X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/GMRES2stage.git/blobdiff_plain/eab5f4a9438f75792a4317e54757ff950cd04faf..96dcb243cd0224275446d6d85fab46ed72241a22:/paper.tex

diff --git a/paper.tex b/paper.tex
index d0fac64..9035059 100644
--- a/paper.tex
+++ b/paper.tex
@@ -790,7 +790,12 @@ Suppose now that the claim holds for all $m=1, 2, \hdots, k-1$, that is, $\foral
 We will show that the statement holds too for $r_k$. Two situations can occur:
 \begin{itemize}
 \item If $k \not\equiv 0 ~(\textrm{mod}\ m)$, then the TSIRM algorithm consists in executing GMRES once. In that case and by using the inductive hypothesis, we obtain either $||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_{k-1}||\leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||$ if $A$ is positive, or $\|r_k\| \leqslant \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{m/2} \|r_{k-1}\|$ $\leqslant$ $\left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_{0}\|$ in the positive definite case.
-\item Else, the TSIRM algorithm consists in two stages: a first GMRES($m$) execution leads to a temporary $x_k$ whose residue satisfies $||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_{k-1}||\leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||$, and a least squares resolution.
+\item Else, the TSIRM algorithm consists in two stages: a first GMRES($m$) execution leads to a temporary $x_k$ whose residue satisfies:
+\begin{itemize}
+\item $||r_k|| \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{m}{2}} ||r_{k-1}||\leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||$ in the positive case, 
+\item $\|r_k\| \leqslant \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{m/2} \|r_{k-1}\|$ $\leqslant$ $\left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_{0}\|$ in the positive definite one,
+\end{itemize}
+and a least squares resolution.
 Let $\operatorname{span}(S) = \left \{ {\sum_{i=1}^k \lambda_i v_i \Big| k \in \mathbb{N}, v_i \in S, \lambda _i \in \mathbb{R}} \right \}$ be the linear span of a set of real vectors $S$. So,\\
 $\min_{\alpha \in \mathbb{R}^s} ||b-R\alpha ||_2 = \min_{\alpha \in \mathbb{R}^s} ||b-AS\alpha ||_2$
 
@@ -801,14 +806,16 @@ $\begin{array}{ll}
 & \leqslant \min_{\lambda \in \mathbb{R}} ||b-\lambda Ax_{k} ||_2\\
 & \leqslant ||b-Ax_{k}||_2\\
 & = ||r_k||_2\\
-& \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||,
+& \leqslant \left(1-\dfrac{\alpha}{\beta}\right)^{\frac{km}{2}} ||r_0||, \textrm{ if $A$ is positive,}\\
+& \leqslant \left( 1-\frac{\lambda_{\mathrm{min}}^2(1/2(A^T + A))}{ \lambda_{\mathrm{max}}(A^T A)} \right)^{km/2} \|r_{0}\|, \textrm{ if $A$ is}\\
+& \textrm{positive definite,} 
 \end{array}$
 \end{itemize}
 which concludes the induction and the proof.
 \end{proof}
 
-We can remark that, at each iterate, the residue of the TSIRM algorithm is lower 
-than the one of the GMRES method.
+%We can remark that, at each iterate, the residue of the TSIRM algorithm is lower 
+%than the one of the GMRES method.
 
 %%%*********************************************************
 %%%*********************************************************
@@ -816,13 +823,13 @@ than the one of the GMRES method.
 \label{sec:05}
 
 
-In order to see the influence of our algorithm with only one processor, we first
-show a comparison with GMRES or FGMRES and our algorithm. In Table~\ref{tab:01},
-we  show the  matrices we  have  used and  some of  them characteristics.  Those
-matrices  are   chosen  from   the  Davis  collection   of  the   University  of
-Florida~\cite{Dav97}. They are matrices arising in real-world applications.  For
-all the  matrices, the name,  the field,  the number of  rows and the  number of
-nonzero elements are given.
+In order to see the behavior of the proposal when considering only one processor, a first
+comparison with GMRES or FGMRES and the new algorithm detailed previously has been experimented. 
+Matrices that have been used with their characteristics (names, fields, rows, and nonzero coefficients) are detailed in 
+Table~\ref{tab:01}.  These latter, which are real-world applications matrices, 
+have been extracted 
+ from   the  Davis  collection,   University  of
+Florida~\cite{Dav97}.
 
 \begin{table}[htbp]
 \begin{center}
@@ -842,8 +849,9 @@ torso3             & 2D/3D problem & 259,156 & 4,429,042 \\
 \label{tab:01}
 \end{center}
 \end{table}
-
-The following  parameters have been chosen  for our experiments.   As by default
+Chosen parameters are detailed below.
+%The following  parameters have been chosen  for our experiments.   
+As by default
 the restart  of GMRES is performed every  30 iterations, we have  chosen to stop
 the GMRES every 30 iterations (\emph{i.e.} $max\_iter_{kryl}=30$).  $s$ is set to 8. CGLS is
 chosen  to minimize  the least-squares  problem with  the  following parameters:
@@ -923,8 +931,16 @@ by core. The Juqueen architecture is composed  of IBM PowerPC A2 at 1.6 GHz with
 speed network.
 
 
+In  many situations, using  preconditioners is  essential in  order to  find the
+solution of a linear system.  There are many preconditioners available in PETSc.
+For parallel applications all  the preconditioners based on matrix factorization
+are  not  available. In  our  experiments, we  have  tested  different kinds  of
+preconditioners, however  as it is  not the subject  of this paper, we  will not
+present results with many preconditioners. In  practise, we have chosen to use a
+multigrid (mg)  and successive  over-relaxation (sor). For  more details  on the
+preconditioner in PETSc please consult~\cite{petsc-web-page}.
+
 
-{\bf Description of preconditioners}\\
 
 \begin{table*}[htbp]
 \begin{center}