v2-20-08-2014

diff --git a/paper.tex b/paper.tex
index 3b10cc66faa84fafc8d3642729d12b8b51b8b658..8bef816d7236b576fe1c43b789d93d3bdaddf081 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -542,8 +542,7 @@ Iterative methods are become more attractive than direct ones to solve large spa
 
 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).
 
 
 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
+However, the iterative methods suffer from scalability problems on parallel computing platforms with many processors due to their need for reduction operations and collective communications to perform matrix-vector multiplications. 

 
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