+
+/** \brief Attribute the value bound to var->bound.
+ *
+ * \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.
+ *
+ */
+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.
+ */
+
+/*
+ * For Vegas: $f(x) = \alpha D_f\ln(x)$
+ * Therefore: $fp(x) = \frac{\alpha D_f}{x}$
+ * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$
+ */
+#define VEGAS_SCALING 1000.0
+
+double func_vegas_f(lmm_variable_t var, double x){
+ xbt_assert0(x>0.0,"Don't call me with stupid values!");
+ return VEGAS_SCALING*var->df*log(x);
+}
+
+double func_vegas_fp(lmm_variable_t var, double x){
+ xbt_assert0(x>0.0,"Don't call me with stupid values!");
+ return VEGAS_SCALING*var->df/x;
+}
+
+double func_vegas_fpi(lmm_variable_t var, double x){
+ xbt_assert0(x>0.0,"Don't call me with stupid values!");
+ return var->df/(x/VEGAS_SCALING);
+}
+
+/*
+ * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$
+ * Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$
+ * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
+ */
+#define RENO_SCALING 1.0
+double func_reno_f(lmm_variable_t var, double x){
+ xbt_assert0(var->df>0.0,"Don't call me with stupid values!");
+
+ return RENO_SCALING*sqrt(3.0/2.0)/var->df*atan(sqrt(3.0/2.0)*var->df*x);
+}
+
+double func_reno_fp(lmm_variable_t var, double x){
+ return RENO_SCALING*3.0/(3.0*var->df*var->df*x*x +2.0);
+}
+
+double func_reno_fpi(lmm_variable_t var, double x){
+ double res_fpi;
+
+ xbt_assert0(var->df>0.0,"Don't call me with stupid values!");
+ xbt_assert0(x>0.0,"Don't call me with stupid values!");
+
+ res_fpi = 1.0/(var->df*var->df*(x/RENO_SCALING)) - 2.0/(3.0*var->df*var->df);
+ if(res_fpi<=0.0) return 0.0;
+/* xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); */
+ return sqrt(res_fpi);
+}
+
+