-/* 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. */
* "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"
/* Improve the value of lambda_i */
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;
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 : variable_set) {
/*
* 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;
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,
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;
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;
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");
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})
+ * @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 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))
+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))
{
- Lagrange::func_f = func_f;
- Lagrange::func_fp = func_fp;
- Lagrange::func_fpi = func_fpi;
+ func_f = f;
+ func_fp = fp;
+ func_fpi = fpi;
}
double (*Lagrange::func_f)(const Variable&, double);
/* 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)
{
}
/*
- * 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)
{
}
/* 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
*/
