$\lambda_h$ 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
\delta_x \sum_{h \in V, l \in L } x_{hl}^2
+ \delta_r\sum_{h \in V }R_{h}^2
+ \delta_p\sum_{h \in V }P_{sh}^{\frac{8}{3}}.
-\label{eq:obj2}
+\label{eq:obj2p}
\end{equation}
-In this equation we have first introduced new regularisation factors
+In this equation we have first introduced new regularization factors
(namely $\delta_x$, $\delta_r$, and $\delta_p$)
instead of the sole $\delta$.
This allows to further separately study the influence of each factor.
which is strictly convex, for any value of $\lambda_h$ since the discriminant
is positive.
-
\ No newline at end of file
+This proposed enhancement has been evaluated as follows:
+10 thresholds $t$, such that $1E-5 \le t \le 1E-3$, have
+been selected and for each of them,
+10 random configurations have been generated.
+For each one, we store the
+number of iterations which is sufficient to make the dual
+function variation smaller than this given threshold with
+the two approaches: either the original one ore the
+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 speeds up the algorithm and guarantees the convexity,
+and thus the convergence.
+\begin{figure*}
+\begin{center}
+\includegraphics[scale=0.5]{convex.png}
+\end{center}
+\caption{Original Vs Convex Guarantee Approaches}\label{Fig:convex}
+\end{figure*}
+
+
