quelques typos

diff --git a/IWCMC14/argmin.tex b/IWCMC14/argmin.tex
index 567785671da45be949653dce77b7549fd0dae131..9a20c42befc2e195e492a99a7ff765ac30922f5c 100644 (file)
--- a/IWCMC14/argmin.tex
+++ b/IWCMC14/argmin.tex
@@ -89,14 +89,14 @@ $$
 \end{table*}
 
 
 \end{table*}
 
 

-This improvment has been evaluated on a set of experiments.
-For 10 tresholds $t$, such that $1E-5 \le t \le 1E-3$, we have 
-executed 10 times the aproach detailled before either with the new 
+This improvement has been evaluated on a set of experiments.
+For 10 thresholds $t$, such that $1E-5 \le t \le 1E-3$, we have 
+executed 10 times the approach detailed before either with the new 

 gradient calculus or with the original argmin one. 
 The Table~\ref{Table:argmin:time} summarizes the averages of these 
 gradient calculus or with the original argmin one. 
 The Table~\ref{Table:argmin:time} summarizes the averages of these 

-excution times, given in seconds. We remark time spent with the gradient 
+execution times, given in seconds. We remark time spent with the gradient 

 approach is about 37 times smaller than the one of the argmin one.
 approach is about 37 times smaller than the one of the argmin one.

-Among implementations of argmin aproaches, we have retained 
+Among implementations of argmin approaches, we have retained 

 the COBYLA one since it does not require any gradient to be executed.
 
 \begin{table*}
 the COBYLA one since it does not require any gradient to be executed.
 
 \begin{table*}


@@ -104,7 +104,7 @@ the COBYLA one since it does not require any gradient to be executed.
 $$
 \begin{array}{|l|l|l|l|l|l|l|l|l|l|l|}
 \hline
 $$
 \begin{array}{|l|l|l|l|l|l|l|l|l|l|l|}
 \hline

-\textrm{Convergence Treshold} &
+\textrm{Convergence Threshold} &

 10^{-5} &
 1.67.10^{-5} &
 2.78.10^{-5} &
 10^{-5} &
 1.67.10^{-5} &
 2.78.10^{-5} &

diff --git a/IWCMC14/convexity.tex b/IWCMC14/convexity.tex
index ff4656dcfcad14eca82c7a5283e3ac7f909478b4..de0ddbc0b7936d9d86f990d0045e4ddc3a077861 100644 (file)
--- a/IWCMC14/convexity.tex
+++ b/IWCMC14/convexity.tex
@@ -22,7 +22,7 @@ in equation~(\ref{eq:obj2}) by
 + \delta_p\sum_{h \in V }P_{sh}^{\frac{8}{3}}.
 \label{eq:obj2p}
 \end{equation}
 + \delta_p\sum_{h \in V }P_{sh}^{\frac{8}{3}}.
 \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.
 (namely $\delta_x$, $\delta_r$, and $\delta_p$)
 instead of the sole $\delta$.  
 This allows to  further separately study the influence of each factor.


@@ -46,27 +46,27 @@ Provided $p^{5/3}$ is replaced by $P$, we have a quadratic function
 which is strictly convex, for any value of $\lambda_h$ since the discriminant 
 is positive. 
 
 which is strictly convex, for any value of $\lambda_h$ since the discriminant 
 is positive. 
 

-This proposed enhacement has been evaluated as follows:  
-10 tresholds $t$, such that $1E-5 \le t \le 1E-3$, have 
+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 
 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 treshold with 
+function variation smaller than this given threshold with 

 the two approaches: either the original one ore the
 the two approaches: either the original one ore the

-one which is convex garantee.
+one which is convex guarantee.

 
 The Figure~\ref{Fig:convex} summarizes the average number of convergence 
 
 The Figure~\ref{Fig:convex} summarizes the average number of convergence 

-iterations for each tresholdvalue. As we can see, even if this new 
+iterations for each treshold value. As we can see, even if this new 

 enhanced method introduces new calculus, 
 enhanced method introduces new calculus, 

-it only slows few down the algorithm and garantee the convexity, 
+it only slows few down the algorithm and guarantee the convexity, 

 and thus the convergence.
 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}
 \end{center}
 \begin{figure*}
 \begin{center}
 \includegraphics[scale=0.5]{convex.png}
 \end{center}

-\caption{Original Vs Convex Garantee Approaches}\label{Fig:convex}
+\caption{Original Vs Convex Guarantee Approaches}\label{Fig:convex}

 \end{figure*} 
 
 
 \end{figure*} 
 
 

diff --git a/exp_controle_asynchrone/simulMWSN.py b/exp_controle_asynchrone/simulMWSN.py
index 45326b6a30dc9d263f5095e048dc7ce32a4c3ed9..8e286a33e631f1e1832c862cae9079f5091ffc00 100644 (file)
--- a/exp_controle_asynchrone/simulMWSN.py
+++ b/exp_controle_asynchrone/simulMWSN.py
@@ -377,7 +377,7 @@ def maj(k,maj_theta,mxg,idxexp,comppsh=False):
                     t= float(2*v[h]*mt.log(float(sigma2)/D))/(3*gamma*la[h])
                     rep = mt.pow(t,float(3)/5)                
                 else :
                     t= float(2*v[h]*mt.log(float(sigma2)/D))/(3*gamma*la[h])
                     rep = mt.pow(t,float(3)/5)                
                 else :

-                    rep = 10
+                    rep = 1000

             else :                
                 t= float(-3*la[h]+mt.sqrt(9*(la[h]**2)+64*delta*v[h]*mt.log(float(sigma2)/D)/gamma))/(16*delta)
                 rep = mt.pow(t,float(3)/5)
             else :                
                 t= float(-3*la[h]+mt.sqrt(9*(la[h]**2)+64*delta*v[h]*mt.log(float(sigma2)/D)/gamma))/(16*delta)
                 rep = mt.pow(t,float(3)/5)