#include <math.h>
#endif
-#define VEGAS_SCALING 1000.0
-
-
XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf,
"Logging specific to SURF (lagrange)");
-XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf,
+XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf_lagrange,
"Logging specific to SURF (lagrange dichotomy)");
/*
//solves the proportional fairness using a lagrange optimizition 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
-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 variable param_var applied to mu
-double partial_diff_mu(double mu, void *param_var);
+static double partial_diff_mu(double mu, void *param_var);
//computes the value of the differential of constraint param_cnst applied to lambda
-double partial_diff_lambda(double lambda, void *param_cnst);
+static double partial_diff_lambda(double lambda, void *param_cnst);
//auxiliar function to compute the partial_diff
-double diff_aux(lmm_variable_t var, double x);
+static double diff_aux(lmm_variable_t var, double x);
static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn)
*/
int max_iterations = 100;
double epsilon_min_error = MAXMIN_PRECISION;
- double dichotomy_min_error = 1e-20;
- double overall_error = 1;
+ double dichotomy_min_error = 1e-18;
+ double overall_modification = 1;
/*
* Variables to manipulate the data structure proposed to model the maxmin
/*
* While doesn't reach a minimun error or a number maximum of iterations.
*/
- while (overall_error > epsilon_min_error && iteration < max_iterations) {
+ while (overall_modification > epsilon_min_error && iteration < max_iterations) {
int dual_updated=0;
iteration++;
* the values of \lambda and \mu.
*/
DEBUG0("-------------- Check convergence ----------");
- overall_error = 0;
+ overall_modification = 0;
xbt_swag_foreach(var, var_list) {
if (var->weight <= 0)
var->value = 0.0;
//uses the partial differential inverse function
tmp = var->func_fpi(var, tmp);
- //computes de overall_error using normalized value
- if (overall_error < (fabs(var->value - tmp)/tmp)) {
- overall_error = (fabs(var->value - tmp)/tmp);
- }
-
- if (overall_error < (fabs(var->value - tmp))) {
- overall_error = (fabs(var->value - tmp));
+ if (overall_modification < (fabs(var->value - tmp)/tmp)) {
+ overall_modification = (fabs(var->value - tmp)/tmp);
}
var->value = tmp;
- DEBUG3("New value of var (%p) = %e, overall_error = %e", var,
- var->value, overall_error);
+ DEBUG3("New value of var (%p) = %e, overall_modification = %e", var,
+ var->value, overall_modification);
}
}
if (!__check_feasible(cnst_list, var_list, 0))
- overall_error = 1.0;
- DEBUG2("Iteration %d: Overall_error : %f", iteration, overall_error);
+ overall_modification = 1.0;
+ DEBUG2("Iteration %d: overall_modification : %f", iteration, overall_modification);
if(!dual_updated) {
- DEBUG1("Could not improve the convergence at iteration %d. Drop it!",iteration);
+ WARN1("Could not improve the convergence at iteration %d. Drop it!",iteration);
break;
}
}
__check_feasible(cnst_list, var_list, 1);
- if (overall_error <= epsilon_min_error) {
+ if (overall_modification <= epsilon_min_error) {
DEBUG1("The method converges in %d iterations.", iteration);
}
if (iteration >= max_iterations) {
*
* @return a double correponding to the result of the dichotomyal process
*/
-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 overall_error;
CDEBUG1(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",middle);
if((min==middle) || (max==middle)) {
- DEBUG0("Cannot improve the convergence!");
+ CWARN4(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);
CDEBUG0(surf_lagrange_dichotomy, "Increasing min");
min = middle;
min_diff = middle_diff;
+ overall_error = max_diff-middle_diff;
} else if (middle_diff > 0) {
CDEBUG0(surf_lagrange_dichotomy, "Decreasing max");
max = middle;
max_diff = middle_diff;
+ overall_error = max_diff-middle_diff;
} else {
overall_error = 0;
}
/*
*
*/
-double partial_diff_mu(double mu, void *param_var)
+static double partial_diff_mu(double mu, void *param_var)
{
double mu_partial = 0.0;
double sigma_mu = 0.0;
/*
*
*/
-double partial_diff_lambda(double lambda, void *param_cnst)
+static double partial_diff_lambda(double lambda, void *param_cnst)
{
int i;
}
-double diff_aux(lmm_variable_t var, double x)
+static double diff_aux(lmm_variable_t var, double x)
{
double tmp_fpi, result;
return result;
}
+/** \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_fpi) (lmm_variable_t var, double x))
+{
+ func_fpi_def = func_fpi;
+}
+
/**************** Vegas and Reno functions *************************/
/*
*/
/*
- * For Vegas fpi: $\frac{\alpha D_f}{x}$
+ * For Vegas: $f(x) = \alpha D_f\ln(x)$
+ * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$
*/
+#define VEGAS_SCALING 1000.0
double func_vegas_fpi(lmm_variable_t var, double x){
xbt_assert0(x>0.0,"Don't call me with stupid values!");
return VEGAS_SCALING*var->df/x;
}
/*
- * For Reno fpi: $\sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
+ * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$
+ * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
*/
+#define RENO_SCALING 1.0
double func_reno_fpi(lmm_variable_t var, double x){
double res_fpi;
res_fpi = 1/(var->df*var->df*x) - 2/(3*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);
+/* xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); */
+ return sqrt(RENO_SCALING*res_fpi);
}
