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sanitize the OOP of kernel::profile
[simgrid.git]
/
src
/
kernel
/
lmm
/
lagrange.cpp
diff --git
a/src/kernel/lmm/lagrange.cpp
b/src/kernel/lmm/lagrange.cpp
index
8d6cb06
..
755da62
100644
(file)
--- a/
src/kernel/lmm/lagrange.cpp
+++ b/
src/kernel/lmm/lagrange.cpp
@@
-1,4
+1,4
@@
-/* Copyright (c) 2007-201
8
. The SimGrid Team. All rights reserved. */
+/* Copyright (c) 2007-201
9
. 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. */
/* 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"
* "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 "xbt/log.h"
#include "xbt/sysdep.h"
@@
-216,7
+217,7
@@
void Lagrange::lagrange_solve()
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 : variable_set) {
XBT_DEBUG("-------------- Check convergence ----------");
overall_modification = 0;
for (Variable& var : variable_set) {
@@
-254,10
+255,10
@@
void Lagrange::lagrange_solve()
/*
* Returns a double value corresponding to the result of a dichotomy process with respect to a given
/*
* 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
*
* @param var_cnst a pointer to a variable or constraint
* @param min_erro a minimum error tolerated
*
@@
-400,9
+401,11
@@
double Lagrange::partial_diff_lambda(double lambda, const Constraint& cnst)
return diff;
}
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.
*/
*
* Set default functions to the ones passed as parameters.
*/
@@
-423,9
+426,9
@@
double (*Lagrange::func_fpi)(const Variable&, double);
/* NOTE for Reno: all functions consider the network coefficient (alpha) equal to 1. */
/*
/* 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)
{
*/
double func_vegas_f(const Variable& var, double x)
{
@@
-446,9
+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)
{
*/
double func_reno_f(const Variable& var, double x)
{
@@
-477,7
+480,7
@@
double func_reno_fpi(const Variable& var, double x)
}
/* Implementing new Reno-2
}
/* 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
*/
* Therefore: $fp(x) = 2/(Weight*x + 2)
* Therefore: $fpi(x) = (2*Weight)/x - 4
*/