-/* 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"
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 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 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.
*/
/* 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
*/