v0-20-08-2014

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@@ -538,6 +538,11 @@ Iterative Krylov methods; sparse linear systems; error minimization; PETSC; %à
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+Iterative methods are become more attractive than direct ones to solve large sparse linear systems. They are more effective in a parallel context and require less memory and arithmetic operations than direct methods. 
+
+%les chercheurs ont développer différentes méthodes exemple de méthode iteratives stationnaires et non stationnaires (krylov) 
+%problème de convergence et difficulté dans le passage à l'échelle
+

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@@ -557,7 +562,7 @@ Iterative Krylov methods; sparse linear systems; error minimization; PETSC; %à
 \section{A Krylov two-stage algorithm}
 We propose a two-stage algorithm to solve large sparse linear systems of the form $Ax=b$, where $A\in\mathbb{R}^{n\times n}$ is a sparse and square nonsingular matrix, $x\in\mathbb{R}^n$ is the solution vector and $b\in\mathbb{R}^n$ is the right-hand side. The algorithm is implemented as an inner-outer iteration solver based on iterative Krylov methods. The main key points of our solver are given in Algorithm~\ref{algo:01}. 
 
 \section{A Krylov two-stage algorithm}
 We propose a two-stage algorithm to solve large sparse linear systems of the form $Ax=b$, where $A\in\mathbb{R}^{n\times n}$ is a sparse and square nonsingular matrix, $x\in\mathbb{R}^n$ is the solution vector and $b\in\mathbb{R}^n$ is the right-hand side. The algorithm is implemented as an inner-outer iteration solver based on iterative Krylov methods. The main key points of our solver are given in Algorithm~\ref{algo:01}. 
 

-The outer iteration is implemented as an iterative Krylov method which minimizes some error function over a Krylov sub-space~\cite{saad96}. At every iteration, the sparse linear system $Ax=b$ is solved iteratively with an iterative method as GMRES method~\cite{saad86} and the Krylov sub-space that we used is spanned by a basis $S$ composed of successive solutions issued from the inner iteration
+In order to accelerate the convergence, the outer iteration is implemented as an iterative Krylov method which minimizes some error function over a Krylov sub-space~\cite{saad96}. At every iteration, the sparse linear system $Ax=b$ is solved iteratively with an iterative method as GMRES method~\cite{saad86} and the Krylov sub-space that we used is spanned by a basis $S$ composed of successive solutions issued from the inner iteration

 \begin{equation}
   S = \{x^1, x^2, \ldots, x^s\} \text{,~} s\leq n.
 \end{equation} 
 \begin{equation}
   S = \{x^1, x^2, \ldots, x^s\} \text{,~} s\leq n.
 \end{equation} 


@@ -570,7 +575,7 @@ which is associated with the least-squares problem
    \underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2
 \label{eq:01}
 \end{equation}
    \underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2
 \label{eq:01}
 \end{equation}

-such that $R=AS$ is a dense rectangular matrix in $\mathbb{R}^{n\times s}$, $s\ll n$, and $R^T$ denotes the transpose of matrix $R$. We use an iterative method to solve the least-squares problem~(\ref{eq:01}) as CGLS~\cite{hestenes52} or LSQR~\cite{paige82} which is more appropriate that a direct method in the parallel context.
+such that $R=AS$ is a dense rectangular matrix in $\mathbb{R}^{n\times s}$, $s\ll n$, and $R^T$ denotes the transpose of matrix $R$. We use an iterative method to solve the least-squares problem~(\ref{eq:01}) as CGLS~\cite{hestenes52} or LSQR~\cite{paige82} methods which is more appropriate than a direct method in the parallel context.

 
 \begin{algorithm}[t]
 \caption{A Krylov two-stage algorithm}
 
 \begin{algorithm}[t]
 \caption{A Krylov two-stage algorithm}


@@ -580,12 +585,12 @@ such that $R=AS$ is a dense rectangular matrix in $\mathbb{R}^{n\times s}$, $s\l
   \State Set the initial guess $x^0$
   \For {$k=1,2,3,\ldots$ until convergence}
     \State Solve iteratively $Ax^k=b$
   \State Set the initial guess $x^0$
   \For {$k=1,2,3,\ldots$ until convergence}
     \State Solve iteratively $Ax^k=b$

-    \State Add vector $x^k$ to Krylov basis $S$
+    \State Add vector $x^k$ to Krylov sub-space basis $S$

     \If {$k$ mod $s=0$ {\bf and} not convergence}
       \State Compute dense matrix $R=AS$
       \State Solve least-squares problem $\underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2$
       \State Compute minimizer $x^k=S\alpha$
     \If {$k$ mod $s=0$ {\bf and} not convergence}
       \State Compute dense matrix $R=AS$
       \State Solve least-squares problem $\underset{\alpha\in\mathbb{R}^{s}}{min}\|b-R\alpha\|_2$
       \State Compute minimizer $x^k=S\alpha$

-      \State Reinitialize Krylov basis $S$
+      \State Reinitialize Krylov sub-space basis $S$

     \EndIf
   \EndFor
 \end{algorithmic}
     \EndIf
   \EndFor
 \end{algorithmic}


@@ -653,7 +658,7 @@ such that $R=AS$ is a dense rectangular matrix in $\mathbb{R}^{n\times s}$, $s\l
 
 \bibitem{saad86} Y.~Saad and M.~H.~Schultz, \emph{GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems}, SIAM Journal on Scientific and Statistical Computing, 7(3):856--869, 1986.
 
 
 \bibitem{saad86} Y.~Saad and M.~H.~Schultz, \emph{GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems}, SIAM Journal on Scientific and Statistical Computing, 7(3):856--869, 1986.
 

-\bibitem{saad96} Y.~Saad and M.~H.~Schultz, \emph{Iterative Methods for Sparse Linear Systems}, PWS Publishing, New York, 1996.
+\bibitem{saad96} Y.~Saad, \emph{Iterative Methods for Sparse Linear Systems}, PWS Publishing, New York, 1996.

 
 \bibitem{hestenes52} M.~R.~Hestenes and E.~Stiefel, \emph{Methods of conjugate gradients for solving linear system}, Journal of Research of National Bureau of Standards, B49:409--436, 1952.
 
 
 \bibitem{hestenes52} M.~R.~Hestenes and E.~Stiefel, \emph{Methods of conjugate gradients for solving linear system}, Journal of Research of National Bureau of Standards, B49:409--436, 1952.