\hyphenation{op-tical net-works semi-conduc-tor}
+
+\usepackage{algorithm}
+\usepackage{algpseudocode}
+\usepackage{amsmath}
+\usepackage{amssymb}
+
+\algnewcommand\algorithmicinput{\textbf{Input:}}
+\algnewcommand\Input{\item[\algorithmicinput]}
+
+\algnewcommand\algorithmicoutput{\textbf{Output:}}
+\algnewcommand\Output{\item[\algorithmicoutput]}
+
+
+
\begin{document}
%
% paper title
% can use linebreaks \\ within to get better formatting as desired
-\title{Bare Demo of IEEEtran.cls for IEEECS Conferences}
+\title{A Krylov two-stage algorithm to solve large sparse linear systems}
+%où
+%\title{A two-stage algorithm with error minimization to solve large sparse linear systems}
+%où
+%\title{???}
% author names and affiliations
% use a multiple column layout for up to two different
% affiliations
-\author{\IEEEauthorblockN{Authors Name/s per 1st Affiliation (Author)}
-\IEEEauthorblockA{line 1 (of Affiliation): dept. name of organization\\
-line 2: name of organization, acronyms acceptable\\
-line 3: City, Country\\
-line 4: Email: name@xyz.com}
+\author{\IEEEauthorblockN{Rapha\"el Couturier}
+\IEEEauthorblockA{Femto-ST Institute - DISC Department\\
+Universit\'e de Franche-Comt\'e, IUT de Belfort-Montb\'eliard\\
+19 avenue de Mar\'echal Juin, BP 527 \\
+90016 Belfort Cedex, France\\
+Email: raphael.couturier@univ-fcomte.fr}
\and
-\IEEEauthorblockN{Authors Name/s per 2nd Affiliation (Author)}
-\IEEEauthorblockA{line 1 (of Affiliation): dept. name of organization\\
-line 2: name of organization, acronyms acceptable\\
-line 3: City, Country\\
-line 4: Email: name@xyz.com}
+\IEEEauthorblockN{Lilia Ziane Khodja}
+\IEEEauthorblockA{Centre de Recherche INRIA Bordeaux Sud-Ouest\\
+200 avenue de la Vieille Tour\\
+33405 Talence Cedex, France\\
+Email: lilia.ziane@inria.fr}
}
% conference papers do not typically use \thanks and this command
\begin{abstract}
-The abstract goes here. DO NOT USE SPECIAL CHARACTERS, SYMBOLS, OR MATH IN YOUR TITLE OR ABSTRACT.
-
+%The abstract goes here. DO NOT USE SPECIAL CHARACTERS, SYMBOLS, OR MATH IN YOUR TITLE OR ABSTRACT.
\end{abstract}
\begin{IEEEkeywords}
-component; formatting; style; styling;
-
+Iterative Krylov methods; sparse linear systems; error minimization; PETSC; %à voir...
\end{IEEEkeywords}
-\section{Introduction}
-% no \IEEEPARstart
-This demo file is intended to serve as a ``starter file''
-for IEEE conference papers produced under \LaTeX\ using
-IEEEtran.cls version 1.7 and later.
-
-All manuscripts must be in English. These guidelines include complete descriptions of the fonts, spacing, and related information for producing your proceedings manuscripts. Please follow them and if you have any questions, direct them to the production editor in charge of your proceedings at Conference Publishing Services (CPS): Phone +1 (714) 821-8380 or Fax +1 (714) 761-1784.
-% You must have at least 2 lines in the paragraph with the drop letter
-% (should never be an issue)
-
-\subsection{Subsection Heading Here}
-Subsection text here.
-
-
-\subsubsection{Subsubsection Heading Here}
-Subsubsection text here.
-
-\section{Type style and Fonts}
-Wherever Times is specified, Times Roman or Times New Roman may be used. If neither is available on your system, please use the font closest in appearance to Times. Avoid using bit-mapped fonts if possible. True-Type 1 or Open Type fonts are preferred. Please embed symbol fonts, as well, for math, etc.
