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// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-22 Bradley M. Bell
// ----------------------------------------------------------------------------
# include <cppad_ipopt_nlp.hpp>
namespace { // Begin empty namespace
using namespace cppad_ipopt;
// ---------------------------------------------------------------------------
/*
This solve the same problem as
../cppad_ipopt/cppad_ipopt_simple.cpp (repository revision
1276) in a convoluted way in order to test the representation code.
*/
class FG_K_gt_one : public cppad_ipopt_fg_info
{
private:
bool retape_;
public:
// derived class part of constructor
FG_K_gt_one(bool retape_in)
: retape_ (retape_in)
{ }
// Evaluation of the objective f(x), and constraints g(x)
// using an Algorithmic Differentiation (AD) class.
ADVector eval_r(size_t k, const ADVector& u)
{
// Fortran style indexing
ADNumber x1 = u[3];
ADNumber x2 = u[2];
ADNumber x3 = u[1];
ADNumber x4 = u[0];
if( k == 0 )
{ ADVector r(1);
// f(x)
r[0] = x1 * x4 * (x1 + x2 + x3) + x3;
return r;
}
ADVector r(2);
// g_1 (x)
r[0] = x1 * x2 * x3 * x4;
// g_2 (x)
r[1] = x1 * x1 + x2 * x2 + x3 * x3 + x4 * x4;
return r;
}
bool retape(size_t k)
{ return retape_; }
size_t number_functions(void)
{ return 2; }
size_t domain_size(size_t k)
{ return 4; }
size_t range_size(size_t k)
{ if( k == 0 )
return 1;
return 2;
}
size_t number_terms(size_t k)
{ return 1; }
void index(size_t k, size_t ell, SizeVector& I, SizeVector& J)
{
if( k == 0 )
I[0] = 0;
else
{ I[0] = 1;
I[1] = 2;
}
// reverse the order of the variables in u from that in x
for(size_t j = 0; j < 4; j++)
J[j] = 3-j;
}
};
} // end empty namespace
bool k_gt_one(void)
{ bool ok = true;
size_t j;
// number of independent variables (domain dimension for f and g)
size_t n = 4;
// number of constraints (range dimension for g)
size_t m = 2;
// initial value of the independent variables
NumberVector x_i(n);
x_i[0] = 1.0;
x_i[1] = 5.0;
x_i[2] = 5.0;
x_i[3] = 1.0;
// lower and upper limits for x
NumberVector x_l(n);
NumberVector x_u(n);
for(j = 0; j < n; j++)
{ x_l[j] = 1.0;
x_u[j] = 5.0;
}
// lower and upper limits for g
NumberVector g_l(m);
NumberVector g_u(m);
g_l[0] = 25.0; g_u[0] = 1.0e19;
g_l[1] = 40.0; g_u[1] = 40.0;
// known solution to check against
double check_x[] = { 1.000000, 4.743000, 3.82115, 1.379408 };
size_t icase;
for(icase = 0; icase <= 1; icase++)
{ // Should cppad_ipopt_nlp retape the operation sequence for
// every new x. Can test both true and false cases because
// the operation sequence does not depend on x (for this case).
bool retape = bool(icase);
// check case where upper and lower limits are equal
if( icase == 1 )
{ x_l[2] = check_x[2];
x_u[2] = check_x[2];
}
// object in derived class
FG_K_gt_one my_fg_info(retape);
cppad_ipopt_fg_info *fg_info = &my_fg_info;
// create the Ipopt interface
cppad_ipopt_solution solution;
Ipopt::SmartPtr<Ipopt::TNLP> cppad_nlp = new cppad_ipopt_nlp(
n, m, x_i, x_l, x_u, g_l, g_u, fg_info, &solution
);
// Create an instance of the IpoptApplication
using Ipopt::IpoptApplication;
Ipopt::SmartPtr<IpoptApplication> app = new IpoptApplication();
// turn off any printing
app->Options()->SetIntegerValue("print_level", 0);
app->Options()->SetStringValue("sb", "yes");
// maximum number of iterations
app->Options()->SetIntegerValue("max_iter", 10);
// approximate accuracy in first order necessary conditions;
// see Mathematical Programming, Volume 106, Number 1,
// Pages 25-57, Equation (6)
app->Options()->SetNumericValue("tol", 1e-9);
// derivative testing
app->Options()->
SetStringValue("derivative_test", "second-order");
// Initialize the IpoptApplication and process the options
Ipopt::ApplicationReturnStatus status = app->Initialize();
ok &= status == Ipopt::Solve_Succeeded;
// Run the IpoptApplication
status = app->OptimizeTNLP(cppad_nlp);
ok &= status == Ipopt::Solve_Succeeded;
/*
Check some of the solution values
*/
ok &= solution.status == cppad_ipopt_solution::success;
//
double check_z_l[] = { 1.087871, 0., 0., 0. };
double check_z_u[] = { 0., 0., 0., 0. };
double rel_tol = 1e-6; // relative tolerance
double abs_tol = 1e-6; // absolute tolerance
for(j = 0; j < n; j++)
{ ok &= CppAD::NearEqual(
check_x[j], solution.x[j], rel_tol, abs_tol
);
ok &= CppAD::NearEqual(
check_z_l[j], solution.z_l[j], rel_tol, abs_tol
);
ok &= CppAD::NearEqual(
check_z_u[j], solution.z_u[j], rel_tol, abs_tol
);
}
}
return ok;
}
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