/* $Id: retape_k1_l1.cpp 1705 2010-09-23 11:46:10Z bradbell $ */
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-10 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the 
                    Common Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

# include <cppad_ipopt_nlp.hpp>

namespace { // Begin empty namespace
// ---------------------------------------------------------------------------
class FG_retape : public cppad_ipopt_fg_info
{
public:
	// derived class part of constructor
	FG_retape(void)
	{ }
	// Evaluation of the objective f(x), and constraints g(x)
	// using an Algorithmic Differentiation (AD) class.
	ADVector eval_r(size_t k, const ADVector&  x)
	{	ADVector fg(2);

		// f(x)
		if( x[0] >= 1. )
			fg[0] = .5 * (x[0] * x[0] + x[1] * x[1]);
		else	fg[0] = x[0] + .5 * x[1] * x[1]; 
		// g (x)
		fg[1] = x[0];

		return fg;
	}
	bool retape(size_t k)
	{	return true; }
}; 
} // end of empty namespace

bool retape_k1_l1(void)
{	bool ok = true;
	size_t j;


	// number of independent variables (domain dimension for f and g)
	size_t n = 2;  
	// number of constraints (range dimension for g)
	size_t m = 1;
	// initial value of the independent variables
	NumberVector x_i(n);
	x_i[0] = 2.0;
	x_i[1] = 2.0;
	// lower and upper limits for x
	NumberVector x_l(n);
	NumberVector x_u(n);
	for(j = 0; j < n; j++)
	{	x_l[j] = -10.; x_u[j] = +10.;
	}
	// lower and upper limits for g
	NumberVector g_l(m);
	NumberVector g_u(m);
	g_l[0] = -1.;     g_u[0] = 1.0e19;

	// object in derived class
	FG_retape fg_retape;
	cppad_ipopt_fg_info *fg_info = &fg_retape;  

	// 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);

	// 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_x[]   = { -1., 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
		);
	}

	return ok;
}
