Fixed spelling errors
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@ -3,7 +3,7 @@
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In this chapter, proximal decoding and \ac{LP} Decoding using \ac{ADMM} are compared.
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First the two algorithms are compared on a theoretical basis.
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Subsequently, their respective simulation results are examined and their
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Subsequently, their respective simulation results are examined, and their
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differences are interpreted on the basis of their theoretical structure.
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%some similarities between the proximal decoding algorithm
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@ -119,13 +119,13 @@ return $\tilde{\boldsymbol{c}}$
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\end{figure}%
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%
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Their major differece is that while with proximal decoding the constraints
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Their major difference is that while with proximal decoding the constraints
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are regarded in a global context, considering all parity checks at the same
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time, with \ac{ADMM} each parity check is
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considered separately and in a more local context (line 4 in both algorithms).
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This difference means that while with proximal decoding the alternating
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minimization of the two parts of the objective function inevitably leads to
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oscillatory behaviour (as explained in section
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oscillatory behavior (as explained in section
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\ref{subsec:prox:conv_properties}), this is not the case with \ac{ADMM}, which
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partly explains the disparate decoding performance of the two methods.
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Furthermore, while with proximal decoding the step considering the constraints
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@ -137,7 +137,7 @@ The contrasting treatment of the constraints (global and approximate with
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proximal decoding as opposed to local and exact with \ac{LP} decoding using
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\ac{ADMM}) also leads to different prospects when the decoding process gets
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stuck in a local minimum.
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With proximal decoding this occurrs due to the approximate nature of the
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With proximal decoding this occurs due to the approximate nature of the
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calculation, whereas with \ac{LP} decoding it occurs due to the approximate
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formulation of the constraints - independent of the optimization method
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itself.
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@ -241,7 +241,7 @@ computed for each check node (line 6 in figure
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\ref{fig:comp:message_passing:admm}), whereas
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with proximal decoding, the same message is transmitted to all \acp{VN}
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(line 5 of figure \ref{fig:comp:message_passing:proximal}).
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This means that while both algorithms have an averege time complexity of
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This means that while both algorithms have an average time complexity of
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$\mathcal{O}\left( n \right)$, more arithmetic operations are required for the
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\ac{ADMM} case.
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