Made proximal operator bold

latex/thesis/chapters/decoding_techniques.tex

@@ -1017,7 +1017,7 @@ $\gamma h\left( \tilde{\boldsymbol{x}} \right) $ has to be computed.
 It is then immediately approximated with gradient-descent:%
 %
 \begin{align*}
-    \text{prox}_{\gamma h} \left( \tilde{\boldsymbol{x}} \right) &\equiv
+    \textbf{prox}_{\gamma h} \left( \tilde{\boldsymbol{x}} \right) &\equiv
         \argmin_{\boldsymbol{t} \in \mathbb{R}^n}
             \left( \gamma h\left( \boldsymbol{t} \right) +
                 \frac{1}{2} \lVert \boldsymbol{t} - \tilde{\boldsymbol{x}} \rVert \right)\\

latex/thesis/chapters/theoretical_background.tex

@@ -274,12 +274,12 @@ desired \cite[Sec. 15.3]{ryan_lin_2009}.
 
 \textit{Proximal algorithms} are algorithms for solving convex optimization
 problems, that rely on the use of \textit{proximal operators}.
-The proximal operator $\text{prox}_f : \mathbb{R}^n \rightarrow \mathbb{R}^n$
+The proximal operator $\textbf{prox}_f : \mathbb{R}^n \rightarrow \mathbb{R}^n$
 of a function $f:\mathbb{R}^n \rightarrow \mathbb{R}$ is defined by
 \cite[Sec. 1.1]{proximal_algorithms}%
 %
 \begin{align*}
-    \text{prox}_{\lambda f}\left( \boldsymbol{v} \right) = \argmin_{\boldsymbol{x}} \left(
+    \textbf{prox}_{\lambda f}\left( \boldsymbol{v} \right) = \argmin_{\boldsymbol{x}} \left(
         f\left( \boldsymbol{x} \right) + \frac{1}{2\lambda}\lVert \boldsymbol{x}
             - \boldsymbol{v} \rVert_2^2 \right)
 .\end{align*}
@@ -300,7 +300,7 @@ and minimizing $g$ using the proximal operator
 %
 \begin{align*}
     \boldsymbol{x} \leftarrow \boldsymbol{x} - \lambda \nabla f\left( \boldsymbol{x} \right) \\
-    \boldsymbol{x} \leftarrow \text{prox}_{\lambda g} \left( \boldsymbol{x} \right) 
+    \boldsymbol{x} \leftarrow \textbf{prox}_{\lambda g} \left( \boldsymbol{x} \right) 
 ,\end{align*}
 %
 Since $g$ is minimized with the proximal operator and is thus not required