/* $Id$ */
-
/* Copyright (c) 2007 Arnaud Legrand, Pedro Velho. All rights reserved. */
-
/* 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. */
-
/*
* Modelling the proportional fairness using the Lagrange Optimization
* Approach. For a detailed description see:
/*
* Lagrange Variables.
*/
- int max_iterations= 100;
+ int max_iterations= 10000;
double epsilon_min_error = 1e-4;
double dicotomi_min_error = 1e-8;
double overall_error = 1;
xbt_swag_t elem_list = NULL;
lmm_element_t elem = NULL;
-
xbt_swag_t cnst_list = NULL;
lmm_constraint_t cnst = NULL;
DEBUG1("#### Minimum error tolerated : %e", epsilon_min_error);
DEBUG1("#### Minimum error tolerated (dicotomi) : %e", dicotomi_min_error);
-
if ( !(sys->modified))
return;
*/
var_list = &(sys->variable_set);
i=0;
- xbt_swag_foreach(var, var_list) { if((var->bound > 0.0) || (var->weight <= 0.0)){
- DEBUG1("#### NOTE var(%d) is a boundless variable", i);
- var->mu = -1.0;
- } else{
- var->mu = 1.0;
- var->new_mu = 2.0;
- }
- DEBUG2("#### var(%d)->mu : %e", i, var->mu);
- DEBUG2("#### var(%d)->weight: %e", i, var->weight);
- i++;
+ xbt_swag_foreach(var, var_list) {
+ if((var->bound > 0.0) || (var->weight <= 0.0)){
+ DEBUG1("#### NOTE var(%d) is a boundless variable", i);
+ var->mu = -1.0;
+ } else{
+ var->mu = 1.0;
+ var->new_mu = 2.0;
+ }
+ DEBUG2("#### var(%d)->mu : %e", i, var->mu);
+ DEBUG2("#### var(%d)->weight: %e", i, var->weight);
+ i++;
}
/*
* Initialize lambda.
*/
cnst_list=&(sys->active_constraint_set);
- xbt_swag_foreach(cnst, cnst_list) {
+ xbt_swag_foreach(cnst, cnst_list){
cnst->lambda = 1.0;
cnst->new_lambda = 2.0;
DEBUG2("#### cnst(%p)->lambda : %e", cnst, cnst->lambda);
iteration++;
DEBUG1("************** ITERATION %d **************", iteration);
-
/*
* Compute the value of mu_i
*/
//forall lambda_i in lambda_1, lambda_2, ..., lambda_n
xbt_swag_foreach(cnst, cnst_list) {
cnst->new_lambda = dicotomi(cnst->lambda, partial_diff_lambda, cnst, dicotomi_min_error);
- if(cnst->new_lambda < 0) cnst->new_lambda = 0;
+ DEBUG2("====> cnst->lambda (%p) = %e", cnst, cnst->new_lambda);
}
-
/*
* Update values of mu and lambda
*/
cnst->lambda = cnst->new_lambda;
}
-
/*
* Now computes the values of each variable (\rho) based on
* the values of \lambda and \mu.
if(var->bound > 0)
tmp+=var->mu;
}
+
+ if(tmp == 0.0)
+ WARN0("CAUTION: division by 0.0");
+
//computes de overall_error
if(overall_error < fabs(var->value - 1.0/tmp)){
overall_error = fabs(var->value - 1.0/tmp);
double min, max;
double overall_error;
double middle;
-
+ double min_diff, max_diff, middle_diff;
+
min = max = init;
+ min_diff = max_diff = middle_diff = 0.0;
overall_error = 1;
- //DEBUG0("STARTING DICOTOMI... Debugging, format used [min, max], [D(min),D(max)]");
+ if(diff(0.0, var_cnst) > 0){
+ DEBUG1("====> returning 0.0 (diff = %e)", diff(0.0, var_cnst));
+ return 0.0;
+ }
while(overall_error > min_error){
- //DEBUG4("====> [%e, %e] , [%e,%e]", min, max, diff(min, var_cnst), diff(max, var_cnst));
+ min_diff = diff(min, var_cnst);
+ max_diff = diff(max, var_cnst);
- if( diff(min, var_cnst) > 0 && diff(max, var_cnst) > 0 ){
+ if( min_diff > 0 && max_diff > 0 ){
if(min == max){
min = min / 2.0;
}else{
max = min;
}
- }else if( diff(min, var_cnst) < 0 && diff(max, var_cnst) < 0 ){
+ }else if( min_diff < 0 && max_diff < 0 ){
if(min == max){
max = max * 2.0;
}else{
min = max;
}
- }else if( diff(min, var_cnst) < 0 && diff(max, var_cnst) > 0 ){
+ }else if( min_diff < 0 && max_diff > 0 ){
middle = (max + min)/2.0;
-
- if( diff(middle, var_cnst) < 0 ){
+ middle_diff = diff(middle, var_cnst);
+ overall_error = fabs(min - max);
+
+ if( middle_diff < 0 ){
min = middle;
- }else if( diff(middle, var_cnst) > 0 ){
+ }else if( middle_diff > 0 ){
max = middle;
}else{
WARN0("Found an optimal solution with 0 error!");
overall_error = 0;
+ return middle;
}
- overall_error = fabs(min - max);
- }else{
- WARN0("The impossible happened, partial_diff(min) >0 && partial_diff(max) < 0");
+
+ }else if(min_diff == 0){
+ return min;
+ }else if(max_diff == 0){
+ return max;
+ }else if(min_diff > 0 && max_diff < 0){
+ WARN0("The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0");
}
}
+
+ DEBUG1("====> returning %e", (min+max)/2.0);
return ((min+max)/2.0);
}
+/*
+ *
+ */
double partial_diff_mu(double mu, void *param_var){
double mu_partial=0.0;
lmm_variable_t var = (lmm_variable_t)param_var;
return mu_partial;
}
-
+/*
+ *
+ */
double partial_diff_lambda(double lambda, void *param_cnst){
double tmp=0.0;
elem_list = &(cnst->element_set);
+
+
+ DEBUG2("Computting diff of cnst (%p) %s", cnst, (char *)cnst->id);
xbt_swag_foreach(elem, elem_list) {
var = elem->variable;
if(var->weight<=0) continue;
tmp = 0;
+
+ DEBUG2("===> Variable (%p) %s", var, (char *)var->id);
+
for(i=0; i<var->cnsts_number; i++){
tmp += (var->cnsts[i].constraint)->lambda;
+ DEBUG1("======> lambda %e + ", (var->cnsts[i].constraint)->lambda);
}
if(var->bound > 0)
tmp += var->mu;
+
+ DEBUG2("======> lambda - %e + %e ", cnst->lambda, lambda);
+
tmp = tmp - cnst->lambda + lambda;
//avoid a disaster value of lambda
- if(tmp==0) lambda_partial = 10e-8;
+ //if(tmp==0) tmp = 10e-8;
- lambda_partial += (-1.0 /tmp);
+ lambda_partial += (-1.0/tmp);
+
+ DEBUG1("======> %e ", (-1.0/tmp));
}
lambda_partial += cnst->bound;
+ DEBUG1("===> %e ", lambda_partial);
+
return lambda_partial;
}
+
