Removed apendix B
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@ -810,10 +810,6 @@ $\gamma \in \left\{ 0.01, 0.05, 0.15 \right\}$.
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\end{figure}
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\chapter{Proximal Decoding as a Message Passing Algorithm}
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\label{chapter:Proximal Decoding as a Message Passing Algorithm}
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%\chapter{\acs{LP} Decoding using \acs{ADMM} as a Proximal Algorithm}%
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%\label{chapter:LD Decoding using ADMM as a Proximal Algorithm}
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%
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@ -161,11 +161,13 @@ $\mathcal{O}\left( n \right)$ (see section TODO, \cite[Sec. VIII.]{lautern}).
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Both algorithms can be understood as message-passing algorithms, \ac{LP}
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decoding using \ac{ADMM} as similarly to \cite[Sec. III. D.]{original_admm}
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or \cite[Sec. II. B.]{efficient_lp_dec_admm} and proximal decoding as shown in
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appendix \ref{chapter:Proximal Decoding as a Message Passing Algorithm}.
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or \cite[Sec. II. B.]{efficient_lp_dec_admm} and proximal decoding by
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starting with algorithm \ref{alg:prox},
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substituting for the gradient of the code-constraint polynomial and separating
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it into two parts.
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The algorithms in their message-passing form are depicted in figure
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\ref{fig:comp:message_passing}.
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$M_{j\to i}$ denotes the message transmitted from \ac{CN} j to \ac{VN} i.
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$M_{j\to i}$ denotes a message transmitted from \ac{CN} j to \ac{VN} i.
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$M_{j\to}$ signifies the special case where a \ac{VN} transmits the same
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message to all \acp{VN}.
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%
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