$S$, which is composed by the $s$ last solutions that have been computed during
the inner iterations phase.
-At each $s$ iterations, the minimization step is applied in order to
+At each $s$ iterations, another kind of minimization step is applied in order to
compute a new solution $x$. For that, the previous residuals of $Ax=b$ are computed by
the inner iterations with $(b-AS)$. The minimization of the residuals is obtained by
\begin{equation}
\underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2
\label{eq:01}
\end{equation}
-with $R=AS$. Then the new solution $x$ is computed with $x=S\alpha$.
+with $R=AS$. The new solution $x$ is then computed with $x=S\alpha$.
In practice, $R$ is a dense rectangular matrix belonging in $\mathbb{R}^{n\times s}$,
\label{algo:01}
\end{algorithm}
-Algorithm~\ref{algo:01} summarizes the principle of our method. The outer
-iteration is inside the for loop. Line~\ref{algo:solve}, the Krylov method is
+Algorithm~\ref{algo:01} summarizes the principle of the proposed method. The outer
+iteration is inside the \emph{for} loop. Line~\ref{algo:solve}, the Krylov method is
called for a maximum of $max\_iter_{kryl}$ iterations. In practice, we suggest to set this parameter
equals to the restart number of the GMRES-like method. Moreover, a tolerance
threshold must be specified for the solver. In practice, this threshold must be
