X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/aa0d01321f4e20c96c3e3e2e8aa8ffbafd2d6b2b..e1229871aeda91b7ad57dc2139fdb458ba0e92f1:/src/surf/lagrange.cpp

diff --git a/src/surf/lagrange.cpp b/src/surf/lagrange.cpp
index 1efd8ac7cb..90a51f741c 100644
--- a/src/surf/lagrange.cpp
+++ b/src/surf/lagrange.cpp
@@ -5,8 +5,7 @@
  * under the terms of the license (GNU LGPL) which comes with this package. */
 
 /*
- * Modelling the proportional fairness using the Lagrange Optimization 
- * Approach. For a detailed description see:
+ * Modeling the proportional fairness using the Lagrangian Optimization Approach. For a detailed description see:
  * "ssh://username@scm.gforge.inria.fr/svn/memo/people/pvelho/lagrange/ppf.ps".
  */
 #include "xbt/log.h"
@@ -18,10 +17,8 @@
 #include <math.h>
 #endif
 
-XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf,
-                                "Logging specific to SURF (lagrange)");
-XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf_lagrange,
-                        "Logging specific to SURF (lagrange dichotomy)");
+XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, "Logging specific to SURF (lagrange)");
+XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf_lagrange, "Logging specific to SURF (lagrange dichotomy)");
 
 #define SHOW_EXPR(expr) XBT_CDEBUG(surf_lagrange,#expr " = %g",expr);
 
@@ -30,18 +27,16 @@ double (*func_fp_def) (lmm_variable_t, double);
 double (*func_fpi_def) (lmm_variable_t, double);
 
 /*
- * Local prototypes to implement the lagrangian optimization with optimal step, also called dichotomy.
+ * Local prototypes to implement the Lagrangian optimization with optimal step, also called dichotomy.
  */
-//solves the proportional fairness using a lagrange optimizition with dichotomy step
+//solves the proportional fairness using a Lagrangian optimization with dichotomy step
 void lagrange_solve(lmm_system_t sys);
 //computes the value of the dichotomy using a initial values, init, with a specific variable or constraint
-static double dichotomy(double init, double diff(double, void *),
-                        void *var_cnst, double min_error);
+static double dichotomy(double init, double diff(double, void *), void *var_cnst, double min_error);
 //computes the value of the differential of constraint param_cnst applied to lambda  
 static double partial_diff_lambda(double lambda, void *param_cnst);
 
-static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list,
-                            int warn)
+static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn)
 {
   void *_cnst, *_elem, *_var;
   xbt_swag_t elem_list = nullptr;
@@ -52,11 +47,11 @@ static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list,
   double tmp;
 
   xbt_swag_foreach(_cnst, cnst_list) {
-  cnst = (lmm_constraint_t)_cnst;
+  cnst = static_cast<lmm_constraint_t>(_cnst);
     tmp = 0;
     elem_list = &(cnst->enabled_element_set);
     xbt_swag_foreach(_elem, elem_list) {
-      elem = (lmm_element_t)_elem;
+      elem = static_cast<lmm_element_t>(_elem);
       var = elem->variable;
       xbt_assert(var->weight > 0);
       tmp += var->value;
@@ -64,30 +59,24 @@ static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list,
 
     if (double_positive(tmp - cnst->bound, sg_maxmin_precision)) {
       if (warn)
-        XBT_WARN
-            ("The link (%p) is over-used. Expected less than %f and got %f",
-             cnst, cnst->bound, tmp);
+        XBT_WARN ("The link (%p) is over-used. Expected less than %f and got %f", cnst, cnst->bound, tmp);
       return 0;
     }
-    XBT_DEBUG
-        ("Checking feasability for constraint (%p): sat = %f, lambda = %f ",
-         cnst, tmp - cnst->bound, cnst->lambda);
+    XBT_DEBUG ("Checking feasability for constraint (%p): sat = %f, lambda = %f ", cnst, tmp - cnst->bound,
+               cnst->lambda);
   }
 
