X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/70babbb010af58f013725e2bbf411f0e3982364a..6a6157abf9619875297ff1d16c1e5c63c526d4c1:/src/surf/lagrange.c diff --git a/src/surf/lagrange.c b/src/surf/lagrange.c index b9353c61a0..62a255a36a 100644 --- a/src/surf/lagrange.c +++ b/src/surf/lagrange.c @@ -1,7 +1,9 @@ -/* $Id$ */ -/* Copyright (c) 2007 Arnaud Legrand, Pedro Velho. All rights reserved. */ +/* Copyright (c) 2007, 2008, 2009, 2010. The SimGrid Team. + * 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: @@ -9,7 +11,6 @@ */ #include "xbt/log.h" #include "xbt/sysdep.h" -#include "xbt/mallocator.h" #include "maxmin_private.h" #include @@ -17,13 +18,16 @@ #include #endif -#define VEGAS_SCALING 1000.0 +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); -XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, - "Logging specific to SURF (lagrange)"); -XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf, - "Logging specific to SURF (lagrange dichotomy)"); +double (*func_f_def) (lmm_variable_t, double); +double (*func_fp_def) (lmm_variable_t, double); +double (*func_fpi_def) (lmm_variable_t, double); /* * Local prototypes to implement the lagrangian optimization with optimal step, also called dichotomy. @@ -31,17 +35,15 @@ XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf, //solves the proportional fairness using a lagrange optimizition with dichotomy step void lagrange_solve(lmm_system_t sys); //computes the value of the dichotomy using a initial values, init, with a specific variable or constraint -double dichotomy(double init, double diff(double, void *), void *var_cnst, - double min_error); +static double dichotomy(double init, double diff(double, void *), + void *var_cnst, double min_error); //computes the value of the differential of variable param_var applied to mu -double partial_diff_mu(double mu, void *param_var); +static double partial_diff_mu(double mu, void *param_var); //computes the value of the differential of constraint param_cnst applied to lambda -double partial_diff_lambda(double lambda, void *param_cnst); -//auxiliar function to compute the partial_diff -double diff_aux(lmm_variable_t var, double x); - +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 int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, + int warn) { xbt_swag_t elem_list = NULL; lmm_element_t elem = NULL; @@ -56,38 +58,107 @@ static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) xbt_swag_foreach(elem, elem_list) { var = elem->variable; if (var->weight <= 0) - continue; + continue; tmp += var->value; } if (double_positive(tmp - cnst->bound)) { if (warn) - WARN3 - ("The link (%p) is over-used. Expected less than %f and got %f", - cnst, cnst->bound, tmp); + XBT_WARN + ("The link (%p) is over-used. Expected less than %f and got %f", + cnst, cnst->bound, tmp); return 0; } - DEBUG3("Checking feasability for constraint (%p): sat = %f, lambda = %f ", - cnst, tmp - cnst->bound, cnst->lambda); + XBT_DEBUG + ("Checking feasability for constraint (%p): sat = %f, lambda = %f ", + cnst, tmp - cnst->bound, cnst->lambda); } xbt_swag_foreach(var, var_list) { - if (var->bound < 0 || var->weight <= 0) + if (!var->weight) + break; + if (var->bound < 0) continue; - DEBUG3("Checking feasability for variable (%p): sat = %f mu = %f", var, - var->value - var->bound, var->mu); + XBT_DEBUG("Checking feasability for variable (%p): sat = %f mu = %f", var, + var->value - var->bound, var->mu); if (double_positive(var->value - var->bound)) { if (warn) - WARN3 - ("The variable (%p) is too large. Expected less than %f and got %f", - var, var->bound, var->value); + XBT_WARN + ("The variable (%p) is too large. Expected less than %f and got %f", + var, var->bound, var->value); return 0; } } return 1; } +static double new_value(lmm_variable_t var) +{ + double tmp = 0; + int i; + + for (i = 0; i < var->cnsts_number; i++) { + tmp += (var->cnsts[i].constraint)->lambda; + } + if (var->bound > 0) + tmp += var->mu; + XBT_DEBUG("\t Working on var (%p). cost = %e; Weight = %e", var, tmp, + var->weight); + //uses the