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rAKA/optimization/fn_optimize.cc
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fn_optimize.cc
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/**
* @file fn_optimize.cc
*
* @author Alejandro M. Aragón <alejandro.aragon@epfl.ch>
*
* @date Thu May 22 14:12:00 2014
*
* @brief File used to show how to use the NLopt optimizator to find the
* minimum of a function
*
* @section LICENSE
*
* Copyright (©) 2010-2011 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*
*/
#include <vector>
#include <math.h>
#include "aka_optimize.hh"
typedef struct {
double a, b;
} my_constraint_data;
//! Functor used for the evaluation of the function and its gradient
class Functor {
int count_; //!< Function evaluation counter
public:
//! Default constructor
Functor() : count_() {}
//! Return function evaluation counter
int count() const
{ return count_; }
double operator()(const std::vector<double> &x, std::vector<double> &grad)
{
++count_;
if (!grad.empty()) {
grad[0] = 0.0;
grad[1] = 0.5 / sqrt(x[1]);
}
return sqrt(x[1]);
}
static double wrap(const std::vector<double> &x, std::vector<double> &grad, void *data) {
return (*reinterpret_cast<Functor*>(data))(x, grad); }
};
double myvconstraint(const std::vector<double> &x, std::vector<double> &grad, void *data)
{
my_constraint_data *d = reinterpret_cast<my_constraint_data*>(data);
double a = d->a, b = d->b;
if (!grad.empty()) {
grad[0] = 3 * a * (a*x[0] + b) * (a*x[0] + b);
grad[1] = -1.0;
}
return ((a*x[0] + b) * (a*x[0] + b) * (a*x[0] + b) - x[1]);
}
int main(int argc, char *argv[]) {
my_constraint_data data[2] = { {2,0}, {-1,1} };
std::vector<double> x(2);
x[0] = 1.234; x[1] = 5.678;
Functor fn;
akantu::Optimizator ofn(x, fn);
ofn.add_inequality_constraint(myvconstraint, &data[0], 1e-8);
ofn.add_inequality_constraint(myvconstraint, &data[1], 1e-8);
ofn.result();
std::cout<<"\nTotal function evaluations: "<<fn.count()<<std::endl;
return 0;
}
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