   xbt_swag_foreach(_var, var_list) {
-  var = (lmm_variable_t)_var;
+    var = static_cast<lmm_variable_t>(_var);
     if (!var->weight)
       break;
     if (var->bound < 0)
       continue;
-    XBT_DEBUG("Checking feasability for variable (%p): sat = %f mu = %f", var,
-           var->value - var->bound, var->mu);
+    XBT_DEBUG("Checking feasability for variable (%p): sat = %f mu = %f", var, var->value - var->bound, var->mu);
 
     if (double_positive(var->value - var->bound, sg_maxmin_precision)) {
       if (warn)
-        XBT_WARN
-            ("The variable (%p) is too large. Expected less than %f and got %f",
-             var, var->bound, var->value);
+        XBT_WARN ("The variable (%p) is too large. Expected less than %f and got %f", var, var->bound, var->value);
       return 0;
     }
   }
@@ -97,15 +86,13 @@ static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list,
 static double new_value(lmm_variable_t var)
 {
   double tmp = 0;
-  int i;
 
-  for (i = 0; i < var->cnsts_number; i++) {
+  for (int i = 0; i < var->cnsts_number; i++) {
     tmp += (var->cnsts[i].constraint)->lambda;
   }
   if (var->bound > 0)
     tmp += var->mu;
-  XBT_DEBUG("\t Working on var (%p). cost = %e; Weight = %e", var, tmp,
-         var->weight);
+  XBT_DEBUG("\t Working on var (%p). cost = %e; Weight = %e", var, tmp, var->weight);
   //uses the partial differential inverse function
   return var->func_fpi(var, tmp);
 }
@@ -114,9 +101,8 @@ static double new_mu(lmm_variable_t var)
 {
   double mu_i = 0.0;
   double sigma_i = 0.0;
-  int j;
 
-  for (j = 0; j < var->cnsts_number; j++) {
+  for (int j = 0; j < var->cnsts_number; j++) {
     sigma_i += (var->cnsts[j].constraint)->lambda;
   }
   mu_i = var->func_fp(var, var->bound) - sigma_i;
@@ -127,21 +113,21 @@ static double new_mu(lmm_variable_t var)
 
 static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list)
 {
-  void *_cnst, *_var;
+  void *_cnst;
+  void *_var;
   lmm_constraint_t cnst = nullptr;
   lmm_variable_t var = nullptr;
 
   double obj = 0.0;
 
   xbt_swag_foreach(_var, var_list) {
-  var = (lmm_variable_t)_var;
+    var = static_cast<lmm_variable_t>(_var);
     double sigma_i = 0.0;
-    int j;
 
     if (!var->weight)
       break;
 
-    for (j = 0; j < var->cnsts_number; j++)
+    for (int j = 0; j < var->cnsts_number; j++)
       sigma_i += (var->cnsts[j].constraint)->lambda;
 
     if (var->bound > 0)
@@ -149,16 +135,15 @@ static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list)
 
     XBT_DEBUG("var %p : sigma_i = %1.20f", var, sigma_i);
 
-    obj += var->func_f(var, var->func_fpi(var, sigma_i)) -
-        sigma_i * var->func_fpi(var, sigma_i);
+    obj += var->func_f(var, var->func_fpi(var, sigma_i)) - sigma_i * var->func_fpi(var, sigma_i);
 
     if (var->bound > 0)
       obj += var->mu * var->bound;
   }
 
   xbt_swag_foreach(_cnst, cnst_list) {
-      cnst = (lmm_constraint_t)_cnst;
-      obj += cnst->lambda * cnst->bound;
+    cnst = static_cast<lmm_constraint_t>(_cnst);
+    obj += cnst->lambda * cnst->bound;
   }
 
   return obj;
@@ -166,18 +151,13 @@ static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list)
 
 void lagrange_solve(lmm_system_t sys)
 {
-  /*
-   * Lagrange Variables.
-   */
+  /* Lagrange Variables. */
   int max_iterations = 100;
   double epsilon_min_error = 0.00001; /* this is the precision on the objective function so it's none of the configurable values and this value is the legacy one */
   double dichotomy_min_error = 1e-14;
   double overall_modification = 1;
 
