Almost almost done with corrections
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@ -1379,7 +1379,7 @@ using \ac{ADMM} and \ac{BP} are shown for various codes.
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To ensure comparability, in all cases the number of iterations was set to
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$K=200$.
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The values of the other parameters were chosen as $\mu = 5$, $\rho = 1$,
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$\epsilon = 10^{-5}$ and $\epsilon=10^{-5}$.
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$\epsilon_\text{pri} = 10^{-5}$ and $\epsilon_\text{dual}=10^{-5}$.
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Comparing the simulation results for the different codes, it is apparent that
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the difference in decoding performance depends on the code being
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considered.
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@ -1,4 +1,4 @@
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\chapter{Proximal Decoding and Implementation}%
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\chapter{Proximal Decoding}%
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\label{chapter:proximal_decoding}
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In this chapter, the proximal decoding algorithm is examined.
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@ -10,7 +10,7 @@ Finally, an improvement on proximal decoding is proposed.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Decoding Algorithm}%
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\section{Decoding Algorithm and Implementation}%
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\label{sec:prox:Decoding Algorithm}
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Proximal decoding was proposed by Wadayama et al. as a novel formulation of
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@ -1265,9 +1265,8 @@ error are strongly correlated, a relationship being depicted in figure
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\label{fig:prox:correlation}
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\end{figure}%
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%
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The y-axis depicts whether there is a bit error and the x-axis the
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variance in $\nabla h\left( \tilde{\boldsymbol{x}} \right)$ after the
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100th iteration.
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The x-axis depicts the variance in $\nabla h\left( \tilde{\boldsymbol{x}} \right)$
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after the 100th iteration, and the y-axis depicts whether there is a bit error.
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While this is not exactly the magnitude of the oscillation, it is
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proportional and easier to compute.
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The datapoints are taken from a single decoding operation.
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