/*
* Lagrange Variables.
*/
- int max_iterations= 1000000;
- double epsilon_min_error = 0.00001;
+ int max_iterations= 10;
+ double epsilon_min_error = 1e-10;
double overall_error = 1;
double min, max, middle;
lmm_constraint_t cnst1 = NULL;
xbt_swag_t var_list = NULL;
- lmm_variable_t var1 = NULL;
+_variable_t var1 = NULL;
/*
if((var1->bound >= 0) && (var1->weight > 0) ){
//for each link with capacity cnsts[i] that uses flow of variable var1 do
//begin dicotomi
- min = max = 1.0;
+ min = max = var1->mu;
overall_error = 1;
while(overall_error < epsilon_min_error){
if( partial_diff_mu(min, var1)>0 && partial_diff_mu(max, var1)>0 ){
if(min == max){
max = max * 2;
}else{
- max = min;
+ max = min;
}
}else if( partial_diff_mu(min,var1)<0 && partial_diff_mu(max,var1) > 0 ){
if(min == max){
}
}
- var1->new_mu = max;
+ var1->mu = max;
- if(var1->new_mu < 0){
- var1->new_mu = 0;
+ if(var1->mu < 0){
+ var1->mu = 0;
}
}
}
*/
xbt_swag_foreach(cnst1, cnst_list) {
+
+ DEBUG2("cnst1 (id=%s) (%p)", (char *)cnst1->id, cnst1);
+
//begin dicotomi
+ i=0;
overall_error = 1;
- min = max = 1.0;
- while(overall_error < epsilon_min_error){
+ min = max = cnst1->lambda;
+ while(overall_error > epsilon_min_error){
+ i++;
+
+
+ // DEBUG4("====> Dicotomi debug. [%e, %e], D(min,max) = [%e, %e]", min, max, partial_diff_lambda(min, cnst1), partial_diff_lambda(max, cnst1));
+
if( partial_diff_lambda(min, cnst1) > 0 && partial_diff_lambda(max, cnst1) > 0 ){
if(min == max){
- min = min / 2;
+ min = min / 2.0;
}else{
max = min;
}
}else if( partial_diff_lambda(min, cnst1) < 0 && partial_diff_lambda(max, cnst1) < 0 ){
if(min == max){
- max = max * 2;
+ max = max * 2.0;
}else{
- max = min;
+ min = max;
}
}else if( partial_diff_lambda(min,cnst1) < 0 && partial_diff_lambda(max,cnst1) > 0 ){
- if(min == max){
- middle = partial_diff_lambda((fabs(min - max)/2), cnst1);
- if( middle > 0 ){
- max = (fabs(min - max)/2);
- }else if( middle < 0 ){
- min = (fabs(min - max)/2);
- }else{
- WARN0("Found an optimal solution with 0 error!");
- overall_error = 0;
- }
- overall_error = fabs(min - max);
+ middle = (max + min)/2.0;
+
+
+ //DEBUG2("Ideal state reached middle = %e, D(fabs(min-max)/2.0) = %e", middle, partial_diff_lambda(middle, cnst1));
+ if( partial_diff_lambda(middle, cnst1) < 0 ){
+ min = middle;
+ }else if( partial_diff_lambda(middle, cnst1) > 0 ){
+ max = middle;
+ }else{
+ WARN0("Found an optimal solution with 0 error!");
+ overall_error = 0;
}
+ overall_error = fabs(min - max);
}else{
WARN0("The impossible happened, partial_diff(min) >0 && partial_diff(max) < 0");
}
}
- cnst1->new_lambda = cnst1->lambda;
- if(cnst1->new_lambda < 0){
- cnst1->new_lambda = 0;
+
+ DEBUG1("Number of iteration in the dicotomi %d", i);
+
+ cnst1->lambda = min;
+
+ if(cnst1->lambda < 0){
+ cnst1->lambda = 0;
}
}
* the values of \lambda and \mu.
*/
overall_error=0;
+ DEBUG1("Iteration %d ", iteration);
xbt_swag_foreach(var1, var_list) {
if(var1->weight <=0)
var1->value = 0.0;
var1->value = 1.0 / tmp;
}
-
- }
- /* Updating lambda's and mu's */
- xbt_swag_foreach(var1, var_list)
- if(!((var1->bound > 0.0) || (var1->weight <= 0.0)))
- var1->mu = var1->new_mu;
-
-
- xbt_swag_foreach(cnst1, cnst_list)
- cnst1->lambda = cnst1->new_lambda;
+ DEBUG2("======> value of var1 (%p) = %e", var1, var1->value);
+ }
}
+
+
+
//verify the KKT property
xbt_swag_foreach(cnst1, cnst_list){
tmp = 0;
}
+double dicotomi(double init, void *diff(double, void*), void *var_cnst){
+ double min, max;
+ double overall_error;
+
+ min = max = init;
+ overall_error = 1;
+
+ while(overall_error > epsilon_min_error){
+ if( diff(min, var_cnst) > 0 && diff(max, var_cnst) > 0 ){
+ if(min == max){
+ min = min / 2.0;
+ }else{
+ max = min;
+ }
+ }else if( diff(min, var_cnst) < 0 && diff(max, var_cnst) < 0 ){
+ if(min == max){
+ max = max * 2.0;
+ }else{
+ min = max;
+ }
+ }else if( diff(min, var_cnst) < 0 && diff(max, var_cnst) > 0 ){
+ middle = (max + min)/2.0;
+
+ if( diff(middle, var_cnst) < 0 ){
+ min = middle;
+ }else if( diff(middle, var_cnst) > 0 ){
+ max = middle;
+ }else{
+ WARN0("Found an optimal solution with 0 error!");
+ overall_error = 0;
+ }
+ overall_error = fabs(min - max);
+ }else{
+ WARN0("The impossible happened, partial_diff(min) >0 && partial_diff(max) < 0");
+ }
+ }
+}
double partial_diff_mu(double mu, lmm_variable_t var1){
double mu_partial=0.0;
lambda_partial += (-1.0 /tmp);
}
+
+ lambda_partial += cnst1->bound;
+
+ //DEBUG3("Partial diff lambda result cnst1 %s (%p) : %e", (char *)cnst1->id, cnst1, lambda_partial);
return lambda_partial;
}