Done with ADMM explanation
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@ -121,3 +121,14 @@
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doi={10.1109/TIT.2020.2984247}
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}
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@ARTICLE{original_admm,
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author={Barman, Siddharth and Liu, Xishuo and Draper, Stark C. and Recht, Benjamin},
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journal={IEEE Transactions on Information Theory},
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title={Decomposition Methods for Large Scale LP Decoding},
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year={2013},
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volume={59},
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number={12},
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pages={7870-7886},
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doi={10.1109/TIT.2013.2281372}
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}
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@ -713,7 +713,7 @@ return $\boldsymbol{\hat{c}}$
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\end{tikzpicture}
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\begin{align*}
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\text{minimize}\hspace{2mm} &\boldsymbol{\gamma}^\text{T} \boldsymbol{c} \\
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\text{minimize}\hspace{2mm} &\boldsymbol{\gamma}^\text{T} \tilde{\boldsymbol{c}} \\
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\text{subject to}\hspace{2mm} &
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\boldsymbol{T}_j\tilde{\boldsymbol{c}} \in \mathcal{P}_{d_j}, \hspace{2mm}
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\forall j\in \mathcal{J}
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@ -752,6 +752,87 @@ return $\boldsymbol{\hat{c}}$
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\end{frame}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\subsection{LP Decoding using ADMM}%
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\label{sub:LP Decoding using ADMM}
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\begin{frame}[t]
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\frametitle{LP Decoding using ADMM}
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\begin{itemize}
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\item Slight reformulation of the LCLP:
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\begin{align*}
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\begin{aligned}
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\text{minimize}\hspace{2mm} &\boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
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+ \sum_{j\in\mathcal{J}} g_j\left( \boldsymbol{z}_j \right) \\
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\text{subject to}\hspace{2mm} &
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\boldsymbol{T}_j\tilde{\boldsymbol{c}} = \boldsymbol{z}_j, \hspace{2mm}
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\forall j\in \mathcal{J}
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\end{aligned}\hspace{2mm},\hspace{1cm}
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g_j\left( \boldsymbol{t} \right) := \begin{cases}
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0, & \boldsymbol{t} \in \mathcal{P}_{d_j} \\
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+\infty, & \boldsymbol{t} \not\in \mathcal{P}_{d_j}
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\end{cases}
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\end{align*}
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\item Iterative algorithm:
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\begin{alignat*}{3}
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\tilde{\boldsymbol{c}} &\leftarrow \argmin_{\tilde{\boldsymbol{c}}}
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\left( \boldsymbol{\gamma}^\text{T}\tilde{\boldsymbol{c}}
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+ \frac{\rho}{2}\sum_{j\in\mathcal{J}} \left\Vert
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\boldsymbol{T}_j\tilde{\boldsymbol{c}} - \boldsymbol{z}_j
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+ \boldsymbol{u}_j \right\Vert \right) \\
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\boldsymbol{z}_j &\leftarrow \argmin_{\boldsymbol{z}_j}
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\left( g\left( \boldsymbol{z}_j \right)
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+ \frac{\rho}{2} \left\Vert \boldsymbol{T}_j \tilde{\boldsymbol{c}}
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- \boldsymbol{z}_j + \boldsymbol{u}_j \right\Vert \right),
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\hspace{5mm} &&\forall j\in\mathcal{J} \\
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\boldsymbol{u}_j &\leftarrow \boldsymbol{u}_j
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+ \boldsymbol{T}_j\tilde{\boldsymbol{c}} - \boldsymbol{z}_j,
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\hspace{5mm} &&\forall j\in\mathcal{J}
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% \left( g\left( \boldsymbol{\boldsymbol{z}_j} \right)
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% + \frac{\rho}{2} \left\Vert \boldsymbol{T}_j\tilde{\boldsymbol{c}}
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% - \boldsymbol{z}_j + \boldsymbol{u}_j\right\Vert \right)
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\end{alignat*}
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\end{itemize}
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\end{frame}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{frame}[t]
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\frametitle{LP Decoding using ADMM}
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\begin{itemize}
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\item Simplified rules%
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\footnote{$\left( \boldsymbol{z}_j \right)_i $ is a slight abuse of notation.
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What is actually meant is the component of $\boldsymbol{z}_j$ that is associated
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with the VN $i$, i.e., $\left( \boldsymbol{T}_j^\text{T} \boldsymbol{z}_j \right)_i$.\\
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The same is true for $\left( \boldsymbol{u}_j \right)_i$}%
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:
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\begin{alignat*}{3}
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\tilde{c}_i &\leftarrow \frac{1}{\left| N_v\left( i \right) \right|} \left(
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\sum_{j\in N_v\left( i \right) } \Big( \left( \boldsymbol{z}_j \right)_i
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- \left( \boldsymbol{u}_j \right)_i \Big)
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- \frac{\gamma_i}{\mu} \right)
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\hspace{5mm} && \forall i\in\mathcal{I} \\
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\boldsymbol{z}_j &\leftarrow \Pi_{\mathcal{P}_{d_j}}\left(
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\boldsymbol{T}_j\tilde{\boldsymbol{c}} + \boldsymbol{u}_j \right)
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\hspace{5mm} && \forall j\in\mathcal{J} \\
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\boldsymbol{u}_j &\leftarrow \boldsymbol{u}_j
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+ \boldsymbol{T}_j\tilde{\boldsymbol{c}}
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- \boldsymbol{z}_j
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\hspace{5mm} && \forall j\in\mathcal{J}
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\end{alignat*}
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\item The main computational effort are the projections
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$\Pi_{\mathcal{P}_{d_j}}, \hspace{1mm} j\in\mathcal{J}$. Many
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different approaches exist, e.g., \cite{original_admm},
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\cite{efficient_lp_dec_admm}, \cite{lautern}.
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\item The approach chosen here is the one described in \cite{lautern}
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\end{itemize}
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\end{frame}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%\begin{frame}[t]
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% \frametitle{LP Relaxation}
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