diff --git a/doc/rcfs.tex b/doc/rcfs.tex
index ee8bf99c02809b7b0e08b1fee58b3ed7d62c4be1..afc1e2b4cc3065f5694172f89693fc10d82f4b37 100644
--- a/doc/rcfs.tex
+++ b/doc/rcfs.tex
@@ -155,6 +155,13 @@ can be seen as a product of Gaussians $\prod_{k=1}^K\mathcal{N}(\bm{\mu}_k,\bm{W
 
 $\bm{\hat{\mu}}$ and $\bm{\hat{W}}$ are the same as the solution of~\eqref{eq:quad_costs_multiple} and its Hessian, respectively. Viewing the quadratic cost probabilistically can capture more information about the cost function in the form of the covariance matrix $\bm{\hat{W}}^{-1}$. 
 
+In case of Gaussians close to singularity, to be numerically more robust when computing the product of two Gaussians $\mathcal{N}(\bm{\mu}_1, \bm{\Sigma}_1)$ and $\mathcal{N}(\bm{\mu}_2, \bm{\Sigma}_2)$, the Gaussian $\mathcal{N}(\bm{\hat{\mu}}, \bm{\hat{\Sigma}})$ resulting from the product can be alternatively computed with 
+\begin{equation*}
+	\bm{\hat{\mu}} = \bm{\Sigma}_2 {\left(\bm{\Sigma}_1+\bm{\Sigma}_2\right)}^{-1} \bm{\mu}_1 + 
+	\bm{\Sigma}_1 {\left(\bm{\Sigma}_1+\bm{\Sigma}_2\right)}^{-1} \bm{\mu}_2 , \quad 
+	\bm{\hat{\Sigma}} = \bm{\Sigma}_1 {\left(\bm{\Sigma}_1+\bm{\Sigma}_2\right)}^{-1} \bm{\Sigma}_2.
+\end{equation*}
+
 \begin{figure}
 \centering
 \includegraphics[width=.8\columnwidth]{images/PoG01.png}
@@ -1273,6 +1280,7 @@ Figure \ref{fig:Bezier_2D_eikonal} presents an example in 2D.
 \section{Linear quadratic tracking (LQT)}\label{sec:LQT}
 \begin{flushright}
 \filename{LQT.*}
+\filename{LQT\_nullspace.*}
 \end{flushright}
 
 Linear quadratic tracking (LQT) is a simple form of optimal control that trades off tracking and control costs expressed as quadratic terms over a time horizon, with the evolution of the state described in a linear form. The LQT problem is formulated as the minimization of the cost