X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/23d19900771651bd580496ebc8ca0fc156aedc88..1a82aaffa07c2cd0cd044d1454d233171075e6f2:/krylov_multi.tex

diff --git a/krylov_multi.tex b/krylov_multi.tex
index 82cf45e..3295c82 100644
--- a/krylov_multi.tex
+++ b/krylov_multi.tex
@@ -48,7 +48,52 @@ thousands of cores are used.
 
 
 A completer...
-On ne peut pas parler de tout...
+On ne peut pas parler de tout...\\
+
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%
+%% BEGIN
+%%%%%%%%%%%%%%%%%%%%%%%
+The key idea of the multisplitting method for solving a large system of linear equations
+$Ax=b$ consists in partitioning the matrix $A$ in $L$ several ways 
+\begin{equation}
+A = M_l - N_l,~l\in\{1,\ldots,L\},
+\label{eq01}
+\end{equation}
+where $M_l$ are nonsingular matrices. Then the linear system is solved by iteration based
+on the multisplittings as follows  
+\begin{equation}
+x^{k+1}=\displaystyle\sum^L_{l=1} E_l M^{-1}_l (N_l x^k + b),~k=1,2,3,\ldots
+\label{eq02}
+\end{equation}
+where $E_l$ are non-negative and diagonal weighting matrices such that $\sum^L_{l=1}E_l=I$ ($I$ is an identity matrix).
+Thus the convergence of such a method is dependent on the condition
+\begin{equation}
+\rho(\displaystyle\sum^L_{l=1}E_l M^{-1}_l N_l)<1.
+\label{eq03}
+\end{equation}
+
+The advantage of the multisplitting method is that at each iteration $k$ there are $L$ different linear
+systems
+\begin{equation}
+y_l^k=M^{-1}_l N_l x_l^{k-1} + M^{-1}_l b,~l\in\{1,\ldots,L\},
+\label{eq04}
+\end{equation}
+to be solved independently by a direct or an iterative method, where $y_l^k$ is the solution of the local system.
+A multisplitting method using an iterative method for solving the $L$ linear systems is called an inner-outer
+iterative method or a two-stage method. The solution of the global linear system at the iteration $k$ is computed
+as follows
+\begin{equation}
+x^k = \displaystyle\sum^L_{l=1} E_l y_l^k,
+\label{eq05}
+\end{equation}    
+In the case where the diagonal weighting matrices $E_l$ have only zero and one factors (i.e. $y_l^k$ are disjoint vectors),
+the multisplitting method is non-overlapping and corresponds to the block Jacobi method.  
+%%%%%%%%%%%%%%%%%%%%%%%
+%% END
+%%%%%%%%%%%%%%%%%%%%%%%
 
 \section{Related works}
 
@@ -102,7 +147,7 @@ b & = & [B_{1}, \ldots, B_{L}]
 where for all $l\in\{1,\ldots,L\}$ $A_l$ is a rectangular block of size $n_l\times n$
 and $X_l$ and $B_l$ are sub-vectors of size $n_l$, such that $\sum_ln_l=n$. In this
 case, we use a row-by-row splitting without overlapping in such a way that successive
-rows of the sparse matrix $A$ and both vectors $x$ and $b$ are assigned to a cluster.
+rows of the sparse matrix $A$ and both vectors $x$ and $b$ are assigned to one cluster.
 So, the multisplitting format of the linear system is defined as follows:
 \begin{equation}
 \forall l\in\{1,\ldots,L\} \mbox{,~} \displaystyle\sum_{i=1}^{l-1}A_{li}X_i + A_{ll}X_l + \displaystyle\sum_{i=l+1}^{L}A_{li}X_i = B_l,