\end{proposition}
+We can now claim that,
+\begin{proposition}
+If $A$ is a positive real matrix and GMRES($m$) is used as solver, then the TSIRM algorithm is convergent.
+\end{proposition}
+
+\begin{proof}
+Let $r_k = b-Ax_k$, where $x_k$ is the approximation of the solution after the
+$k$-th iterate of TSIRM.
+We will prove that $r_k \rightarrow 0$ when $k \rightarrow +\infty$.
+
+Each step of the TSIRM algorithm \\
+$\min_{\alpha \in \mathbb{R}^s} ||b-R\alpha ||_2 = \min_{\alpha \in \mathbb{R}^s} ||b-AS\alpha ||_2$
+
+$\begin{array}{ll}
+& = \min_{x \in Vect\left(x_0, x_1, \hdots, x_{k-1} \right)} ||b-AS\alpha ||_2\\
+& \leqslant \min_{x \in Vect\left( S_{k-1} \right)} ||b-Ax ||_2\\
+& \leqslant ||b-Ax_{k-1}||
+\end{array}$
+\end{proof}
+
%%%*********************************************************
%%%*********************************************************
In Figure~\ref{fig:01}, the number of iterations per second corresponding to
-Table~\ref{tab:01} is displayed. It can be noticed that the number of
+Table~\ref{tab:03} is displayed. It can be noticed that the number of
iterations per second of FMGRES is constant whereas it decreases with TSIRM with
both preconditioners. This can be explained by the fact that when the number of
cores increases the time for the least-squares minimization step also increases but, generally,
% that's all folks
\end{document}
-
