\begin{abstract}
-In this article, a two-stage iterative algorithm is proposed to improve the
-convergence of Krylov based iterative methods, typically those of GMRES variants. The
-principle of the proposed approach is to build an external iteration over the Krylov
-method, and to frequently store its current residual (at each
-GMRES restart for instance). After a given number of outer iterations, a minimization
-step is applied on the matrix composed by the saved residuals, in order to
-compute a better solution and to make new iterations if required. It is proven that
-the proposal has the same convergence properties than the inner embedded method itself.
-Experiments using up to 16,394 cores also show that the proposed algorithm
-runs around 5 or 7 times faster than GMRES.
+In this article, a two-stage iterative algorithm is proposed to improve the
+convergence of Krylov based iterative methods, typically those of GMRES
+variants. The principle of the proposed approach is to build an external
+iteration over the Krylov method, and to frequently store its current residual
+(at each GMRES restart for instance). After a given number of outer iterations,
+a least-squares minimization step is applied on the matrix composed by the saved
+residuals, in order to compute a better solution and to make new iterations if
+required. It is proven that the proposal has the same convergence properties
+than the inner embedded method itself. Experiments using up to 16,394 cores
+also show that the proposed algorithm runs around 5 or 7 times faster than
+GMRES.
\end{abstract}
\begin{IEEEkeywords}