partial differential inverse function + return var->func_fpi(var, tmp); +} + +static double new_mu(lmm_variable_t var) +{ + double mu_i = 0.0; + double sigma_i = 0.0; + int j; + + for (j = 0; j < var->cnsts_number; j++) { + sigma_i += (var->cnsts[j].constraint)->lambda; + } + mu_i = var->func_fp(var, var->bound) - sigma_i; + if (mu_i < 0.0) + return 0.0; + return mu_i; +} + +static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list) +{ + lmm_constraint_t cnst = NULL; + lmm_variable_t var = NULL; + + double obj = 0.0; + + xbt_swag_foreach(var, var_list) { + double sigma_i = 0.0; + int j; + + if (!var->weight) + break; + + for (j = 0; j < var->cnsts_number; j++) + sigma_i += (var->cnsts[j].constraint)->lambda; + + if (var->bound > 0) + sigma_i += var->mu; + + XBT_DEBUG("var %p : sigma_i = %1.20f", var, sigma_i); + + obj += var->func_f(var, var->func_fpi(var, sigma_i)) - + sigma_i * var->func_fpi(var, sigma_i); + + if (var->bound > 0) + obj += var->mu * var->bound; + } + + xbt_swag_foreach(cnst, cnst_list) + obj += cnst->lambda * cnst->bound; + + return obj; +} + void lagrange_solve(lmm_system_t sys) { /* @@ -95,8 +166,8 @@ void lagrange_solve(lmm_system_t sys) */ int max_iterations = 100; double epsilon_min_error = MAXMIN_PRECISION; - double dichotomy_min_error = 1e-20; - double overall_error = 1; + double dichotomy_min_error = 1e-14; + double overall_modification = 1; /* * Variables to manipulate the data structure proposed to model the maxmin @@ -114,18 +185,33 @@ void lagrange_solve(lmm_system_t sys) int iteration = 0; double tmp = 0; int i; + double obj, 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); - DEBUG0("Iterative method configuration snapshot =====>"); - DEBUG1("#### Maximum number of iterations : %d", max_iterations); - DEBUG1("#### Minimum error tolerated : %e", - epsilon_min_error); - DEBUG1("#### Minimum error tolerated (dichotomy) : %e", - dichotomy_min_error); + if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) { + lmm_print(sys); + } if (!(sys->modified)) return; + + /* + * Initialize lambda. + */ + cnst_list = &(sys->active_constraint_set); + xbt_swag_foreach(cnst, cnst_list) { + cnst->lambda = 1.0; + cnst->new_lambda = 2.0; + XBT_DEBUG("#### cnst(%p)->lambda : %e", cnst, cnst->lambda); + } + /* * Initialize the var list variable with only the active variables. * Associate an index in the swag variables. Initialize mu. @@ -133,128 +219,137 @@ void lagrange_solve(lmm_system_t sys) 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 (or inactive) variable", i); - var->mu = -1.0; - } else { - var->mu = 1.0; - var->new_mu = 2.0; + if (!var->weight) + var->value = 0.0; + else { + int nb = 0; + if (var->bound < 0.0) { + XBT_DEBUG("#### NOTE var(%d) is a boundless variable", i); + var->mu = -1.0; + var->value = new_value(var); + } else { + var->mu = 1.0; + var->new_mu = 2.0; + var->value = new_value(var); + } + XBT_DEBUG("#### var(%p) ->weight : %e", var, var->weight); + XBT_DEBUG("#### var(%p) ->mu : %e", var, var->mu); + XBT_DEBUG("#### var(%p) ->weight: %e", var, var->weight); + XBT_DEBUG("#### var(%p) ->bound: %e", var, var->bound); + for (i = 0; i < var->cnsts_number; i++) { + if (var->cnsts[i].value == 0.0) + nb++; + } + if (nb == var->cnsts_number) + var->value = 1.0; } - DEBUG3("#### var(%d) %p ->mu : %e", i, var, var->mu); - DEBUG3("#### var(%d) %p ->weight: %e", i, var, var->weight); - DEBUG3("#### var(%d) %p ->bound: %e", i, var, var->bound); - i++; } /* - * Initialize lambda. + * Compute dual objective. */ - cnst_list = &(sys->active_constraint_set); - xbt_swag_foreach(cnst, cnst_list) { - cnst->lambda = 1.0; - cnst->new_lambda = 2.0; - DEBUG2("#### cnst(%p)->lambda : %e", cnst, cnst->lambda); - } + obj = dual_objective(var_list, cnst_list); /* * While doesn't reach a minimun error or a number maximum of iterations. */ - while (overall_error > epsilon_min_error && iteration < max_iterations) { - int dual_updated=0; + while (overall_modification > epsilon_min_error + && iteration < max_iterations) { +/* int dual_updated=0; */ iteration++; - DEBUG1("************** ITERATION %d **************", iteration); - DEBUG0("-------------- Gradient Descent ----------"); + XBT_DEBUG("************** ITERATION %d **************", iteration); + XBT_DEBUG("-------------- Gradient Descent ----------"); + /* - * Compute the value of mu_i + * Improve the value of mu_i */ - //forall mu_i in mu_1, mu_2, ..., mu_n xbt_swag_foreach(var, var_list) { - if ((var->bound >= 0) && (var->weight > 0)) { - DEBUG1("Working on var (%p)", var); - var->new_mu = - dichotomy(var->mu, partial_diff_mu, var, dichotomy_min_error); - dual_updated += (fabs(var->new_mu-var->mu)>dichotomy_min_error); - DEBUG2("dual_updated (%d) : %1.20f",dual_updated,fabs(var->new_mu-var->mu)); - DEBUG3("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, var->mu, var->new_mu); - var->mu = var->new_mu; + if (!var->weight) + break; + if (var->bound >= 0) { + XBT_DEBUG("Working on var (%p)", var); + var->new_mu = new_mu(var); +/* dual_updated += (fabs(var->new_mu-var->mu)>dichotomy_min_error); */ +/* XBT_DEBUG("dual_updated (%d) : %1.20f",dual_updated,fabs(var->new_mu-var->mu)); */ + XBT_DEBUG("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, + var->mu, var->new_mu); + var->mu = var->new_mu; + + new_obj = dual_objective(var_list, cnst_list); + XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, + obj - new_obj); + xbt_assert1(obj - new_obj >= -epsilon_min_error, + "Our gradient sucks! (%1.20f)", obj - new_obj); + obj = new_obj; } } /* - * Compute the value of lambda_i + * Improve the value of lambda_i */ - //forall lambda_i in lambda_1, lambda_2, ..., lambda_n xbt_swag_foreach(cnst, cnst_list) { - DEBUG1("Working on cnst (%p)", cnst); + XBT_DEBUG("Working on cnst (%p)", cnst); cnst->new_lambda = - dichotomy(cnst->lambda, partial_diff_lambda, cnst, - dichotomy_min_error); - dual_updated += (fabs(cnst->new_lambda-cnst->lambda)>dichotomy_min_error); - DEBUG2("dual_updated (%d) : %1.20f",dual_updated,fabs(cnst->new_lambda-cnst->lambda)); - DEBUG3("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", cnst, cnst->lambda, cnst->new_lambda); + dichotomy(cnst->lambda, partial_diff_lambda, cnst, + dichotomy_min_error); +/* dual_updated += (fabs(cnst->new_lambda-cnst->lambda)>dichotomy_min_error); */ +/* XBT_DEBUG("dual_updated (%d) : %1.20f",dual_updated,fabs(cnst->new_lambda-cnst->lambda)); */ + XBT_DEBUG("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", + cnst, cnst->lambda, cnst->new_lambda); cnst->lambda = cnst->new_lambda; + + new_obj = dual_objective(var_list, cnst_list); + XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, + obj - new_obj); + xbt_assert1(obj - new_obj >= -epsilon_min_error, + "Our gradient sucks! (%1.20f)", obj - new_obj); + obj = new_obj; } /* * Now computes the values of each variable (\rho) based on * the values of \lambda and \mu. */ - DEBUG0("-------------- Check convergence ----------"); - overall_error = 0; + XBT_DEBUG("-------------- Check convergence ----------"); + overall_modification = 0; xbt_swag_foreach(var, var_list) { if (var->weight <= 0) - var->value = 0.0; + var->value = 0.0; else { - //compute sigma_i + mu_i - tmp = 0; - for (i = 0; i < var->cnsts_number; i++) { - tmp += (var->cnsts[i].constraint)->lambda; - } - if (var->bound > 0) - tmp += var->mu; - DEBUG3("\t Working on var (%p). cost = %e; Df = %e", var, tmp, - var->df); - - //uses the partial differential inverse function - tmp = var->func_fpi(var, tmp); - - //computes de overall_error using normalized value - if (overall_error < (fabs(var->value - tmp)/tmp)) { - overall_error = (fabs(var->value - tmp)/tmp); - } - - if (overall_error < (fabs(var->value - tmp))) { - overall_error = (fabs(var->value - tmp)); - } - - var->value = tmp; - DEBUG3("New value of var (%p) = %e, overall_error = %e", var, - var->value, overall_error); + tmp = new_value(var); + + overall_modification = + MAX(overall_modification, fabs(var->value - tmp)); + + var->value = tmp; + XBT_DEBUG("New value of var (%p) = %e, overall_modification = %e", + var, var->value, overall_modification); } } + XBT_DEBUG("-------------- Check feasability ----------"); if (!