12-10-2014 04

diff --git a/biblio.bib b/biblio.bib
index da3ffc3345b6769db11fe8afcba701b5ee8505be..3e9367b5510837fd22d17d9ac53193ba72c58fe4 100644 (file)
--- a/biblio.bib
+++ b/biblio.bib
@@ -125,4 +125,25 @@ year = {2008},
                 \path|http://www.math.utah.edu/~beebe/|",
   fjournal =   "SIAM Journal on Numerical Analysis",
   journal-url =  "http://epubs.siam.org/sinum",
                 \path|http://www.math.utah.edu/~beebe/|",
   fjournal =   "SIAM Journal on Numerical Analysis",
   journal-url =  "http://epubs.siam.org/sinum",

-}
\ No newline at end of file
+}
+
+@article{Meijerink77,
+ author = {Meijerink, J.A. and Vorst, H.A.van der},
+ title = {An Iterative Solution Method for Linear Systems of Which the Coefficient Matrix is a Symmetric {M}-Matrix},
+ journal = {Mathematics of Computation},
+ year = {1977},
+ volume = {31},
+ number = {137},
+ pages = {148--162},
+ publisher = {American Mathematical Society},
+ }
+
+@techreport{Huang89,
+    author    = {Huang, Y. and Vorst, H.A. van der},
+    title     = {Some Observations on the Convergence Behavior of {GMRES}},
+    institution = {Delft Univ. Technology},
+    type     = { Report 89--09}, 
+    year      = {1989},
+}
+
+

diff --git a/paper.tex b/paper.tex
index 32d18a3039fde3ef26e2922f5e16f6a801d372f5..51781ba01ad4fd0363f031d16e054de8d9a270f3 100644 (file)
--- a/paper.tex
+++ b/paper.tex
@@ -601,10 +601,9 @@ is summarized while intended perspectives are provided.
 %%%*********************************************************
 \section{Related works}
 \label{sec:02} 
 %%%*********************************************************
 \section{Related works}
 \label{sec:02} 

-Krylov subspace iteration methods have increasingly become useful and successful techniques for solving linear and nonlinear systems and eigenvalue problems, especially since the increase development of the preconditioners~\cite{}. One reason of the popularity of these methods is their generality, simplicity and efficiency to solve systems of equations arising from very large and complex problems. %A Krylov method is based on a projection process onto a Krylov subspace spanned by vectors and it forms a sequence of approximations by minimizing the residual over the subspace formed~\cite{}.
+Krylov subspace iteration methods have increasingly become useful and successful techniques for solving linear and nonlinear systems and eigenvalue problems, especially since the increase development of the preconditioners~\cite{Saad2003,Meijerink77}. One reason of the popularity of these methods is their generality, simplicity and efficiency to solve systems of equations arising from very large and complex problems. %A Krylov method is based on a projection process onto a Krylov subspace spanned by vectors and it forms a sequence of approximations by minimizing the residual over the subspace formed~\cite{}.

 
 

-GMRES is one of the most widely used Krylov iterative method for solving sparse and large linear systems. It is developed by Saad and al.~\cite{} as a generalized method to deal with unsymmetric and non-Hermitian problems, and indefinite symmetric problems too. In its original version called full GMRES, it minimizes the residual
-over the current Krylov subspace until convergence in at most $n$ iterations, where $n$ is the size of the sparse matrix. It should be noted that full GMRES is too expensive in the case of large matrices since the required orthogonalization process per iteration grows quadratically with the number of iterations. For that reason, in practice GMRES is restarted after each $m\ll n$ iterations to avoid the storage of a large orthonormal basis. However, the convergence behavior of the restarted GMRES in many cases depends quite critically on the value of $m$~\cite{}. Therefore in most cases, a preconditioning technique is applied to the restarted GMRES method in order to improve its convergence.
+GMRES is one of the most widely used Krylov iterative method for solving sparse and large linear systems. It is developed by Saad and al.~\cite{Saad86} as a generalized method to deal with unsymmetric and non-Hermitian problems, and indefinite symmetric problems too. In its original version called full GMRES, it minimizes the residual over the current Krylov subspace until convergence in at most $n$ iterations, where $n$ is the size of the sparse matrix. It should be noted that full GMRES is too expensive in the case of large matrices since the required orthogonalization process per iteration grows quadratically with the number of iterations. For that reason, in practice GMRES is restarted after each $m\ll n$ iterations to avoid the storage of a large orthonormal basis. However, the convergence behavior of the restarted GMRES in many cases depends quite critically on the value of $m$~\cite{Huang89}. Therefore in most cases, a preconditioning technique is applied to the restarted GMRES method in order to improve its convergence.

 
 %FGMRES , GMRESR, two-stage, communication avoiding 
 
 
 %FGMRES , GMRESR, two-stage, communication avoiding