X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/57e564506a8117605eca5b5d80c0f492e6121c12..HEAD:/BookGPU/Chapters/chapter13/ch13.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter13/ch13.tex b/BookGPU/Chapters/chapter13/ch13.tex
index ce2c7c2..db2ab78 100755
--- a/BookGPU/Chapters/chapter13/ch13.tex
+++ b/BookGPU/Chapters/chapter13/ch13.tex
@@ -134,7 +134,7 @@ where $b=\{b_{1},b_{2},b_{3}\}$, $\|b\|_{2}$ denotes the Euclidean norm of $b$,
 $v=e^{-a}.u$ represents the general change of variables such that $a=\frac{b^{t}(x,y,z)}{2\eta}$.
 Consequently, the numerical resolution of the diffusion problem (the self-adjoint
 operator~(\ref{ch13:eq:04})) is done by optimization algorithms, in contrast to that
-of the convection-diffusion problem (non self-adjoint operator~(\ref{ch13:eq:03}))
+of the convection-diffusion problem (nonself-adjoint operator~(\ref{ch13:eq:03}))
 which is done by relaxation algorithms. In the case of our studied algorithm, the convergence\index{convergence}
 is ensured by M-matrix property; then, the performance is linked to the magnitude of
 the spectral radius of the iteration matrix, which is independent of the condition
@@ -747,7 +747,7 @@ consequently it also depends on the number of computing nodes.
 %%--------------------------%%
 \section{Red-black ordering technique}
 \label{ch13:sec:06}
-As is well-known, the Jacobi method\index{iterative method!Jacobi} is characterized
+As is well known, the Jacobi method\index{iterative method!Jacobi} is characterized
 by a slow convergence\index{convergence} rate compared to some iterative methods\index{iterative method}
 (for example, Gauss-Seidel method\index{iterative method!Gauss-Seidel}). So, in this
 section, we present some solutions to reduce the execution time and the number of