#include "xbt/sysdep.h"
#include <algorithm>
-#include <cstdlib>
-#ifndef MATH
#include <cmath>
-#endif
+#include <cstdlib>
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);
-#define VEGAS_SCALING 1000.0
-#define RENO_SCALING 1.0
-#define RENO2_SCALING 1.0
+static constexpr double VEGAS_SCALING = 1000.0;
+static constexpr double RENO_SCALING = 1.0;
+static constexpr double RENO2_SCALING = 1.0;
namespace simgrid {
namespace kernel {
double (*func_fp_def)(const Variable&, double);
double (*func_fpi_def)(const Variable&, double);
+System* make_new_lagrange_system(bool selective_update)
+{
+ return new Lagrange(selective_update);
+}
+
/*
* Local prototypes to implement the Lagrangian optimization with optimal step, also called dichotomy.
*/
-// solves the proportional fairness using a Lagrangian optimization with dichotomy step
-void lagrange_solve(kernel::lmm::System* 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, const Constraint&), const Constraint& cnst, double min_error);
// computes the value of the differential of constraint cnst applied to lambda
static double partial_diff_lambda(double lambda, const Constraint& cnst);
-template <class CnstList, class VarList>
-static int __check_feasible(const CnstList& cnst_list, const VarList& var_list, int warn)
+bool Lagrange::check_feasible(bool warn)
{
- for (Constraint const& cnst : cnst_list) {
+ for (Constraint const& cnst : active_constraint_set) {
double tmp = 0;
for (Element const& elem : cnst.enabled_element_set) {
Variable* var = elem.variable;
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);
- return 0;
+ return false;
}
XBT_DEBUG("Checking feasability for constraint (%p): sat = %f, lambda = %f ", &cnst, tmp - cnst.bound, cnst.lambda);
}
- for (Variable const& var : var_list) {
+ for (Variable const& var : variable_set) {
if (not var.sharing_weight)
break;
if (var.bound < 0)
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);
- return 0;
+ return false;
}
}
- return 1;
+ return true;
}
static double new_value(const Variable& var)
return mu_i;
}
-template <class VarList, class CnstList>
-static double dual_objective(const VarList& var_list, const CnstList& cnst_list)
+double Lagrange::dual_objective()
{
double obj = 0.0;
- for (Variable const& var : var_list) {
+ for (Variable const& var : variable_set) {
double sigma_i = 0.0;
if (not var.sharing_weight)
obj += var.mu * var.bound;
}
- for (Constraint const& cnst : cnst_list)
+ for (Constraint const& cnst : active_constraint_set)
obj += cnst.lambda * cnst.bound;
return obj;
}
-void lagrange_solve(kernel::lmm::System* sys)
+// solves the proportional fairness using a Lagrangian optimization with dichotomy step
+void Lagrange::lagrange_solve()
{
/* Lagrange Variables. */
int max_iterations = 100;
XBT_DEBUG("#### Minimum error tolerated (dichotomy) : %e", dichotomy_min_error);
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- sys->print();
+ print();
}
- if (not sys->modified)
+ if (not modified)
return;
/* Initialize lambda. */
- auto& cnst_list = sys->active_constraint_set;
+ auto& cnst_list = active_constraint_set;
for (Constraint& cnst : cnst_list) {
cnst.lambda = 1.0;
cnst.new_lambda = 2.0;
/*
* Initialize the var_list variable with only the active variables. Initialize mu.
*/
- auto& var_list = sys->variable_set;
+ auto& var_list = variable_set;
for (Variable& var : var_list) {
if (not var.sharing_weight)
var.value = 0.0;
}
/* Compute dual objective. */
- double obj = dual_objective(var_list, cnst_list);
+ double obj = dual_objective();
/* While doesn't reach a minimum error or a number maximum of iterations. */
int iteration = 0;
XBT_DEBUG("Updating mu : var->mu (%p) : %1.20f -> %1.20f", &var, var.mu, var.new_mu);
var.mu = var.new_mu;
- double new_obj = dual_objective(var_list, cnst_list);
+ double new_obj = dual_objective();
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;
XBT_DEBUG("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", &cnst, cnst.lambda, cnst.new_lambda);
cnst.lambda = cnst.new_lambda;
- double new_obj = dual_objective(var_list, cnst_list);
+ double new_obj = dual_objective();
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;
}
XBT_DEBUG("-------------- Check feasability ----------");
- if (not __check_feasible(cnst_list, var_list, 0))
+ if (not check_feasible(false))
overall_modification = 1.0;
XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
}
- __check_feasible(cnst_list, var_list, 1);
+ check_feasible(true);
if (overall_modification <= epsilon_min_error) {
XBT_DEBUG("The method converges in %d iterations.", iteration);
}
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- sys->print();
+ print();
}
}
