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// Copyright (C) 2009 International Business Machines.
// All Rights Reserved.
// This code is published under the Eclipse Public License.
//
// $Id$
//
// Author: Andreas Waechter IBM 2009-04-02
// This file is part of the Ipopt tutorial. It is a correct version
// of a C++ implementation of the coding exercise problem (in AMPL
// formulation):
//
// param n := 4;
//
// var x {1..n} <= 0, >= -1.5, := -0.5;
//
// minimize obj:
// sum{i in 1..n} (x[i]-1)^2;
//
// subject to constr {i in 2..n-1}:
// (x[i]^2+1.5*x[i]-i/n)*cos(x[i+1]) - x[i-1] = 0;
//
// The constant term "i/n" in the constraint is supposed to be input data
//
#include <cstdio>
#include "IpIpoptApplication.hpp"
#include "TutorialCpp_nlp.hpp"
using namespace Ipopt;
int main(
int /*argc*/,
char** /*argv*/
)
{
// Set the data:
// Number of variables
Index N = 100;
// constant terms in the constraints
Number* a = new double[N - 2];
for( Index i = 0; i < N - 2; i++ )
{
a[i] = (double(i + 2)) / (double) N;
}
// Create a new instance of your nlp
// (use a SmartPtr, not raw)
SmartPtr<TNLP> mynlp = new TutorialCpp_NLP(N, a);
// Create a new instance of IpoptApplication
// (use a SmartPtr, not raw)
SmartPtr<IpoptApplication> app = new IpoptApplication();
// Change some options
// Note: The following choices are only examples, they might not be
// suitable for your optimization problem.
app->Options()->SetNumericValue("tol", 1e-10);
app->Options()->SetStringValue("mu_strategy", "adaptive");
// Intialize the IpoptApplication and process the options
app->Initialize();
// Ask Ipopt to solve the problem
ApplicationReturnStatus status = app->OptimizeTNLP(mynlp);
if( status == Solve_Succeeded )
{
printf("\n\n*** The problem solved!\n");
}
else
{
printf("\n\n*** The problem FAILED!\n");
}
// As the SmartPtrs go out of scope, the reference count
// will be decremented and the objects will automatically
// be deleted.
// However, we created the Number array for a here and have to delete it
delete[] a;
return (int) status;
}
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