X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/39d24b63aa597b0ee886e0f98b2755eb27924b3e..2d37e348a09783cda723c7019640ee69de168324:/src/kernel/lmm/lagrange.cpp diff --git a/src/kernel/lmm/lagrange.cpp b/src/kernel/lmm/lagrange.cpp index bebf316e4c..755da62076 100644 --- a/src/kernel/lmm/lagrange.cpp +++ b/src/kernel/lmm/lagrange.cpp @@ -1,4 +1,4 @@ -/* Copyright (c) 2007-2018. The SimGrid Team. All rights reserved. */ +/* Copyright (c) 2007-2019. 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. */ @@ -8,48 +8,33 @@ * "ssh://username@scm.gforge.inria.fr/svn/memo/people/pvelho/lagrange/ppf.ps". */ #include "src/kernel/lmm/maxmin.hpp" +#include "src/surf/surf_interface.hpp" #include "xbt/log.h" #include "xbt/sysdep.h" #include -#include -#ifndef MATH #include -#endif +#include 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); -#define VEGAS_SCALING 1000.0 -#define RENO_SCALING 1.0 -#define RENO2_SCALING 1.0 +static constexpr double VEGAS_SCALING = 1000.0; +static constexpr double RENO_SCALING = 1.0; +static constexpr double RENO2_SCALING = 1.0; namespace simgrid { namespace kernel { namespace lmm { -double (*func_f_def)(const Variable&, double); -double (*func_fp_def)(const Variable&, double); -double (*func_fpi_def)(const Variable&, double); - System* make_new_lagrange_system(bool selective_update) { return new Lagrange(selective_update); } -/* - * Local prototypes to implement the Lagrangian optimization with optimal step, also called dichotomy. - */ -// 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, const Constraint&), const Constraint& cnst, double min_error); -// computes the value of the differential of constraint cnst applied to lambda -static double partial_diff_lambda(double lambda, const Constraint& cnst); - -template -static int __check_feasible(const CnstList& cnst_list, const VarList& var_list, int warn) +bool Lagrange::check_feasible(bool warn) { - for (Constraint const& cnst : cnst_list) { + for (Constraint const& cnst : active_constraint_set) { double tmp = 0; for (Element const& elem : cnst.enabled_element_set) { Variable* var = elem.variable; @@ -60,12 +45,12 @@ static int __check_feasible(const CnstList& cnst_list, const VarList& var_list, if (double_positive(tmp - cnst.bound, sg_maxmin_precision)) { if (warn) XBT_WARN("The link (%p) is over-used. Expected less than %f and got %f", &cnst, cnst.bound, tmp); - return 0; + return false; } XBT_DEBUG("Checking feasability for constraint (%p): sat = %f, lambda = %f ", &cnst, tmp - cnst.bound, cnst.lambda); } - for (Variable const& var : var_list) { + for (Variable const& var : variable_set) { if (not var.sharing_weight) break; if (var.bound < 0) @@ -75,13 +60,13 @@ static int __check_feasible(const CnstList& cnst_list, const VarList& var_list, if (double_positive(var.value - var.bound, sg_maxmin_precision)) { if (warn) XBT_WARN("The variable (%p) is too large. Expected less than %f and got %f", &var, var.bound, var.value); - return 0; + return false; } } - return 1; + return true; } -static double new_value(const Variable& var) +double Lagrange::new_value(const Variable& var) { double tmp = 0; @@ -92,10 +77,10 @@ static double new_value(const Variable& var) tmp += var.mu; 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); + return func_fpi(var, tmp); } -static double new_mu(const Variable& var) +double Lagrange::new_mu(const Variable& var) { double mu_i = 0.0; double sigma_i = 0.0; @@ -103,18 +88,17 @@ static double new_mu(const Variable& var) for (Element const& elem : var.cnsts) { sigma_i += elem.constraint->lambda; } - mu_i = var.func_fp(var, var.bound) - sigma_i; + mu_i = func_fp(var, var.bound) - sigma_i; if (mu_i < 0.0) return 0.0; return mu_i; } -template -static double dual_objective(const VarList& var_list, const CnstList& cnst_list) +double Lagrange::dual_objective() { double obj = 0.0; - for (Variable const& var : var_list) { + for (Variable const& var : variable_set) { double sigma_i = 0.0; if (not var.sharing_weight) @@ -128,13 +112,13 @@ static double dual_objective(const VarList& var_list, const CnstList& cnst_list) 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); + obj += func_f(var, func_fpi(var, sigma_i)) - sigma_i * func_fpi(var, sigma_i); if (var.bound > 0) obj += var.mu * var.bound; } - for (Constraint const& cnst : cnst_list) + for (Constraint const& cnst : active_constraint_set) obj += cnst.lambda * cnst.bound; return obj; @@ -159,22 +143,20 @@ void Lagrange::lagrange_solve() print(); } - if (not modified) + if (not modified_) return; /* Initialize lambda. */ - auto& cnst_list = active_constraint_set; - for (Constraint& cnst : cnst_list) { + for (Constraint& cnst : active_constraint_set) { 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. Initialize mu. + * Initialize the active variables. Initialize mu. */ - auto& var_list = variable_set; - for (Variable& var : var_list) { + for (Variable& var : variable_set) { if (not var.sharing_weight) var.value = 0.0; else { @@ -198,7 +180,7 @@ void Lagrange::lagrange_solve() } /* Compute dual objective. */ - double obj = dual_objective(var_list, cnst_list); + double obj = dual_objective(); /* While doesn't reach a minimum error or a number maximum of iterations. */ int iteration = 0; @@ -208,14 +190,14 @@ void Lagrange::lagrange_solve() XBT_DEBUG("-------------- Gradient