X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/a3a3d31eb820dc24865879894bf9635cd21e1c08..b830103bb89748d30c84ff7a0e88ca821d2d78b7:/src/kernel/lmm/lagrange.cpp diff --git a/src/kernel/lmm/lagrange.cpp b/src/kernel/lmm/lagrange.cpp index db92f1e454..fdab111a34 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. */ @@ -8,6 +8,7 @@ * "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" @@ -26,10 +27,6 @@ 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); @@ -80,7 +77,7 @@ double Lagrange::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); } double Lagrange::new_mu(const Variable& var) @@ -91,7 +88,7 @@ double Lagrange::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; @@ -115,7 +112,7 @@ double Lagrange::dual_objective() 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; @@ -146,22 +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 { @@ -195,7 +190,7 @@ 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); @@ -210,9 +205,9 @@ 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); + cnst.new_lambda = dichotomy(cnst.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; @@ -222,10 +217,10 @@ void Lagrange::lagrange_solve() 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 { @@ -260,17 +255,16 @@ 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 */ -double Lagrange::dichotomy(double init, double diff(double, const Constraint&), const Constraint& cnst, - double min_error) +double Lagrange::dichotomy(double init, const Constraint& cnst, double min_error) { double min = init; double max = init; @@ -288,15 +282,15 @@ double Lagrange::dichotomy(double init, double diff(double, const Constraint&), overall_error = 1; - diff_0 = diff(1e-16, cnst); + diff_0 = partial_diff_lambda(1e-16, cnst); if (diff_0 >= 0) { XBT_CDEBUG(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", diff_0); XBT_OUT(); return 0.0; } - double min_diff = diff(min, cnst); - double max_diff = diff(max, cnst); + double min_diff = partial_diff_lambda(min, cnst); + double max_diff = partial_diff_lambda(max, cnst); while (overall_error > min_error) { XBT_CDEBUG(surf_lagrange_dichotomy, "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f", min, max, @@ -306,7 +300,7 @@ double Lagrange::dichotomy(double init, double diff(double, const Constraint&), if (min == max) { XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing min"); min = min / 2.0; - min_diff = diff(min, cnst); + min_diff = partial_diff_lambda(min, cnst); } else { XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max"); max = min; @@ -316,7 +310,7 @@ double Lagrange::dichotomy(double init, double diff(double, const Constraint&), if (min == max) { XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing max"); max = max * 2.0; - max_diff = diff(max, cnst); + max_diff = partial_diff_lambda(max, cnst); } else { XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing min"); min = max; @@ -333,7 +327,7 @@ double Lagrange::dichotomy(double init, double diff(double, const Constraint&), min, max - min, min_diff, max_diff); break; } - middle_diff = diff(middle, cnst); + middle_diff = partial_diff_lambda(middle, cnst); if (middle_diff < 0) { XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing min"); @@ -396,7 +390,7 @@ double Lagrange::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; @@ -406,30 +400,34 @@ double Lagrange::partial_diff_lambda(double lambda, const Constraint& cnst) 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}) +/** @brief Attribute the value bound to var->bound. * - * Set default functions to the ones passed as parameters. This is a polymorphism in C pure, enjoy the roots of - * programming. + * @param f function (f) + * @param fp partial differential of f (f prime, (f')) + * @param 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 (*f)(const Variable& var, double x), + double (*fp)(const Variable& var, double x), + double (*fpi)(const Variable& var, double x)) { - func_f_def = func_f; - func_fp_def = func_fp; - func_fpi_def = func_fpi; + func_f = f; + func_fp = fp; + func_fpi = 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) { @@ -450,9 +448,9 @@ 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) { @@ -481,7 +479,7 @@ 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 */