algorithmss. In this paper, we will reconsider the use of a multisplitting
method. In opposition to traditional multisplitting method that suffer from slow
convergence, as proposed in~\cite{huang1993krylov}, the use of a minimization
-process can drastically improve the convergence.
+process can drastically improve the convergence.\\
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-In this work we develop a new parallel two-stage algorithm for large-scale clusters. Our objective is to mix between Krylov based iterative methods and the multisplitting method to improve the scalability. In fact Krylov subspace methods are well-known for their good convergence compared to others iterative methods. So our main contribution is to use the multisplitting method which splits the problem to solve into different blocks in order to reduce the large amount of communications and, to implement both inner and outer iterations as Krylov subspace iterations improving the convergence of the multisplitting algorithm.
+\noindent {\bf Contributions:}\\
+In this work we develop a new parallel two-stage algorithm for large-scale clusters. Our objective is to mix between Krylov based iterative methods and the multisplitting method to improve the scalability. In fact Krylov subspace methods are well-known for their good convergence compared to others iterative methods. So our main contribution is to use the multisplitting method which splits the problem to solve into different blocks in order to reduce the large amount of communications and, to implement both inner and outer iterations as Krylov subspace iterations improving the convergence of the multisplitting algorithm.\\
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\begin{figure}[htbp]
\centering
\begin{tabular}{c}
-\includegraphics[width=0.8\textwidth]{weak_scaling_280k} \\ (a) \includegraphics[width=0.8\textwidth]{weak_scaling_280K}\\
+\includegraphics[width=0.8\textwidth]{weak_scaling_280k} \\ \includegraphics[width=0.8\textwidth]{weak_scaling_280K}\\
\end{tabular}
\caption{Weak scaling with 3 blocks of cores}
-\label{fig:001}
+\label{fig:002}
\end{figure}
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