166163143 230.0
59 0.05 0.1577777777777778 0.003493582440950862 0.48325358851674644 0.3258064516129032 209.0
60 0.05 0.1788888888888889 0.005261703837653205 0.6392405063291139 0.38996138996138996 158.0
61 0.05 0.2 0.006157092466616276 0.8015873015873016 0.44493392070484583 126.0
62 0.06 0.01 0.0011150155426408975 0.09555345316934721 0.08721934369602763 1057.0
63 0.06 0.03111111111111111 0.0009756300078880724 0.08805579773321709 0.08092948717948718 1147.0
64 0.06 0.052222222222222225 0.0010514553197480028 0.11197339246119734 0.10069790628115653 902.0
65 0.06 0.07333333333333333 0.001240079365079365 0.14511494252873564 0.12672521957340024 696.0
66 0.06 0.09444444444444444 0.001657423344170332 0.20281124497991967 0.1686143572621035 498.0
67 0.06 0.11555555555555555 0.002254457909984902 0.2596401028277635 0.20612244897959184 389.0
68 0.06 0.1366666666666667 0.002661303429221518 0.3447098976109215 0.2563451776649746 293.0
69 0.06 0.1577777777777778 0.0031530700135351296 0.39147286821705424 0.28133704735376047 258.0
70 0.06 0.1788888888888889 0.004362886959572042 0.5580110497237569 0.35815602836879434 181.0
71 0.06 0.2 0.004514446227929374 0.5674157303370787 0.36200716845878134 178.0
72 0.07 0.01 0.0012946771337689403 0.09970384995064166 0.09066427289048475 1013.0
73 0.07 0.03111111111111111 0.0010289466914824399 0.09628217349857007 0.08782608695652173 1049.0
74 0.07 0.052222222222222225 0.0010438806735103032 0.09591642924976258 0.08752166377816291 1053.0
75 0.07 0.07333333333333333 0.0012928486997635933 0.13430851063829788 0.11840562719812427 752.0
76 0.07 0.09444444444444444 0.0015063438976482455 0.1688963210702341 0.14449213161659513 598.0
77 0.07 0.11555555555555555 0.002330815838880355 0.271505376344086 0.2135306553911205 372.0
78 0.07 0.1366666666666667 0.0023434148434148434 0.27297297297297296 0.21443736730360935 370.0
79 0.07 0.1577777777777778 0.0031994962806770923 0.3726937269372694 0.271505376344086 271.0
80 0.07 0.1788888888888889 0.00349771001944915 0.39920948616600793 0.2853107344632768 253.0
81 0.07 0.2 0.0046729985254575414 0.5519125683060109 0.35563380281690143 183.0
82 0.08 0.01 0.0015084372339892844 0.10620399579390116 0.09600760456273764 951.0
83 0.08 0.03111111111111111 0.001224325180768012 0.09165154264972777 0.08395677472984206 1102.0
84 0.08 0.052222222222222225 0.0012009189640768587 0.10631578947368421 0.0960989533777355 950.0
85 0.08 0.07333333333333333 0.001338252314814815 0.13151041666666666 0.11622554660529344 768.0
86 0.08 0.09444444444444444 0.00176326613370409 0.1843065693430657 0.15562403697996918 548.0
87 0.08 0.11555555555555555 0.0020696496989600437 0.21767241379310345 0.17876106194690267 464.0
88 0.08 0.1366666666666667 0.002608166922683052 0.271505376344086 0.2135306553911205 372.0
89 0.08 0.1577777777777778 0.002895403546869345 0.3289902280130293 0.24754901960784315 307.0
90 0.08 0.1788888888888889 0.0032259906360625787 0.36330935251798563 0.26649076517150394 278.0
91 0.08 0.2 0.0038941579398010934 0.4190871369294606 0.2953216374269006 241.0
92 0.09 0.01 0.0015245519143069254 0.11247216035634744 0.1011011011011011 898.0
93 0.09 0.03111111111111111 0.0012297727814969193 0.08930150309460655 0.08198051948051949 1131.0
94 0.09 0.052222222222222225 0.0012491077801570307 0.10813704496788008 0.09758454106280193 934.0
95 0.09 0.07333333333333333 0.001427743751034597 0.11703360370799537 0.10477178423236515 863.0
96 0.09 0.09444444444444444 0.0016587186398507154 0.14658925979680695 0.12784810126582277 689.0
97 0.09 0.11555555555555555 0.0018920988308743412 0.18738404452690166 0.1578125 539.0
98 0.09 0.1366666666666667 0.0023095418881295873 0.23006833712984054 0.18703703703703703 439.0
99 0.09 0.1577777777777778 0.002767052767052767 0.27297297297297296 0.21443736730360935 370.0
100 0.09 0.1788888888888889 0.003568624151663021 0.3568904593639576 0.2630208333333333 283.0
101 0.09 0.2 0.003883823032759203 0.35815602836879434 0.26370757180156656 282.0
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8
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{
"duration": 1115.670067375002,
"name": "2d_BER_FER_DFR_gamma_omega_pegreg252x504",
"platform": "Linux-6.2.9-arch1-1-x86_64-with-glibc2.37",
"omega": 0.2,
"K": 100,
"end_time": "2023-04-11 00:04:22.127611"
}