From 6857ecf2cdc87f1eec0618e8f148dae094224933 Mon Sep 17 00:00:00 2001
From: raphael couturier <couturie@extinction>
Date: Thu, 9 Oct 2014 15:45:23 +0200
Subject: [PATCH 1/1] no more parallelization part

---
 paper.tex | 30 ++++++++++++++----------------
 1 file changed, 14 insertions(+), 16 deletions(-)

diff --git a/paper.tex b/paper.tex
index 8ffd387..8bb4dc3 100644
--- a/paper.tex
+++ b/paper.tex
@@ -583,8 +583,7 @@ performances.
 The present paper is organized  as follows. In Section~\ref{sec:02} some related
 works are presented. Section~\ref{sec:03} presents our two-stage algorithm using
 a  least-square  residual  minimization.   Section~\ref{sec:04}  describes  some
-convergence  results  on this  method.   In Section~\ref{sec:05},  parallization
-details  of  TSARM  are  given.  Section~\ref{sec:06}  shows  some  experimental
+convergence  results  on this  method.   Section~\ref{sec:05}  shows  some  experimental
 results  obtained on large  clusters of  our algorithm  using routines  of PETSc
 toolkit.  Finally Section~\ref{sec:06} concludes and gives some perspectives.
 %%%*********************************************************
@@ -680,18 +679,6 @@ To summarize, the important parameters of TSARM are:
 \item $\epsilon_{ls}$ the threshold to stop the least-square method
 \end{itemize}
 
-%%%*********************************************************
-%%%*********************************************************
-
-\section{Convergence results}
-\label{sec:04}
-
-
-
-%%%*********************************************************
-%%%*********************************************************
-\section{Parallelization}
-\label{sec:05}
 
 The  parallelisation  of  TSARM  relies   on  the  parallelization  of  all  its
 parts. More  precisely, except  the least-square step,  all the other  parts are
@@ -733,10 +720,21 @@ In each iteration  of CGLS, there is two  matrix-vector multiplications and some
 classical operations:  dots, norm, multiplication  and addition on  vectors. All
 these operations are easy to implement in PETSc or similar environment.
 
+
+
+%%%*********************************************************
+%%%*********************************************************
+
+\section{Convergence results}
+\label{sec:04}
+
+
+
+
 %%%*********************************************************
 %%%*********************************************************
 \section{Experiments using petsc}
-\label{sec:06}
+\label{sec:05}
 
 
 In order to see the influence of our algorithm with only one processor, we first
@@ -913,7 +911,7 @@ but they are not scalable with many cores.
 %%%*********************************************************
 %%%*********************************************************
 \section{Conclusion}
-\label{sec:07}
+\label{sec:06}
 %The conclusion goes here. this is more of the conclusion
 %%%*********************************************************
 %%%*********************************************************
-- 
2.39.5