-  /*
-   * Variables to manipulate the data structure proposed to model the maxmin
-   * fairness. See docummentation for more details.
-   */
+  /* Variables to manipulate the data structure proposed to model the maxmin fairness. See documentation for details. */
   xbt_swag_t cnst_list = nullptr;
   void *_cnst;
   lmm_constraint_t cnst = nullptr;
@@ -186,20 +166,17 @@ void lagrange_solve(lmm_system_t sys)
   void *_var;
   lmm_variable_t var = nullptr;
 
-  /*
-   * Auxiliary variables.
-   */
+  /* Auxiliary variables. */
   int iteration = 0;
   double tmp = 0;
   int i;
-  double obj, new_obj;
+  double obj;
+  double new_obj;
 
   XBT_DEBUG("Iterative method configuration snapshot =====>");
-  XBT_DEBUG("#### Maximum number of iterations       : %d", max_iterations);
-  XBT_DEBUG("#### Minimum error tolerated            : %e",
-         epsilon_min_error);
-  XBT_DEBUG("#### Minimum error tolerated (dichotomy) : %e",
-         dichotomy_min_error);
+  XBT_DEBUG("#### Maximum number of iterations        : %d", max_iterations);
+  XBT_DEBUG("#### Minimum error tolerated             : %e", epsilon_min_error);
+  XBT_DEBUG("#### Minimum error tolerated (dichotomy) : %e", dichotomy_min_error);
 
   if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
     lmm_print(sys);
@@ -208,10 +185,7 @@ void lagrange_solve(lmm_system_t sys)
   if (!(sys->modified))
     return;
 
-
-  /* 
-   * Initialize lambda.
-   */
+  /* Initialize lambda. */
   cnst_list = &(sys->active_constraint_set);
   xbt_swag_foreach(_cnst, cnst_list) {
   cnst = (lmm_constraint_t)_cnst;
@@ -227,7 +201,7 @@ void lagrange_solve(lmm_system_t sys)
   var_list = &(sys->variable_set);
   i = 0;
   xbt_swag_foreach(_var, var_list) {
-  var = (lmm_variable_t)_var;
+  var = static_cast<lmm_variable_t>(_var);
     if (!var->weight)
       var->value = 0.0;
     else {
@@ -246,7 +220,7 @@ void lagrange_solve(lmm_system_t sys)
       XBT_DEBUG("#### var(%p) ->weight: %e", var, var->weight);
       XBT_DEBUG("#### var(%p) ->bound: %e", var, var->bound);
       for (i = 0; i < var->cnsts_number; i++) {
-        if (var->cnsts[i].value == 0.0)
+        if (var->cnsts[i].value ==   0.0)
           nb++;
       }
       if (nb == var->cnsts_number)
@@ -254,27 +228,18 @@ void lagrange_solve(lmm_system_t sys)
     }
   }
 
-  /* 
-   * Compute dual objective.
-   */
+  /*  Compute dual objective. */
   obj = dual_objective(var_list, cnst_list);
 
-  /*
-   * While doesn't reach a minimun error or a number maximum of iterations.
-   */
-  while (overall_modification > epsilon_min_error
-         && iteration < max_iterations) {
-/*     int dual_updated=0; */
-
+  /* While doesn't reach a minimum error or a number maximum of iterations. */
+  while (overall_modification > epsilon_min_error && iteration < max_iterations) {
     iteration++;
     XBT_DEBUG("************** ITERATION %d **************", iteration);
     XBT_DEBUG("-------------- Gradient Descent ----------");
 