__check_feasible(cnst_list, var_list, 0)) - overall_error = 1.0; - DEBUG2("Iteration %d: Overall_error : %f", iteration, overall_error); - if(!dual_updated) { - DEBUG1("Could not improve the convergence at iteration %d. Drop it!",iteration); - break; - } + overall_modification = 1.0; + XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, + overall_modification); +/* if(!dual_updated) { */ +/* XBT_WARN("Could not improve the convergence at iteration %d. Drop it!",iteration); */ +/* break; */ +/* } */ } - __check_feasible(cnst_list, var_list, 1); - if (overall_error <= epsilon_min_error) { - DEBUG1("The method converges in %d iterations.", iteration); + if (overall_modification <= epsilon_min_error) { + XBT_DEBUG("The method converges in %d iterations.", iteration); } if (iteration >= max_iterations) { - DEBUG1 - ("Method reach %d iterations, which is the maximum number of iterations allowed.", - iteration); + XBT_DEBUG + ("Method reach %d iterations, which is the maximum number of iterations allowed.", + iteration); } -/* INFO1("Method converged after %d iterations", iteration); */ +/* XBT_INFO("Method converged after %d iterations", iteration); */ if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) { lmm_print(sys); @@ -273,8 +368,8 @@ void lagrange_solve(lmm_system_t sys) * * @return a double correponding to the result of the dichotomyal process */ -double dichotomy(double init, double diff(double, void *), void *var_cnst, - double min_error) +static double dichotomy(double init, double diff(double, void *), + void *var_cnst, double min_error) { double min, max; double overall_error; @@ -283,7 +378,7 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, double diff_0 = 0.0; min = max = init; - XBT_IN; + XBT_IN(""); if (init == 0.0) { min = max = 0.5; @@ -293,9 +388,8 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, overall_error = 1; if ((diff_0 = diff(1e-16, var_cnst)) >= 0) { - CDEBUG1(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", - diff_0); - XBT_OUT; + XBT_CDEBUG(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", diff_0); + XBT_OUT(); return 0.0; } @@ -303,132 +397,114 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, max_diff = diff(max, var_cnst); while (overall_error > min_error) { - CDEBUG4(surf_lagrange_dichotomy, - "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f", min, max, - min_diff,max_diff); + XBT_CDEBUG(surf_lagrange_dichotomy, + "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f", + min, max, min_diff, max_diff); if (min_diff > 0 && max_diff > 0) { if (min == max) { - CDEBUG0(surf_lagrange_dichotomy, "Decreasing min"); - min = min / 2.0; - min_diff = diff(min, var_cnst); + XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing min"); + min = min / 2.0; + min_diff = diff(min, var_cnst); } else { - CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); - max = min; - max_diff = min_diff; - + XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max"); + max = min; + max_diff = min_diff; } } else if (min_diff < 0 && max_diff < 0) { if (min == max) { - CDEBUG0(surf_lagrange_dichotomy, "Increasing max"); - max = max * 2.0; - max_diff = diff(max, var_cnst); + XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing max"); + max = max * 2.0; + max_diff = diff(max, var_cnst); } else { - CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); - min = max; - min_diff = max_diff; + XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing min"); + min = max; + min_diff = max_diff; } } else if (min_diff < 0 && max_diff > 0) { middle = (max + min) / 2.0; - CDEBUG1(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",middle); - - if((min==middle) || (max==middle)) { - DEBUG0("Cannot improve the convergence!"); - break; + XBT_CDEBUG(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f", + middle); + + if ((min == middle) || (max == middle)) { + XBT_CWARN(surf_lagrange_dichotomy, + "Cannot improve the convergence! min=max=middle=%1.20f, diff = %1.20f." + " Reaching the 'double' limits. Maybe scaling your