From: velho Date: Tue, 24 Apr 2007 09:17:53 +0000 (+0000) Subject: Removed this file, it is just an experience of optimal step before X-Git-Tag: v3.3~1917 X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/commitdiff_plain/bdaf3e0e35da86e81694db125a7df5effa9f9658 Removed this file, it is just an experience of optimal step before achieve a formal proof that the method always converge. git-svn-id: svn+ssh://scm.gforge.inria.fr/svn/simgrid/simgrid/trunk@3443 48e7efb5-ca39-0410-a469-dd3cf9ba447f --- diff --git a/src/surf/lagrangedico.c b/src/surf/lagrangedico.c deleted file mode 100644 index e0e2af1946..0000000000 --- a/src/surf/lagrangedico.c +++ /dev/null @@ -1,397 +0,0 @@ -/* $Id$ */ - -/* 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: - * "ssh://username@scm.gforge.inria.fr/svn/memo/people/pvelho/lagrange/ppf.ps". - */ -#include "xbt/log.h" -#include "xbt/sysdep.h" -#include "xbt/mallocator.h" -#include "maxmin_private.h" - -#include -#ifndef MATH -#include -#endif - - -XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrangedico, surf, - "Logging specific to SURF (lagrange)"); - - -void lagrange_dicotomi_solve(lmm_system_t sys); - -double partial_diff_mu(double mu, lmm_variable_t var1); -double partial_diff_lambda(double lambda, lmm_constraint_t cnst1); - -void lagrange_dicotomi_solve(lmm_system_t sys) -{ - /* - * Lagrange Variables. - */ - int max_iterations= 10; - double epsilon_min_error = 1e-10; - double overall_error = 1; - double min, max, middle; - - - /* - * Variables to manipulate the data structure proposed to model the maxmin - * fairness. See docummentation for more details. - */ - xbt_swag_t elem_list = NULL; - lmm_element_t elem1 = NULL; - - - xbt_swag_t cnst_list = NULL; - lmm_constraint_t cnst1 = NULL; - - xbt_swag_t var_list = NULL; -_variable_t var1 = NULL; - - - /* - * Auxiliar variables. - */ - int iteration=0; - double tmp=0; - int i; - - - DEBUG0("Iterative method configuration snapshot =====>"); - DEBUG1("#### Maximum number of iterations : %d", max_iterations); - DEBUG1("#### Minimum error tolerated : %e", epsilon_min_error); - - - if ( !(sys->modified)) - return; - - /* - * Initialize the var list variable with only the active variables. - * Associate an index in the swag variables. Initialize mu. - */ - var_list = &(sys->variable_set); - i=0; - xbt_swag_foreach(var1, var_list) { - if((var1->bound > 0.0) || (var1->weight <= 0.0)){ - DEBUG1("#### NOTE var1(%d) is a boundless variable", i); - var1->mu = -1.0; - } else{ - var1->mu = 1.0; - var1->new_mu = 2.0; - } - DEBUG2("#### var1(%d)->mu : %e", i, var1->mu); - DEBUG2("#### var1(%d)->weight: %e", i, var1->weight); - i++; - } - - /* - * Initialize lambda. - */ - cnst_list=&(sys->active_constraint_set); - xbt_swag_foreach(cnst1, cnst_list) { - cnst1->lambda = 1.0; - cnst1->new_lambda = 2.0; - DEBUG2("#### cnst1(%p)->lambda : %e", cnst1, cnst1->lambda); - } - - - - /* - * While doesn't reach a minimun error or a number maximum of iterations. - */ - while(overall_error > epsilon_min_error && iteration < max_iterations){ - - iteration++; - - - /* d Dual - * Compute the value of ----------- (\lambda^k, \mu^k) this portion - * d \mu_i^k - * of code depends on function f(x). - */ - var_list = &(sys->variable_set); - //forall mu_i in mu_1, mu_2, ..., mu_n - xbt_swag_foreach(var1, var_list) { - 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 = 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){ - min = min / 2; - }else{ - max = min; - } - }else if( partial_diff_mu(min, var1)<0 && partial_diff_mu(max, var1)<0 ){ - if(min == max){ - max = max * 2; - }else{ - max = min; - } - }else if( partial_diff_mu(min,var1)<0 && partial_diff_mu(max,var1) > 0 ){ - if(min == max){ - middle = partial_diff_mu((fabs(min - max)/2), var1); - 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); - } - }else{ - WARN0("The impossible happened, partial_diff(min) >0 && partial_diff(max) < 0"); - } - } - - var1->mu = max; - - if(var1->mu < 0){ - var1->mu = 0; - } - } - } - - - /* d Dual - * Compute the value of ------------- (\lambda^k, \mu^k) this portion - * d \lambda_i^k - * of code depends on function f(x). - */ - xbt_swag_foreach(cnst1, cnst_list) { - - - DEBUG2("cnst1 (id=%s) (%p)", (char *)cnst1->id, cnst1); - - //begin dicotomi - i=0; - overall_error = 1; - 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 / 2.0; - }else{ - max = min; - } - }else if( partial_diff_lambda(min, cnst1) < 0 && partial_diff_lambda(max, cnst1) < 0 ){ - if(min == max){ - max = max * 2.0; - }else{ - min = max; - } - }else if( partial_diff_lambda(min,cnst1) < 0 && partial_diff_lambda(max,cnst1) > 0 ){ - 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"); - } - } - - - DEBUG1("Number of iteration in the dicotomi %d", i); - - cnst1->lambda = min; - - if(cnst1->lambda < 0){ - cnst1->lambda = 0; - } - } - - - /* - * Now computes the values of each variable (\rho) based on - * 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; - else { - tmp = 0; - for(i=0; icnsts_number; i++){ - tmp += (var1->cnsts[i].constraint)->lambda; - if(var1->bound > 0) - tmp+=var1->mu; - } - - //computes de overall_error - if(overall_error < fabs(var1->value - 1.0/tmp)){ - overall_error = fabs(var1->value - 1.0/tmp); - } - - var1->value = 1.0 / tmp; - } - - - DEBUG2("======> value of var1 (%p) = %e", var1, var1->value); - } - } - - - - - - //verify the KKT property - xbt_swag_foreach(cnst1, cnst_list){ - tmp = 0; - elem_list = &(cnst1->element_set); - xbt_swag_foreach(elem1, elem_list) { - var1 = elem1->variable; - if(var1->weight<=0) continue; - tmp += var1->value; - } - - tmp = tmp - cnst1->bound; - - - if(tmp != 0 || cnst1->lambda != 0){ - WARN4("The link %s(%p) doesn't match the KKT property, value expected (=0) got (lambda=%e) (sum_rho=%e)", (char *)cnst1->id, cnst1, cnst1->lambda, tmp); - } - - } - - - xbt_swag_foreach(var1, var_list){ - if(var1->bound <= 0 || var1->weight <= 0) continue; - tmp = 0; - tmp = (var1->value - var1->bound); - - - if(tmp != 0 || var1->mu != 0){ - WARN4("The flow %s(%p) doesn't match the KKT property, value expected (=0) got (lambda=%e) (sum_rho=%e)", (char *)var1->id, var1, var1->mu, tmp); - } - - } - - - if(overall_error <= epsilon_min_error){ - DEBUG1("The method converge in %d iterations.", iteration); - }else{ - WARN1("Method reach %d iterations, which is the maxmimun number of iterations allowed.", iteration); - } - - - -} - - -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; - int i; - - //for each link with capacity cnsts[i] that uses flow of variable var1 do - for(i=0; icnsts_number; i++) - mu_partial += (var1->cnsts[i].constraint)->lambda + mu; - - mu_partial = (-1.0/mu_partial) + var1->bound; - - return mu_partial; -} - - -double partial_diff_lambda(double lambda, lmm_constraint_t cnst1){ - - double tmp=0.0; - int i; - double lambda_partial=0.0; - xbt_swag_t elem_list = NULL; - lmm_element_t elem1 = NULL; - lmm_variable_t var1 = NULL; - - - elem_list = &(cnst1->element_set); - - xbt_swag_foreach(elem1, elem_list) { - var1 = elem1->variable; - if(var1->weight<=0) continue; - - tmp = 0; - for(i=0; icnsts_number; i++){ - tmp += (var1->cnsts[i].constraint)->lambda; - } - - if(var1->bound > 0) - tmp += var1->mu; - - tmp = tmp - cnst1->lambda + lambda; - - //un peux du bricolage pour evite la catastrophe - if(tmp==0) lambda_partial = 10e-8; - - 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; -} -