-    /*                       
-     * Improve the value of mu_i
-     */
+    /* Improve the value of mu_i */
     xbt_swag_foreach(_var, var_list) {
-      var = (lmm_variable_t)_var;
+      var = static_cast<lmm_variable_t>(_var);
       if (!var->weight)
         break;
       if (var->bound >= 0) {
@@ -282,69 +247,53 @@ void lagrange_solve(lmm_system_t sys)
         var->new_mu = new_mu(var);
 /*   dual_updated += (fabs(var->new_mu-var->mu)>dichotomy_min_error); */
 /*   XBT_DEBUG("dual_updated (%d) : %1.20f",dual_updated,fabs(var->new_mu-var->mu)); */
-        XBT_DEBUG("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var,
-               var->mu, var->new_mu);
+        XBT_DEBUG("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, var->mu, var->new_mu);
         var->mu = var->new_mu;
 
         new_obj = dual_objective(var_list, cnst_list);
-        XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj,
-               obj - new_obj);
-        xbt_assert(obj - new_obj >= -epsilon_min_error,
-                    "Our gradient sucks! (%1.20f)", obj - new_obj);
+        XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, obj - new_obj);
+        xbt_assert(obj - new_obj >= -epsilon_min_error, "Our gradient sucks! (%1.20f)", obj - new_obj);
         obj = new_obj;
       }
     }
 
-    /*
-     * Improve the value of lambda_i
-     */
+    /* Improve the value of lambda_i */
     xbt_swag_foreach(_cnst, cnst_list) {
-      cnst = (lmm_constraint_t)_cnst;
+      cnst = static_cast<lmm_constraint_t>(_cnst);
       XBT_DEBUG("Working on cnst (%p)", cnst);
-      cnst->new_lambda =
-          dichotomy(cnst->lambda, partial_diff_lambda, cnst,
-                    dichotomy_min_error);
+      cnst->new_lambda = dichotomy(cnst->lambda, partial_diff_lambda, cnst, dichotomy_min_error);
 /*       dual_updated += (fabs(cnst->new_lambda-cnst->lambda)>dichotomy_min_error); */
 /*       XBT_DEBUG("dual_updated (%d) : %1.20f",dual_updated,fabs(cnst->new_lambda-cnst->lambda)); */
-      XBT_DEBUG("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f",
-             cnst, cnst->lambda, cnst->new_lambda);
+      XBT_DEBUG("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", cnst, cnst->lambda, cnst->new_lambda);
       cnst->lambda = cnst->new_lambda;
 
       new_obj = dual_objective(var_list, cnst_list);
-      XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj,
-             obj - new_obj);
-      xbt_assert(obj - new_obj >= -epsilon_min_error,
-                  "Our gradient sucks! (%1.20f)", obj - new_obj);
+      XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, obj - new_obj);
+      xbt_assert(obj - new_obj >= -epsilon_min_error, "Our gradient sucks! (%1.20f)", obj - new_obj);
       obj = new_obj;
     }
 
-    /*
-     * Now computes the values of each variable (\rho) based on
-     * the values of \lambda and \mu.
-     */
+    /* Now computes the values of each variable (\rho) based on the values of \lambda and \mu. */
     XBT_DEBUG("-------------- Check convergence ----------");
     overall_modification = 0;
     xbt_swag_foreach(_var, var_list) {
-      var = (lmm_variable_t)_var;
+      var = static_cast<lmm_variable_t>(_var);
       if (var->weight <= 0)
         var->value = 0.0;
       else {
         tmp = new_value(var);
 
-        overall_modification =
-            MAX(overall_modification, fabs(var->value - tmp));
+        overall_modification = MAX(overall_modification, fabs(var->value - tmp));
 
         var->value = tmp;
-        XBT_DEBUG("New value of var (%p)  = %e, overall_modification = %e",
-               var, var->value, overall_modification);
+        XBT_DEBUG("New value of var (%p)  = %e, overall_modification = %e", var, var->value, overall_modification);
       }
     }
 
