suit

diff --git a/paper.tex b/paper.tex
index c66f8c71ae1ec2704b0db181df987640f4a3d97f..35d9ed7de2a73014af8158abb6c5711ed81210f9 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -637,8 +637,8 @@ direct method in the parallel context.
   \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 $Ax^k=b$
+  \For {$k=1,2,3,\ldots$ until convergence} \label{algo:conv}
+    \State Solve iteratively $Ax^k=b$  \label{algo:solve}
     \State $S_{k~mod~s}=x^k$ 
     \If {$k$ mod $s=0$ {\bf and} not convergence}
       \State Compute dense matrix $R=AS$
@@ -690,12 +690,28 @@ torso3             & 2D/3D problem & 259,156 & 4,429,042 \\
 \end{center}
 \end{table}
 
-In  table~\ref{tab:02}, some  experiments comparing  the sovling  of  the linear
+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     (line     \ref{algo:solve}    in
+Algorithm~\ref{algo:01}).   $s$ is  set to  8. CGLS  is chosen  to  minimize the
+least-squares  problem.  Two  conditions  are  used to  stop  CGLS,  either  the
+precision is under $1e-40$ or the  number of iterations is greater to $20$.  The
+external   precision    is   set    to   $1e-10$   (line    \ref{algo:conv}   in
+Algorithm~\ref{algo:01}).  Those  experiments have been performed  on a Intel(R)
+Core(TM) i7-3630QM CPU @ 2.40GHz with the version 3.5.1 of PETSc.
+
+
+In  Table~\ref{tab:02}, some  experiments comparing  the solving  of  the linear
 systems obtained with the previous matrices  with a GMRES variant and with out 2
 stage algorithm are  given. In the second column, it can  be noticed that either
 gmres or fgmres is used to  solve the linear system.  According to the matrices,
-different preconditioner is used.  With the 2  stage algorithm, the same
-solver and the same preconditionner is used.
+different preconditioner is  used.  With the 2 stage  algorithm, the same solver
+and  the same  preconditionner  is used.   This  Table shows  that  the 2  stage
+algorithm  can  drastically  reduce  the  number  of  iterations  to  reach  the
+convergence when the  number of iterations for the normal GMRES  is more or less
+greater than  500. In fact  this also depends  on tow parameters: the  number of
+iterations  to  stop  GMRES  and   the  number  of  iterations  to  perform  the
+minimization.
 
 
 \begin{table}
@@ -703,7 +719,8 @@ solver and the same preconditionner is used.
 \begin{tabular}{|c|c|r|r|r|r|} 
 \hline
 
- \multirow{2}{*}{Matrix name}  & Solver /   & \multicolumn{2}{c|}{gmres variant} & \multicolumn{2}{c|}{2 stage} \\
+ \multirow{2}{*}{Matrix name}  & Solver /   & \multicolumn{2}{c|}{gmres variant} & \multicolumn{2}{c|}{2 stage} \\ 
+\cline{3-6}
        &  precond             & Time  & \# Iter.  & Time  & \# Iter.  \\\hline \hline
 
 crashbasis         & gmres / none             &  15.65     & 518  &  14.12 & 450  \\
@@ -711,7 +728,7 @@ parabolic\_fem     & gmres / ilu           & 1009.94   & 7573 & 401.52 & 2970 \\
 epb3               & fgmres / sor             &  8.67     & 600  &  8.21 & 540  \\
 atmosmodj          &  fgmres / sor & 104.23  & 451 & 88.97 & 366  \\
 bfwa398            & gmres / none  & 1.42 & 9612 & 0.28 & 1650 \\
-torso3             & fgmres/sor  & 565  & 37.70 & 34.97 & 510 \\
+torso3             & fgmres/sor  & 37.70 & 565 & 34.97 & 510 \\
 \hline
 
 \end{tabular}
@@ -721,13 +738,6 @@ torso3             & fgmres/sor  & 565  & 37.70 & 34.97 & 510 \\
 \end{table}
 
 
-Param : retart 30 iters
-cols = 8
-iter cgls = 20
-cgls prec = 1e-40
-prec = 1e-10
-Intel(R) Core(TM) i7-3630QM CPU @ 2.40GHz
-
 
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