[main.tex] Continue cites.

main.tex

@@ -483,8 +483,8 @@
 	\end{column}
 	\begin{column}{.5\linewidth}
 	\begin{itemize}
-		\item OLS\footnote{\cite{OLS}}, OMP\footnote{\cite{OMP}}, for $g(\xb) = \iota_{{\|\xb\|}_0 \leq K}$,
-		\item SLS\footnote{\cite{SLS}}, SBR\footnote{\cite{SBR}} for $g(\xb) = \mu {\|\xb\|}_0$,
+		\item OLS\footnote[frame]{\cite{OLS}}, OMP\footnote[frame]{\cite{OMP}}, for $g(\xb) = \iota_{{\|\xb\|}_0 \leq K}$,
+		\item SLS\footnote[frame]{\cite{SLS}}, SBR\footnote[frame]{\cite{SBR}} for $g(\xb) = \mu {\|\xb\|}_0$,
 		\item $\dots$
 	\end{itemize}
 	\end{column}
@@ -532,11 +532,11 @@
 	\begin{column}{.5\linewidth}
 	$\ell_1$-norm relaxations $g(\xb) = {\|\xb\|}_1$: LASSO, solved by
 	\begin{itemize}
-		\item primal-only proximal algorithms: ISTA\footnote{\cite{Daubechies04}}, FISTA\footnote{\cite{Beck2009_FISTA}},
-		\item primal-dual proximal algorithms: Chambolle-Pock\foonote{\cite{Chambolle2011}}, Condat-Vu,
-		\item ICD\footnote{\cite{Friedman07}},
-		\item Active set\footnote{\cite{Lee_NIPS2006_featuresign}},
-		\item Homotopy\footnote{\cite{Osborne_homotopy}},
+		\item primal-only proximal algorithms: ISTA\footnote[frame]{\cite{Daubechies04}}, FISTA\footnote[frame]{\cite{Beck2009_FISTA}},
+		\item primal-dual proximal algorithms: Chambolle-Pock\footnote[frame]{\cite{Chambolle2011}}, Condat-Vu,
+		\item ICD\footnote[frame]{\cite{Friedman07}},
+		\item Active set\footnote[frame]{\cite{Lee_NIPS2006_featuresign}},
+		\item Homotopy\footnote[frame]{\cite{Osborne_homotopy}},
 		\item $\dots$
 	\end{itemize}
 	\end{column}
@@ -549,7 +549,7 @@
 		\min_{\xb \in \mathbb{R}^Q} \underbrace{\highlight{pink}{\tfrac{1}{2} {\|\yb - \Av \xb\|}_2^2}}_{\text{data fidelity}} + \underbrace{\highlight{lime}{\mu {\|\xb\|}_0}}_{\text{regularizer}}
 	\end{equation*} $\rightarrow$ NP-hard.
 
-	Approach in this thesis: exact resolution\footnote{ref}, from the work of~\cite{Ramzi_these}, using a branch-and-bound algorithm. Such algorithm:
+	Approach in this thesis: exact resolution as\footnote{\cite{Ramzi_2021_art}}\footnote{\cite{Guyard_node_screening}}\footnote{\cite{Hazimeh2021SparseRA}}, using the work of\footnote{\cite{Ramzi_these}}, which developed a dedicated branch-and-bound algorithm. Such algorithm:
 	\begin{itemize}
 		\item implicitely explore all the combinatorial space,
 		\item is garanteed to find the global minimum of the problem in a \emph{finite} number of iterations,