Added bibliography entry for original ADMM paper

latex/thesis/bibliography.bib

@@ -121,3 +121,14 @@
   doi={10.1109/TIT.2020.2984247}
 }
 
+@ARTICLE{original_admm,
+  author={Barman, Siddharth and Liu, Xishuo and Draper, Stark C. and Recht, Benjamin},
+  journal={IEEE Transactions on Information Theory}, 
+  title={Decomposition Methods for Large Scale LP Decoding}, 
+  year={2013},
+  volume={59},
+  number={12},
+  pages={7870-7886},
+  doi={10.1109/TIT.2013.2281372}
+}
+

latex/thesis/chapters/decoding_techniques.tex

@@ -692,9 +692,9 @@ The resulting formulation of the relaxed optimization problem becomes:%
         \begin{itemize}
             \item Distributed nature, making it a competitor to BP
                 (which can also be implemented in a distributed manner)
-                (See original ADMM paper)
+                \cite[Sec. I]{original_admm}
             \item Computational performance similar to BP has been demnonstrated
-                (See original ADMM paper)
+                \cite[Sec. I]{original_admm}
         \end{itemize}
     \item Adaptive linear programming?
     \item How ADMM is adapted to LP decoding

latex/thesis/chapters/introduction.tex

@@ -6,10 +6,10 @@
     \item Problem definition
     \item Motivation
         \begin{itemize}
-            \item Error floor when decoding with BP (seems to not be persent with LP decoding - 
+            \item Error floor when decoding with BP (seems to not be persent with LP decoding
-                see original ADMM paper introduction)
+                \cite[Sec. I]{original_admm})
             \item Strong theoretical guarantees that allow for better and better approximations
-                for ML decoding (See original ADMM peper introduction)
+                for ML decoding \cite[Sec. I]{original_admm}
         \end{itemize}
     \item Results summary
 \end{itemize}