Added citations for different methods to solve lp decoding problem

latex/thesis/abbreviations.tex

@@ -1,3 +1,10 @@
+\DeclareAcroEnding{gerund}{}{ing}
+
+% For more info on custom endings see https://tex.stackexchange.com/a/268225
+\NewAcroCommand\acg{m}{\acrogerund\UseAcroTemplate{first}{#1}}
+\NewAcroCommand\acsg{m}{\acrogerund\UseAcroTemplate{short}{#1}}
+\NewAcroCommand\aclg{m}{\acrogerund\UseAcroTemplate{long}{#1}}
+
 %
 % A
 %
@@ -64,7 +71,16 @@
 }
 
 %
-%L
+% I
+%
+
+\DeclareAcronym{ILP} {
+    short = ILP,
+    long  = integer linear program
+}
+
+%
+% L
 %
 
 \DeclareAcronym{LCLP}{
@@ -83,8 +99,9 @@
 }
 
 \DeclareAcronym{LP}{
-    short = LP,
+    short              = LP,
-    long  = linear programming
+    long               = linear programming,
+%    long-gerund-form   = linear programming
 }
 
 

latex/thesis/bibliography.bib

@@ -148,3 +148,36 @@
   url={https://web.stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf}
 }
 
+@INPROCEEDINGS{alp,
+  author={Taghavi, Mohammad H. and Siegel, Paul H.},
+  booktitle={2006 IEEE International Symposium on Information Theory}, 
+  title={Adaptive Linear Programming Decoding}, 
+  year={2006},
+  volume={},
+  number={},
+  pages={1374-1378},
+  doi={10.1109/ISIT.2006.262071}
+}
+
+@INPROCEEDINGS{interior_point,
+  author={Vontobel, Pascal O.},
+  booktitle={2008 Information Theory and Applications Workshop}, 
+  title={Interior-point algorithms for linear-programming decoding}, 
+  year={2008},
+  volume={},
+  number={},
+  pages={433-437},
+  doi={10.1109/ITA.2008.4601085}
+}
+
+@ARTICLE{pdd,
+  author={Zhao, Ming-Min and Shi, Qingjiang and Cai, Yunlong and Zhao, Min-Jian and Yu, Quan},
+  journal={IEEE Communications Letters}, 
+  title={Decoding Binary Linear Codes Using Penalty Dual Decomposition Method}, 
+  year={2019},
+  volume={23},
+  number={6},
+  pages={958-962},
+  doi={10.1109/LCOMM.2019.2911277}
+}
+

latex/thesis/chapters/decoding_techniques.tex

@@ -175,8 +175,8 @@ which minimizes the objective function $g$.
 decoding and one, which is an approximation with a more manageable
 representation.
 To solve the resulting linear program, various optimization methods can be
-used.
+used (see for example \cite{alp}, \cite{interior_point},
-\todo{Citation needed}
+\cite{efficient_lp_dec_admm}, \cite{pdd}).
 
 They begin by looking at the \ac{ML} decoding problem%
 \footnote{They assume that all codewords are equally likely to be transmitted,
@@ -685,7 +685,6 @@ The resulting formulation of the relaxed optimization problem becomes:%
         \hspace{5mm}\forall j\in\mathcal{J}.
     \end{aligned} \label{eq:lp:relaxed_formulation}
 \end{align}%
-\todo{Space before $\forall$?}
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -730,7 +729,6 @@ In this form, the problem almost fits the \ac{ADMM} template described in sectio
 \ref{sec:theo:Optimization Methods}, except for the fact that there are multiple equality
 constraints $\boldsymbol{T}_j \tilde{\boldsymbol{c}} = \boldsymbol{z}_j$ and the
 additional constraints $\boldsymbol{z}_j \in \mathcal{P}_{d_j} \, \forall\, j\in\mathcal{J}$.
-\todo{$\forall$ in text?}
 The multiple constraints can be addressed by introducing additional terms in the
 augmented lagrangian:%
 %
@@ -830,7 +828,6 @@ able to be handled at the same time.
 This can also be understood by interpreting the decoding process as a message-passing
 algorithm \cite[Sec. III. D.]{original_admm}, \cite[Sec. II. B.]{efficient_lp_dec_admm},
 as is shown in figure \ref{fig:lp:message_passing}.%
-\todo{Explicitly specify sections?}%
 %
 \begin{figure}[H]
     \centering