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// Copyright (C) 2013 by Thomas Moulard, AIST, CNRS.
//
// This file is part of the roboptim.
//
// roboptim 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.
//
// roboptim 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 roboptim. If not, see <http://www.gnu.org/licenses/>.
#include "common.hh"
namespace roboptim
{
namespace schittkowski
{
namespace problem1
{
struct ExpectedResult
{
static const double f0;
static const double x[];
static const double fx;
};
const double ExpectedResult::f0 = 909.;
const double ExpectedResult::x[] = {1., 1.};
const double ExpectedResult::fx = 0.;
template <typename T>
class F : public GenericDifferentiableFunction<T>
{
public:
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS
(GenericDifferentiableFunction<T>);
explicit F () throw ();
void
impl_compute (result_t& result, const argument_t& x) const throw ();
void
impl_gradient (gradient_t& grad, const argument_t& x, size_type)
const throw ();
};
template <typename T>
F<T>::F () throw ()
: GenericDifferentiableFunction<T>
(2, 1, "100 (x₁ - x₀²)² + (1 - x₀)²")
{}
template <typename T>
void
F<T>::impl_compute (result_t& result, const argument_t& x)
const throw ()
{
result[0] = 100 * std::pow (x[1] - std::pow (x[0], 2), 2)
+ std::pow (1 - x[0], 2);
}
template <>
void
F<EigenMatrixSparse>::impl_gradient
(gradient_t& grad, const argument_t& x, size_type)
const throw ()
{
grad.insert (0) =
-400 * x[0] * (x[1] - std::pow (x[0], 2)) - 2 * (1 - x[0]);
grad.insert (1) = 200 * (x[1] - std::pow (x[0], 2));
}
template <typename T>
void
F<T>::impl_gradient (gradient_t& grad, const argument_t& x, size_type)
const throw ()
{
grad[0] = -400 * x[0] * (x[1] - std::pow (x[0], 2)) - 2 * (1 - x[0]);
grad[1] = 200 * (x[1] - std::pow (x[0], 2));
}
} // end of namespace problem1.
} // end of namespace schittkowski.
} // end of namespace roboptim.
BOOST_FIXTURE_TEST_SUITE (schittkowski, TestSuiteConfiguration)
BOOST_AUTO_TEST_CASE (schittkowski_problem1)
{
using namespace roboptim;
using namespace roboptim::schittkowski::problem1;
// Build problem.
F<functionType_t> f;
solver_t::problem_t problem (f);
problem.argumentBounds ()[1] = F<functionType_t>::makeLowerInterval (-1.5);
F<functionType_t>::argument_t x (2);
x << -2., 1.;
problem.startingPoint () = x;
BOOST_CHECK_CLOSE (f (x)[0], ExpectedResult::f0, 1e-6);
std::cout << f.inputSize () << std::endl;
std::cout << problem.function ().inputSize () << std::endl;
// Initialize solver.
SolverFactory<solver_t> factory (SOLVER_NAME, problem);
solver_t& solver = factory ();
std::cout << f.inputSize () << std::endl;
std::cout << problem.function ().inputSize () << std::endl;
// Compute the minimum and retrieve the result.
solver_t::result_t res = solver.minimum ();
std::cout << f.inputSize () << std::endl;
std::cout << problem.function ().inputSize () << std::endl;
// Display solver information.
std::cout << solver << std::endl;
// Check if the minimization has succeed.
if (res.which () != solver_t::SOLVER_VALUE)
{
std::cout << "A solution should have been found. Failing..."
<< std::endl
<< boost::get<SolverError> (res).what ()
<< std::endl;
BOOST_CHECK_EQUAL (res.which (), solver_t::SOLVER_VALUE);
return;
}
// Get the result.
Result& result = boost::get<Result> (res);
// Check final x.
for (unsigned i = 0; i < result.x.size (); ++i)
BOOST_CHECK_CLOSE (result.x[i], ExpectedResult::x[i], 1e-6);
// Check final value.
BOOST_CHECK_CLOSE (1. + result.value[0], 1. + ExpectedResult::fx, 1e-6);
// Display the result.
std::cout << "A solution has been found: " << std::endl
<< result << std::endl;
}
BOOST_AUTO_TEST_SUITE_END ()
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