-/*
- * NOTE for Reno: all functions consider the network
- * coeficient (alpha) equal to 1.
- */
-
-/*
- * For Reno f: $\alpha_f d_f \log\left(x_f\right)$
- */
-double func_reno_f(lmm_variable_t var, double x){
- xbt_assert0(x,"Please report this bug.");
- return var->df * log(x);
-}
-
-/*
- * For Reno fp: $\frac{\alpha D_f}{x}$
- */
-double func_reno_fp(lmm_variable_t var, double x){
- xbt_assert0(x,"Please report this bug.");
- return var->df/x;
-}
-
-/*
- * For Reno fpi: $\frac{\alpha D_f}{x}$
- */
-double func_reno_fpi(lmm_variable_t var, double x){
- xbt_assert0(x,"Please report this bug.");
- return var->df/x;
-}
-
-/*
- * For Reno fpip: $-\frac{\alpha D_f}{x^2}$
- */
-double func_reno_fpip(lmm_variable_t var, double x){
- xbt_assert0(x,"Please report this bug.");
- return -( var->df/(x*x) ) ;
-}
-
-
-/*
- * For Vegas f: $\frac{\sqrt{\frac{3}{2}}}{D_f} \arctan\left(\sqrt{\frac{3}{2}}x_f D_f\right)$
+/** \brief Update the constraint set propagating recursively to
+ * other constraints so the system should not be entirely computed.
+ *
+ * \param sys the lmm_system_t
+ * \param cnst the lmm_constraint_t affected by the change
+ *
+ * A recursive algorithm to optimize the system recalculation selecting only
+ * constraints that have changed. Each constraint change is propagated
+ * to the list of constraints for each variable.