/* This program is free software; you can redistribute it and/or modify it
* under the terms of the license (GNU LGPL) which comes with this package. */
/* This program is free software; you can redistribute it and/or modify it
* under the terms of the license (GNU LGPL) which comes with this package. */
* 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".
*/
* 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".
*/
#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)");
#define SHOW_EXPR(expr) XBT_CDEBUG(surf_lagrange,#expr " = %g",expr);
#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)");
#define SHOW_EXPR(expr) XBT_CDEBUG(surf_lagrange,#expr " = %g",expr);
double (*func_f_def) (lmm_variable_t, double);
double (*func_fp_def) (lmm_variable_t, double);
double (*func_f_def) (lmm_variable_t, double);
double (*func_fp_def) (lmm_variable_t, double);
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);
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 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 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)
{
xbt_swag_t elem_list = nullptr;
lmm_element_t elem = nullptr;
lmm_constraint_t cnst = nullptr;
lmm_variable_t var = nullptr;
xbt_swag_t elem_list = nullptr;
lmm_element_t elem = nullptr;
lmm_constraint_t cnst = nullptr;
lmm_variable_t var = nullptr;
elem_list = &(cnst->enabled_element_set);
xbt_swag_foreach(_elem, elem_list) {
elem = static_cast<lmm_element_t>(_elem);
var = elem->variable;
elem_list = &(cnst->enabled_element_set);
xbt_swag_foreach(_elem, elem_list) {
elem = static_cast<lmm_element_t>(_elem);
var = elem->variable;
xbt_swag_foreach(_var, var_list) {
var = static_cast<lmm_variable_t>(_var);
xbt_swag_foreach(_var, var_list) {
var = static_cast<lmm_variable_t>(_var);
- 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->sharing_weight);
//uses the partial differential inverse function
return var->func_fpi(var, tmp);
}
//uses the partial differential inverse function
return var->func_fpi(var, tmp);
}
- /* 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;
-
- xbt_swag_t var_list = nullptr;
- void *_var;
- lmm_variable_t var = nullptr;
-
- /* Auxiliary variables. */
- int iteration = 0;
- double tmp = 0;
- int i;
- 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);
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
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);
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- XBT_DEBUG("#### var(%p) ->weight : %e", var, var->weight);
+ var->value = new_value(var);
+ XBT_DEBUG("#### var(%p) ->weight : %e", var, var->sharing_weight);
- for (i = 0; i < var->cnsts_number; i++) {
- if (var->cnsts[i].value == 0.0)
- nb++;
- }
- if (nb == var->cnsts_number)
+ auto weighted = std::find_if(begin(var->cnsts), end(var->cnsts),
+ [](s_lmm_element_t const& x) { return x.consumption_weight != 0.0; });
+ if (weighted == end(var->cnsts))
while (overall_modification > epsilon_min_error && iteration < max_iterations) {
iteration++;
XBT_DEBUG("************** ITERATION %d **************", iteration);
while (overall_modification > epsilon_min_error && iteration < max_iterations) {
iteration++;
XBT_DEBUG("************** ITERATION %d **************", iteration);
/* Improve the value of mu_i */
xbt_swag_foreach(_var, var_list) {
/* Improve the value of mu_i */
xbt_swag_foreach(_var, var_list) {
XBT_DEBUG("Working on var (%p)", var);
var->new_mu = new_mu(var);
XBT_DEBUG("Working on var (%p)", var);
var->new_mu = new_mu(var);
XBT_DEBUG("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, var->mu, var->new_mu);
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;
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("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 */
xbt_swag_foreach(_cnst, cnst_list) {
/* Improve the value of lambda_i */
xbt_swag_foreach(_cnst, cnst_list) {
XBT_DEBUG("Working on cnst (%p)", cnst);
cnst->new_lambda = dichotomy(cnst->lambda, partial_diff_lambda, cnst, dichotomy_min_error);
XBT_DEBUG("Working on cnst (%p)", cnst);
cnst->new_lambda = dichotomy(cnst->lambda, partial_diff_lambda, cnst, dichotomy_min_error);
XBT_DEBUG("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", cnst, cnst->lambda, cnst->new_lambda);
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;
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("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 convergence ----------");
overall_modification = 0;
xbt_swag_foreach(_var, var_list) {
XBT_DEBUG("-------------- Check convergence ----------");
overall_modification = 0;
xbt_swag_foreach(_var, var_list) {
var->value = tmp;
XBT_DEBUG("New value of var (%p) = %e, overall_modification = %e", var, var->value, overall_modification);
var->value = tmp;
XBT_DEBUG("New value of var (%p) = %e, overall_modification = %e", var, var->value, overall_modification);
overall_modification = 1.0;
XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
overall_modification = 1.0;
XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
*
* @param init initial value for \mu or \lambda
* @param diff a function that computes the differential of with respect a \mu or \lambda
*
* @param init initial value for \mu or \lambda
* @param diff a function that computes the differential of with respect a \mu or \lambda
* @param min_erro a minimum error tolerated
*
* @return a double corresponding to the result of the dichotomy process
* @param min_erro a minimum error tolerated
*
* @return a double corresponding to the result of the dichotomy process
} else if (middle_diff > 0) {
XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max");
max = middle;
overall_error = max_diff - middle_diff;
max_diff = middle_diff;
} else if (middle_diff > 0) {
XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max");
max = middle;
overall_error = max_diff - middle_diff;
max_diff = middle_diff;
} 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_abort();
} 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_abort();
- elem = static_cast<lmm_element_t>(_elem);
- var = elem->variable;
- xbt_assert(var->weight > 0);
+ lmm_element_t elem = static_cast<lmm_element_t>(_elem);
+ lmm_variable_t var = elem->variable;
+ xbt_assert(var->sharing_weight > 0);
XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)", var);
// Initialize the summation variable
XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)", var);
// Initialize the summation variable
double func_vegas_f(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
double func_vegas_f(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
}
double func_vegas_fp(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
}
double func_vegas_fp(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
}
double func_vegas_fpi(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
}
double func_vegas_fpi(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
* Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$
* Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
*/
* Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$
* Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
*/
- 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));
+ xbt_assert(var->sharing_weight > 0.0, "Don't call me with stupid values!");
+ return RENO2_SCALING * (1.0 / var->sharing_weight) *
+ log((x * var->sharing_weight) / (2.0 * x * var->sharing_weight + 3.0));
}
double func_reno2_fpi(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values!");
}
double func_reno2_fpi(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values!");