     XBT_DEBUG("-------------- Check feasability ----------");
     if (!__check_feasible(cnst_list, var_list, 0))
       overall_modification = 1.0;
-    XBT_DEBUG("Iteration %d: overall_modification : %f", iteration,
-           overall_modification);
+    XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
 /*     if(!dual_updated) { */
 /*       XBT_WARN("Could not improve the convergence at iteration %d. Drop it!",iteration); */
 /*       break; */
@@ -357,11 +306,8 @@ void lagrange_solve(lmm_system_t sys)
     XBT_DEBUG("The method converges in %d iterations.", iteration);
   }
   if (iteration >= max_iterations) {
-    XBT_DEBUG
-        ("Method reach %d iterations, which is the maximum number of iterations allowed.",
-         iteration);
+    XBT_DEBUG ("Method reach %d iterations, which is the maximum number of iterations allowed.", iteration);
   }
-/*   XBT_INFO("Method converged after %d iterations", iteration); */
 
   if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
     lmm_print(sys);
@@ -369,48 +315,47 @@ void lagrange_solve(lmm_system_t sys)
 }
 
 /*
- * Returns a double value corresponding to the result of a dichotomy proccess with
- * respect to a given variable/constraint (\mu in the case of a variable or \lambda in
- * case of a constraint) and a initial value init. 
+ * Returns a double value corresponding to the result of a dichotomy process with respect to a given
+ * variable/constraint (\mu in the case of a variable or \lambda in case of a constraint) and a initial value init.
  *
  * @param init initial value for \mu or \lambda
  * @param diff a function that computes the differential of with respect a \mu or \lambda
  * @param var_cnst a pointer to a variable or constraint 
- * @param min_erro a minimun error tolerated
+ * @param min_erro a minimum error tolerated
  *
- * @return a double correponding to the result of the dichotomyal process
+ * @return a double corresponding to the result of the dichotomy process
  */
-static double dichotomy(double init, double diff(double, void *),
-                        void *var_cnst, double min_error)
+static double dichotomy(double init, double diff(double, void *), void *var_cnst, double min_error)
 {
-  double min, max;
+  double min =init;
+  double max= init;
   double overall_error;
   double middle;
-  double min_diff, max_diff, middle_diff;
+  double middle_diff;
   double diff_0 = 0.0;
-  min = max = init;
 
   XBT_IN();
 
-  if (init == 0.0) {
-    min = max = 0.5;
+  if (fabs(init) < 1e-20) {
+    min = 0.5;
+    max = 0.5;
   }
 
   overall_error = 1;
 
-  if ((diff_0 = diff(1e-16, var_cnst)) >= 0) {
+  diff_0 = diff(1e-16, var_cnst);
+  if (diff_0 >= 0) {
     XBT_CDEBUG(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", diff_0);
     XBT_OUT();
     return 0.0;
   }
 
-  min_diff = diff(min, var_cnst);
-  max_diff = diff(max, var_cnst);
+  double min_diff = diff(min, var_cnst);
+  double max_diff = diff(max, var_cnst);
 
   while (overall_error > min_error) {
-    XBT_CDEBUG(surf_lagrange_dichotomy,
-            "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f",
-            min, max, min_diff, max_diff);
+    XBT_CDEBUG(surf_lagrange_dichotomy, "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f",
+               min, max, min_diff, max_diff);
 