Descent ----------"); /* Improve the value of mu_i */ - for (Variable& var : var_list) { + for (Variable& var : variable_set) { if (var.sharing_weight && var.bound >= 0) { 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; - double new_obj = dual_objective(var_list, cnst_list); + double new_obj = dual_objective(); 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; @@ -223,22 +205,22 @@ void Lagrange::lagrange_solve() } /* Improve the value of lambda_i */ - for (Constraint& cnst : cnst_list) { + for (Constraint& cnst : active_constraint_set) { 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; - double new_obj = dual_objective(var_list, cnst_list); + double new_obj = dual_objective(); 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; } - /* Now computes the values of each variable (\rho) based on the values of \lambda and \mu. */ + /* Now computes the values of each variable (@rho) based on the values of @lambda and @mu. */ XBT_DEBUG("-------------- Check convergence ----------"); overall_modification = 0; - for (Variable& var : var_list) { + for (Variable& var : variable_set) { if (var.sharing_weight <= 0) var.value = 0.0; else { @@ -252,12 +234,12 @@ void Lagrange::lagrange_solve() } XBT_DEBUG("-------------- Check feasability ----------"); - if (not __check_feasible(cnst_list, var_list, 0)) + if (not check_feasible(false)) overall_modification = 1.0; XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification); } - __check_feasible(cnst_list, var_list, 1); + check_feasible(true); if (overall_modification <= epsilon_min_error) { XBT_DEBUG("The method converges in %d iterations.", iteration); @@ -273,16 +255,17 @@ void Lagrange::lagrange_solve() /* * Returns a double value corresponding to the result of a dichotomy process with respect to a given - * variable/constraint (\mu in the case of a variable or \lambda in case of a constraint) and a initial value init. + * variable/constraint (@mu in the case of a variable or @lambda in case of a constraint) and a initial value init. * - * @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 var_cnst a pointer to a variable or constraint * @param min_erro a minimum error tolerated * * @return a double corresponding to the result of the dichotomy process */ -static double dichotomy(double init, double diff(double, const Constraint&), const Constraint& cnst, double min_error) +double Lagrange::dichotomy(double init, double diff(double, const Constraint&), const Constraint& cnst, + double min_error) { double min = init; double max = init; @@ -382,7 +365,7 @@ static double dichotomy(double init, double diff(double, const Constraint&), con return ((min + max) / 2.0); } -static double partial_diff_lambda(double lambda, const Constraint& cnst) +double Lagrange::partial_diff_lambda(double lambda, const Constraint& cnst) { double diff = 0.0; @@ -408,7 +391,7 @@ static double partial_diff_lambda(double lambda, const Constraint& cnst) // replace value of cnst.lambda by the value of parameter lambda sigma_i = (sigma_i - cnst.lambda) + lambda; - diff += -var.func_fpi(var, sigma_i); + diff += -func_fpi(var, sigma_i); } diff += cnst.bound; @@ -418,30 +401,34 @@ static double partial_diff_lambda(double lambda, const Constraint& cnst) return diff; } -/** \brief Attribute the value bound to var->bound. +/** @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 polymorphism in C pure, enjoy the roots of - * programming. + * @param func_f function (f) + * @param func_fp partial differential of f (f prime, (f')) + * @param func_fpi inverse of the partial differential of f (f prime inverse, (f')^{-1}) * + * Set default functions to the ones passed as parameters. */ -void set_default_protocol_function(double (*func_f)(const Variable& var, double x), - double (*func_fp)(const Variable& var, double x), - double (*func_fpi)(const Variable& var, double x)) +void Lagrange::set_default_protocol_function(double (*func_f)(const Variable& var, double x), + double (*func_fp)(const Variable& var, double x), + double (*func_fpi)(const Variable& var, double x)) { - func_f_def = func_f; - func_fp_def = func_fp; - func_fpi_def = func_fpi; + Lagrange::func_f = func_f; + Lagrange::func_fp = func_fp; + Lagrange::func_fpi = func_fpi; } +double (*Lagrange::func_f)(const Variable&, double); +double (*Lagrange::func_fp)(const Variable&, double); +double (*Lagrange::func_fpi)(const Variable&, double); + /**************** Vegas and Reno functions *************************/ /* NOTE for Reno: all functions consider the network coefficient (alpha) equal to 1. */ /* - * For Vegas: $f(x) = \alpha D_f\ln(x)$ - * Therefore: $fp(x) = \frac{\alpha D_f}{x}$ - * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$ + * 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_f(const Variable& var, double x) { @@ -462,9 +449,9 @@ double func_vegas_fpi(const Variable& var, double x) } /* - * 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: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ + * 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_f(const Variable& var, double x) { @@ -493,7 +480,7 @@ double func_reno_fpi(const Variable& var, double x) } /* Implementing new Reno-2 - * For Reno-2: $f(x) = U_f(x_f) = \frac{{2}{D_f}}*ln(2+x*D_f)$ + * 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 */