function would help ([%1.20f,%1.20f]).", + min, max - min, min_diff, max_diff); + break; } middle_diff = diff(middle, var_cnst); if (middle_diff < 0) { - CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); - min = middle; - min_diff = middle_diff; + XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing min"); + min = middle; + overall_error = max_diff - middle_diff; + min_diff = middle_diff; +/* SHOW_EXPR(overall_error); */ } else if (middle_diff > 0) { - CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); - max = middle; - max_diff = middle_diff; + XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max"); + max = middle; + overall_error = max_diff - middle_diff; + max_diff = middle_diff; +/* SHOW_EXPR(overall_error); */ } else { - overall_error = 0; + overall_error = 0; +/* SHOW_EXPR(overall_error); */ } } else if (min_diff == 0) { - max=min; + max = min; overall_error = 0; +/* SHOW_EXPR(overall_error); */ } else if (max_diff == 0) { - min=max; + min = max; overall_error = 0; +/* SHOW_EXPR(overall_error); */ } else if (min_diff > 0 && max_diff < 0) { - CWARN0(surf_lagrange_dichotomy, - "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); + XBT_CWARN(surf_lagrange_dichotomy, + "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); abort(); } else { - CWARN2(surf_lagrange_dichotomy, - "diffmin (%1.20f) or diffmax (%1.20f) are something I don't know, taking no action.", - min_diff, max_diff); + XBT_CWARN(surf_lagrange_dichotomy, + "diffmin (%1.20f) or diffmax (%1.20f) are something I don't know, taking no action.", + min_diff, max_diff); abort(); } } - CDEBUG1(surf_lagrange_dichotomy, "returning %e", (min + max) / 2.0); - XBT_OUT; + XBT_CDEBUG(surf_lagrange_dichotomy, "returning %e", (min + max) / 2.0); + XBT_OUT(); return ((min + max) / 2.0); } -/* - * - */ -double partial_diff_mu(double mu, void *param_var) -{ - double mu_partial = 0.0; - double sigma_mu = 0.0; - lmm_variable_t var = (lmm_variable_t) param_var; - int i; - XBT_IN; - //compute sigma_i - for (i = 0; i < var->cnsts_number; i++) - sigma_mu += (var->cnsts[i].constraint)->lambda; - - //compute sigma_i + mu_i - sigma_mu += mu; - - //use auxiliar function passing (sigma_i + mu_i) - mu_partial = diff_aux(var, sigma_mu); - - //add the RTT limit - mu_partial += var->bound; - - XBT_OUT; - return mu_partial; -} - -/* - * - */ -double partial_diff_lambda(double lambda, void *param_cnst) +static double partial_diff_lambda(double lambda, void *param_cnst) { - int i; + int j; xbt_swag_t elem_list = NULL; lmm_element_t elem = NULL; lmm_variable_t var = NULL; lmm_constraint_t cnst = (lmm_constraint_t) param_cnst; - double lambda_partial = 0.0; + double diff = 0.0; double sigma_i = 0.0; - XBT_IN; + XBT_IN(""); elem_list = &(cnst->element_set); - CDEBUG1(surf_lagrange_dichotomy,"Computting diff of cnst (%p)", cnst); + XBT_CDEBUG(surf_lagrange_dichotomy, "Computing diff of cnst (%p)", cnst); xbt_swag_foreach(elem, elem_list) { var = elem->variable; if (var->weight <= 0) continue; - //initilize de sumation variable + XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)", + var); + // Initialize the summation variable sigma_i = 0.0; - //compute sigma_i of variable var - for (i = 0; i < var->cnsts_number; i++) { - sigma_i += (var->cnsts[i].constraint)->lambda; + // Compute sigma_i + for (j = 0; j < var->cnsts_number; j++) { + sigma_i += (var->cnsts[j].constraint)->lambda; } //add mu_i if this flow has a RTT constraint associated @@ -438,31 +514,42 @@ double partial_diff_lambda(double lambda, void *param_cnst) //replace value of cnst->lambda by the value of parameter lambda sigma_i = (sigma_i - cnst->lambda) + lambda; - //use the auxiliar function passing (\sigma_i + \mu_i) - lambda_partial += diff_aux(var, sigma_i); + diff += -var->func_fpi(var, sigma_i); } - lambda_partial += cnst->bound; + diff += cnst->bound; - XBT_OUT; - return lambda_partial; + XBT_CDEBUG(surf_lagrange_dichotomy, + "d D/d lambda for cnst (%p) at %1.20f = %1.20f", cnst, lambda, + diff); + XBT_OUT(); + return diff; } +/** \brief Attribute the value bound to