     if (min_diff > 0 && max_diff > 0) {
       if (min == max) {
@@ -434,14 +379,12 @@ static double dichotomy(double init, double diff(double, void *),
       }
     } else if (min_diff < 0 && max_diff > 0) {
       middle = (max + min) / 2.0;
-      XBT_CDEBUG(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",
-              middle);
-
-      if ((min == middle) || (max == middle)) {
-        XBT_CWARN(surf_lagrange_dichotomy,
-               "Cannot improve the convergence! min=max=middle=%1.20f, diff = %1.20f."
-               " Reaching the 'double' limits. Maybe scaling your function would help ([%1.20f,%1.20f]).",
-               min, max - min, min_diff, max_diff);
+      XBT_CDEBUG(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f", middle);
+
+      if ((fabs(min - middle) < 1e-20) || (fabs(max - middle) < 1e-20)){
+        XBT_CWARN(surf_lagrange_dichotomy, "Cannot improve the convergence! min=max=middle=%1.20f, diff = %1.20f."
+                  " Reaching the 'double' limits. Maybe scaling your function would help ([%1.20f,%1.20f]).",
+                  min, max - min, min_diff, max_diff);
         break;
       }
       middle_diff = diff(middle, var_cnst);
@@ -462,17 +405,16 @@ static double dichotomy(double init, double diff(double, void *),
         overall_error = 0;
 /*   SHOW_EXPR(overall_error); */
       }
-    } else if (min_diff == 0) {
+    } else if (fabs(min_diff) < 1e-20) {
       max = min;
       overall_error = 0;
 /*       SHOW_EXPR(overall_error); */
-    } else if (max_diff == 0) {
+    } else if (fabs(max_diff) < 1e-20) {
       min = max;
       overall_error = 0;
 /*       SHOW_EXPR(overall_error); */
     } else if (min_diff > 0 && max_diff < 0) {
-      XBT_CWARN(surf_lagrange_dichotomy,
-             "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0");
+      XBT_CWARN(surf_lagrange_dichotomy, "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0");
       xbt_abort();
     } else {
       XBT_CWARN(surf_lagrange_dichotomy,
@@ -489,13 +431,12 @@ static double dichotomy(double init, double diff(double, void *),
 
 static double partial_diff_lambda(double lambda, void *param_cnst)
 {
-
   int j;
   void *_elem;
   xbt_swag_t elem_list = nullptr;
   lmm_element_t elem = nullptr;
   lmm_variable_t var = nullptr;
-  lmm_constraint_t cnst = (lmm_constraint_t) param_cnst;
+  lmm_constraint_t cnst = static_cast<lmm_constraint_t>(param_cnst);
   double diff = 0.0;
   double sigma_i = 0.0;
 
@@ -505,11 +446,10 @@ static double partial_diff_lambda(double lambda, void *param_cnst)
   XBT_CDEBUG(surf_lagrange_dichotomy, "Computing diff of cnst (%p)", cnst);
 
   xbt_swag_foreach(_elem, elem_list) {
-  elem = (lmm_element_t)_elem;
+  elem = static_cast<lmm_element_t>(_elem);
     var = elem->variable;
     xbt_assert(var->weight > 0);
-    XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)",
-            var);
+    XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)", var);
     // Initialize the summation variable
     sigma_i = 0.0;
 
@@ -528,12 +468,9 @@ static double partial_diff_lambda(double lambda, void *param_cnst)
     diff += -var->func_fpi(var, sigma_i);
   }
 
-
   diff += cnst->bound;
 
-  XBT_CDEBUG(surf_lagrange_dichotomy,
-          "d D/d lambda for cnst (%p) at %1.20f = %1.20f", cnst, lambda,
-          diff);
+  XBT_CDEBUG(surf_lagrange_dichotomy, "d D/d lambda for cnst (%p) at %1.20f = %1.20f", cnst, lambda, diff);
   XBT_OUT();
   return diff;
 }
@@ -542,33 +479,21 @@ static double partial_diff_lambda(double lambda, void *param_cnst)
  * 
  *  \param func_fpi  inverse of the partial differential of f (f prime inverse, (f')^{-1})
  * 
- *  Set default functions to the ones passed as parameters. This is a polimorfism in C pure, enjoy the roots of programming.
+ *  Set default functions to the ones passed as parameters. This is a polymorphism in C pure, enjoy the roots of
+ *  programming.
  *
  */
-void lmm_set_default_protocol_function(double (*func_f)
-
-
-
-
-
-
-                                        (lmm_variable_t var, double x),
-                                       double (*func_fp) (lmm_variable_t
-                                                          var, double x),
-                                       double (*func_fpi) (lmm_variable_t
-                                                           var, double x))
+void lmm_set_default_protocol_function(double (*func_f) (lmm_variable_t var, double x),
+                                       double (*func_fp) (lmm_variable_t var, double x),
+                                       double (*func_fpi) (lmm_variable_t var, double x))
 {
   func_f_def = func_f;
   func_fp_def = func_fp;
   func_fpi_def = func_fpi;
 }
 
