Fixed typos; Added citation
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@ -189,7 +189,7 @@ making the \ac{ML} and \ac{MAP} decoding problems equivalent.}%
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.\end{align}%
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.\end{align}%
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%
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Assuming a memoryless channel, equation (\ref{eq:lp:ml}) can be rewritten in terms
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Assuming a memoryless channel, equation (\ref{eq:lp:ml}) can be rewritten in terms
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of the \acp{LLR} $\gamma_i$ \cite[Sec 2.5]{feldman_thesis}:%
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of the \acp{LLR} $\gamma_i$ \cite[Sec. 2.5]{feldman_thesis}:%
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%
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%
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\begin{align*}
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\begin{align*}
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\hat{\boldsymbol{c}} = \argmin_{\boldsymbol{c}\in\mathcal{C}}
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\hat{\boldsymbol{c}} = \argmin_{\boldsymbol{c}\in\mathcal{C}}
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@ -236,6 +236,7 @@ decoding, redefining the constraints in terms of the \text{codeword polytope}
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%
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%
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which represents the \textit{convex hull} of all possible codewords,
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which represents the \textit{convex hull} of all possible codewords,
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i.e., the convex set of linear combinations of all codewords.
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i.e., the convex set of linear combinations of all codewords.
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This corresponds to simply lifting the integer requirement.
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However, since the number of constraints needed to characterize the codeword
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However, since the number of constraints needed to characterize the codeword
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polytope is exponential in the code length, this formulation is relaxed further.
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polytope is exponential in the code length, this formulation is relaxed further.
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By observing that each check node defines its own local single parity-check
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By observing that each check node defines its own local single parity-check
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@ -249,8 +250,7 @@ This consideration leads to constraints, that can be described as follows
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\boldsymbol{T}_j \tilde{\boldsymbol{c}} \in \mathcal{P}_{d_j}
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\boldsymbol{T}_j \tilde{\boldsymbol{c}} \in \mathcal{P}_{d_j}
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\hspace{5mm}\forall j\in \mathcal{J}
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\hspace{5mm}\forall j\in \mathcal{J}
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,\end{align*}%
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,\end{align*}%
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\todo{Explicitly state that the first relaxation is essentially just lifing the integer
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%
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requirement}%
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where $\mathcal{P}_{d_j}$ is the \textit{check polytope}, the convex hull of all
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where $\mathcal{P}_{d_j}$ is the \textit{check polytope}, the convex hull of all
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binary vectors of length $d_j$ with even parity%
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binary vectors of length $d_j$ with even parity%
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\footnote{Essentially $\mathcal{P}_{d_j}$ is the set of vectors that satisfy
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\footnote{Essentially $\mathcal{P}_{d_j}$ is the set of vectors that satisfy
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@ -827,7 +827,7 @@ It is then immediately approximated with gradient-descent:%
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\begin{align*}
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\begin{align*}
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\text{prox}_{\gamma h} \left( \boldsymbol{x} \right) &\equiv
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\text{prox}_{\gamma h} \left( \boldsymbol{x} \right) &\equiv
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\argmin_{\boldsymbol{t} \in \mathbb{R}^n}
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\argmin_{\boldsymbol{t} \in \mathbb{R}^n}
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\left( \gamma h\left( \boldsymbol{x} \right) +
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\left( \gamma h\left( \boldsymbol{t} \right) +
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\frac{1}{2} \lVert \boldsymbol{t} - \boldsymbol{x} \rVert \right)\\
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\frac{1}{2} \lVert \boldsymbol{t} - \boldsymbol{x} \rVert \right)\\
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&\approx \boldsymbol{r} - \gamma \nabla h \left( \boldsymbol{r} \right),
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&\approx \boldsymbol{r} - \gamma \nabla h \left( \boldsymbol{r} \right),
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\hspace{5mm} \gamma > 0, \text{ small}
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\hspace{5mm} \gamma > 0, \text{ small}
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@ -847,7 +847,7 @@ as it keeps the effect of $h\left( \boldsymbol{x} \right) $ on the landscape
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of the objective function small.
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of the objective function small.
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Otherwise, unwanted stationary points, including local minima, are introduced.
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Otherwise, unwanted stationary points, including local minima, are introduced.
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The authors say that in practice, the value of $\gamma$ should be adjusted
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The authors say that in practice, the value of $\gamma$ should be adjusted
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according to the decoding performance.
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according to the decoding performance \cite[Sec. 3.1]{proximal_paper}.
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%The components of the gradient of the code-constraint polynomial can be computed as follows:%
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%The components of the gradient of the code-constraint polynomial can be computed as follows:%
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%%
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%%
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