X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/desynchronisation-controle.git/blobdiff_plain/e02082ad2d87032e8cff4fa8cc48682c85efc624..refs/heads/master:/IWCMC14/convexity.tex?ds=inline

diff --git a/IWCMC14/convexity.tex b/IWCMC14/convexity.tex
index de0ddbc..d88481c 100644
--- a/IWCMC14/convexity.tex
+++ b/IWCMC14/convexity.tex
@@ -12,6 +12,10 @@ The function inside the $\arg \min$ is strictly convex if and only if
 $\lambda_h$ is not null. This asymptotic configuration may arise due to 
 the definition of $\lambda_h$. Worth, in this case,  the function is 
 strictly decreasing and the minimal value is obtained when $p$ is the infinity.
+Thus, the method  follows its iterative calculus
+with an arbitrarely large value for $P_{sh}^{(k)}$. This leads to 
+a convergence which is dramatically slow down.
+
 
 To prevent this configuration, we replace the objective function given 
 in equation~(\ref{eq:obj2}) by 
@@ -59,9 +63,8 @@ one which is convex guarantee.
 The Figure~\ref{Fig:convex} summarizes the average number of convergence 
 iterations for each treshold value. As we can see, even if this new 
 enhanced method introduces new calculus, 
-it only slows few down the algorithm and guarantee the convexity, 
+it speeds up  the algorithm and guarantees the convexity, 
 and thus the convergence.
-Notice that the encoding power has been arbitrarily limited to 10 W.
 \begin{figure*}
 \begin{center}
 \includegraphics[scale=0.5]{convex.png}