-
 /**************** Vegas and Reno functions *************************/
-/*
- * NOTE for Reno: all functions consider the network
- * coeficient (alpha) equal to 1.
- */
+/* NOTE for Reno: all functions consider the network coefficient (alpha) equal to 1. */
 
 /*
  * For Vegas: $f(x) = \alpha D_f\ln(x)$
@@ -605,14 +530,12 @@ double func_reno_f(lmm_variable_t var, double x)
 {
   xbt_assert(var->weight > 0.0, "Don't call me with stupid values!");
 
-  return RENO_SCALING * sqrt(3.0 / 2.0) / var->weight *
-      atan(sqrt(3.0 / 2.0) * var->weight * x);
+  return RENO_SCALING * sqrt(3.0 / 2.0) / var->weight * atan(sqrt(3.0 / 2.0) * var->weight * x);
 }
 
 double func_reno_fp(lmm_variable_t var, double x)
 {
-  return RENO_SCALING * 3.0 / (3.0 * var->weight * var->weight * x * x +
-                               2.0);
+  return RENO_SCALING * 3.0 / (3.0 * var->weight * var->weight * x * x + 2.0);
 }
 
 double func_reno_fpi(lmm_variable_t var, double x)
@@ -622,16 +545,13 @@ double func_reno_fpi(lmm_variable_t var, double x)
   xbt_assert(var->weight > 0.0, "Don't call me with stupid values!");
   xbt_assert(x > 0.0, "Don't call me with stupid values!");
 
-  res_fpi =
-      1.0 / (var->weight * var->weight * (x / RENO_SCALING)) -
-      2.0 / (3.0 * var->weight * var->weight);
+  res_fpi = 1.0 / (var->weight * var->weight * (x / RENO_SCALING)) - 2.0 / (3.0 * var->weight * var->weight);
   if (res_fpi <= 0.0)
     return 0.0;
 /*   xbt_assert(res_fpi>0.0,"Don't call me with stupid values!"); */
   return sqrt(res_fpi);
 }
 
-
 /* Implementing new Reno-2
  * For Reno-2:  $f(x)   = U_f(x_f) = \frac{{2}{D_f}}*ln(2+x*D_f)$
  * Therefore:   $fp(x)  = 2/(Weight*x + 2)
@@ -641,25 +561,19 @@ double func_reno_fpi(lmm_variable_t var, double x)
 double func_reno2_f(lmm_variable_t var, double x)
 {
   xbt_assert(var->weight > 0.0, "Don't call me with stupid values!");
-  return RENO2_SCALING * (1.0 / var->weight) * log((x * var->weight) /
-                                                   (2.0 * x * var->weight +
-                                                    3.0));
+  return RENO2_SCALING * (1.0 / var->weight) * log((x * var->weight) / (2.0 * x * var->weight + 3.0));
 }
 
 double func_reno2_fp(lmm_variable_t var, double x)
 {
-  return RENO2_SCALING * 3.0 / (var->weight * x *
-                                (2.0 * var->weight * x + 3.0));
+  return RENO2_SCALING * 3.0 / (var->weight * x * (2.0 * var->weight * x + 3.0));
 }
 
 double func_reno2_fpi(lmm_variable_t var, double x)
 {
-  double res_fpi;
-  double tmp;
-
   xbt_assert(x > 0.0, "Don't call me with stupid values!");
-  tmp = x * var->weight * var->weight;
-  res_fpi = tmp * (9.0 * x + 24.0);
+  double tmp = x * var->weight * var->weight;
+  double res_fpi = tmp * (9.0 * x + 24.0);
 
   if (res_fpi <= 0.0)
     return 0.0;