\algnewcommand\Output{\item[\algorithmicoutput]}
\newtheorem{proposition}{Proposition}
+ \newtheorem{proof}{Proof}
\begin{document}
%
% use a multiple column layout for up to two different
% affiliations
- \author{\IEEEauthorblockN{Rapha\"el Couturier\IEEEauthorrefmark{1}, Lilia Ziane Khodja \IEEEauthorrefmark{2}, and Christophe Guyeux\IEEEauthorrefmark{1}}
+ \author{\IEEEauthorblockN{Rapha\"el Couturier\IEEEauthorrefmark{1}, Lilia Ziane Khodja\IEEEauthorrefmark{2}, and Christophe Guyeux\IEEEauthorrefmark{1}}
\IEEEauthorblockA{\IEEEauthorrefmark{1} Femto-ST Institute, University of Franche Comte, France\\
Email: \{raphael.couturier,christophe.guyeux\}@univ-fcomte.fr}
\IEEEauthorblockA{\IEEEauthorrefmark{2} INRIA Bordeaux Sud-Ouest, France\\
\begin{equation}
||r_m|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_0|| ,
\end{equation}
- where $\alpha = \lambda_min(M)^2$ and $\beta = \lambda_max(A^T A)$, which proves
+ where $\alpha = \lambda_{min}(M)^2$ and $\beta = \lambda_{max}(A^T A)$, which proves
the convergence of GMRES($m$) for all $m$ under that assumption regarding $A$.
\end{proposition}
+ We can now claim that,
+ \begin{proposition}
+ If $A$ is a positive real matrix, 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 $k \rightarrow +\infty$.
+ \end{proof}
+
%%%*********************************************************
%%%*********************************************************
\section{Experiments using PETSc}
In the following larger experiments are described on two large scale architectures: Curie and Juqeen... {\bf description...}\\
-{\bf Description of preconditioners}
+{\bf Description of preconditioners}\\
\begin{table*}[htbp]
\begin{center}
\hline
\end{tabular}
-\caption{Comparison of FGMRES and TSIRM with FGMRES for example ex15 of PETSc with two preconditioner (mg and sor) with 25,000 components per core on Juqueen (threshold 1e-3, restart=30, s=12), time is expressed in seconds.}
+\caption{Comparison of FGMRES and TSIRM with FGMRES for example ex15 of PETSc with two preconditioners (mg and sor) with 25,000 components per core on Juqueen (threshold 1e-3, restart=30, s=12), time is expressed in seconds.}
\label{tab:03}
\end{center}
\end{table*}
Table~\ref{tab:03} shows the execution times and the number of iterations of
-example ex15 of PETSc on the Juqueen architecture. Differents number of cores
-are studied rangin from 2,048 upto 16,383. Two preconditioners have been
-tested. For those experiments, the number of components (or unknown of the
+example ex15 of PETSc on the Juqueen architecture. Different numbers of cores
+are studied ranging from 2,048 up-to 16,383. Two preconditioners have been
+tested: {\it mg} and {\it sor}. For those experiments, the number of components (or unknowns of the
problems) per processor is fixed to 25,000, also called weak scaling. This
number can seem relatively small. In fact, for some applications that need a lot
of memory, the number of components per processor requires sometimes to be
-In this Table, we can notice that TSIRM is always faster than FGMRES. The last
+In Table~\ref{tab:03}, we can notice that TSIRM is always faster than FGMRES. The last
column shows the ratio between FGMRES and the best version of TSIRM according to
the minimization procedure: CGLS or LSQR. Even if we have computed the worst
-case between CGLS and LSQR, it is clear that TSIRM is alsways faster than
-FGMRES. For this example, the multigrid preconditionner is faster than SOR. The
+case between CGLS and LSQR, it is clear that TSIRM is always faster than
+FGMRES. For this example, the multigrid preconditioner is faster than SOR. The
gain between TSIRM and FGMRES is more or less similar for the two
preconditioners. Looking at the number of iterations to reach the convergence,
it is obvious that TSIRM allows the reduction of the number of iterations. It
% that's all folks
\end{document}
-
-