-
% An example of a floating figure using the graphicx package.
% Note that \label must occur AFTER (or within) \caption.
+%%%*********************************************************
+%%%*********************************************************
+\section{Introduction}
+% no \IEEEPARstart
+% You must have at least 2 lines in the paragraph with the drop letter
+% (should never be an issue)
+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. A number of iterative methods are proposed and adapted by many researchers and the increased need for solving very large sparse linear systems triggered the development of efficient iterative techniques suitable for the parallel processing.
+
+The most successful iterative methods currently available are those based on the Krylov subspace which consists in forming a basis of a sequence of successive matrix powers times the initial residual. These methods are based on orthogonality of vectors of the Krylov subspace basis to solve generalized linear systems. The most well-known iterative Krylov subspace methods are Conjugate Gradient method and GMRES method (generalized minimal residual).
+
+%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
+
+%%%*********************************************************
+%%%*********************************************************
+
+
+
+%%%*********************************************************
+%%%*********************************************************
+\section{Related works}
+%Wherever Times is specified, Times Roman or Times New Roman may be used. If neither is available on your system, please use the font closest in appearance to Times. Avoid using bit-mapped fonts if possible. True-Type 1 or Open Type fonts are preferred. Please embed symbol fonts, as well, for math, etc.
+%%%*********************************************************
+%%%*********************************************************
+
+
+
+%%%*********************************************************
+%%%*********************************************************
+\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}.
+
+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}
+The advantage of such a Krylov subspace is that we neither need an orthogonal basis nor any synchronization between processors to generate this basis. The algorithm is periodically restarted every $s$ iterations with a new initial guess $x=S\alpha$ which minimizes the residual norm $\|b-Ax\|_2$ over the Krylov subspace spanned by vectors of $S$, where $\alpha$ is a solution of the normal equations
+\begin{equation}
+ R^TR\alpha = R^Tb,
+\end{equation}
+which is associated with the least-squares problem
+\begin{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} methods which is more appropriate than a direct method in the parallel context.
+
+\begin{algorithm}[t]
+\caption{A Krylov two-stage algorithm}
+\begin{algorithmic}[1]
+ \Input $A$ (sparse matrix), $b$ (right-hand side)
+ \Output $x$ (solution vector)\vspace{0.2cm}
+ \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 subspace 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 $x^k=S\alpha$
+ \State Reinitialize Krylov subspace basis $S$
+ \EndIf
+ \EndFor
+\end{algorithmic}
+\label{algo:01}
+\end{algorithm}
+%%%*********************************************************
+%%%*********************************************************
+
+
+
+%%%*********************************************************
+%%%*********************************************************
+\section{Experiments using petsc}
+%%%*********************************************************
+%%%*********************************************************
+
+
+
+%%%*********************************************************
+%%%*********************************************************
\section{Conclusion}
-The conclusion goes here. this is more of the conclusion
+%The conclusion goes here. this is more of the conclusion
+%%%*********************************************************
+%%%*********************************************************
+
+
% conference papers do not normally have an appendix
+
% use section* for acknowledgement
+%%%*********************************************************
+%%%*********************************************************
\section*{Acknowledgment}
-
-
-The authors would like to thank...
-more thanks here
+%The authors would like to thank...
+%more thanks here
+%%%*********************************************************
+%%%*********************************************************
% trigger a \newpage just before the given reference
% (used to reserve space for the reference number labels box)
\begin{thebibliography}{1}
-\bibitem{IEEEhowto:kopka}
-H.~Kopka and P.~W. Daly, \emph{A Guide to \LaTeX}, 3rd~ed.\hskip 1em plus
- 0.5em minus 0.4em\relax Harlow, England: Addison-Wesley, 1999.
+\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, \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{paige82} C.~C.~Paige and A.~M.~Saunders, \emph{LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares}, ACM Trans. Math. Softw. 8(1):43--71, 1982.
\end{thebibliography}