var->bound. + * + * \param func_fpi inverse of the partial differential of f (f prime inverse, (f')^{-1}) + * + * Set default functions to the ones passed as parameters. This is a polimorfism in C pure, enjoy the roots of programming. + * + */ +void lmm_set_default_protocol_function(double (*func_f) -double diff_aux(lmm_variable_t var, double x) -{ - double tmp_fpi, result; - XBT_IN2("(var (%p), x (%1.20f))", var, x); - xbt_assert0(var->func_fpi, - "Initialize the protocol functions first create variables before."); - tmp_fpi = var->func_fpi(var, x); - result = - tmp_fpi; - XBT_OUT; - return result; + + + (lmm_variable_t var, double x), + double (*func_fp) (lmm_variable_t + var, double x), + double (*func_fpi) (lmm_variable_t + var, double x)) +{ + func_f_def = func_f; + func_fp_def = func_fp; + func_fpi_def = func_fpi; } @@ -473,25 +560,99 @@ double diff_aux(lmm_variable_t var, double x) */ /* - * For Vegas fpi: $\frac{\alpha D_f}{x}$ + * For Vegas: $f(x) = \alpha D_f\ln(x)$ + * Therefore: $fp(x) = \frac{\alpha D_f}{x}$ + * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$ */ -double func_vegas_fpi(lmm_variable_t var, double x){ - xbt_assert0(x>0.0,"Don't call me with stupid values!"); - return VEGAS_SCALING*var->df/x; +#define VEGAS_SCALING 1000.0 + +double func_vegas_f(lmm_variable_t var, double x) +{ + xbt_assert1(x > 0.0, "Don't call me with stupid values! (%1.20f)", x); + return VEGAS_SCALING * var->weight * log(x); +} + +double func_vegas_fp(lmm_variable_t var, double x) +{ + xbt_assert1(x > 0.0, "Don't call me with stupid values! (%1.20f)", x); + return VEGAS_SCALING * var->weight / x; +} + +double func_vegas_fpi(lmm_variable_t var, double x) +{ + xbt_assert1(x > 0.0, "Don't call me with stupid values! (%1.20f)", x); + return var->weight / (x / VEGAS_SCALING); } /* - * For Reno fpi: $\sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ + * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$ + * Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$ + * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ */ -double func_reno_fpi(lmm_variable_t var, double x){ - double res_fpi; +#define RENO_SCALING 1.0 +double func_reno_f(lmm_variable_t var, double x) +{ + xbt_assert0(var->weight > 0.0, "Don't call me with stupid values!"); + + return RENO_SCALING * sqrt(3.0 / 2.0) / var->weight * + atan(sqrt(3.0 / 2.0) * var->weight * x); +} + +double func_reno_fp(lmm_variable_t var, double x) +{ + return RENO_SCALING * 3.0 / (3.0 * var->weight * var->weight * x * x + + 2.0); +} + +double func_reno_fpi(lmm_variable_t var, double x) +{ + double res_fpi; - xbt_assert0(var->df>0.0,"Don't call me with stupid values!"); - xbt_assert0(x>0.0,"Don't call me with stupid values!"); + xbt_assert0(var->weight > 0.0, "Don't call me with stupid values!"); + xbt_assert0(x > 0.0, "Don't call me with stupid values!"); - res_fpi = 1/(var->df*var->df*x) - 2/(3*var->df*var->df); - if(res_fpi<=0.0) return 0.0; - xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); + res_fpi = + 1.0 / (var->weight * var->weight * (x / RENO_SCALING)) - + 2.0 / (3.0 * var->weight * var->weight); + if (res_fpi <= 0.0) + return 0.0; +/* xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); */ return sqrt(res_fpi); } + +/* Implementing new Reno-2 + * For Reno-2: $f(x) = U_f(x_f) = \frac{{2}{D_f}}*ln(2+x*D_f)$ + * Therefore: $fp(x) = 2/(Weight*x + 2) + * Therefore: $fpi(x) = (2*Weight)/x - 4 + */ +#define RENO2_SCALING 1.0 +double func_reno2_f(lmm_variable_t var, double x) +{ + xbt_assert0(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)); +} + +double func_reno2_fp(lmm_variable_t var, double x) +{ + return RENO2_SCALING * 3.0 / (var->weight * x * + (2.0 * var->weight * x + 3.0)); +} + +double func_reno2_fpi(lmm_variable_t var, double x) +{ + double res_fpi; + double tmp; + + xbt_assert0(x > 0.0, "Don't call me with stupid values!"); + tmp = x * var->weight * var->weight; + res_fpi = tmp * (9.0 * x + 24.0); + + if (res_fpi <= 0.0) + return 0.0; + + res_fpi = RENO2_SCALING * (-3.0 * tmp + sqrt(res_fpi)) / (4.0 * tmp); + return res_fpi; +}