/* $Id: ode_fast.hpp 1771 2011-01-01 15:41:51Z bradbell $ */
# ifndef CPPAD_ODE_FAST_INCLUDED
# define CPPAD_ODE_FAST_INCLUDED
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-11 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.
-------------------------------------------------------------------------- */
/*
$begin ipopt_ode_fast.hpp$$
$spell
	cppad_ipopt_nlp
$$

$section ODE Fitting Using Fast Representation$$

$index cppad_ipopt_nlp, ode example source$$
$index ode, cppad_ipopt_nlp example source$$
$index example, cppad_ipopt_nlp ode source$$
$index source, cppad_ipopt_nlp ode example$$

$code
$verbatim%cppad_ipopt/example/ode_fast.hpp%0%// BEGIN PROGRAM%// END PROGRAM%1%$$
$$

$end
*/

// BEGIN PROGRAM
# include "ode_problem.hpp"

namespace {
	class FG_fast : public cppad_ipopt_fg_info
	{
	private:
		bool       retape_;
		SizeVector N_;
		SizeVector S_;
	public:
		// derived class part of constructor
		FG_fast(bool retape_in, const SizeVector& N)
		: retape_ (retape_in), N_(N)
		{	assert( N_[0] == 0 );
			S_.resize( N_.size() );
			S_[0] = 0;
			for(size_t i = 1; i < N_.size(); i++)
				S_[i] = S_[i-1] + N_[i];
		}
		// r^k for k = 0, 1, ..., Nz-1 used for measurements
		// r^k for k = Nz              use for initial condition
		// r^k for k = Nz+1, ..., 2*Nz used for trapezoidal approx
		size_t number_functions(void)
		{	return Nz + 1 + Nz; }
		ADVector eval_r(size_t k, const ADVector &u)
		{	count_eval_r();

			size_t j;
			ADVector y(Ny), a(Na);
			// objective function --------------------------------
			if( k < Nz )
			{	// used for measurement with index k+1
				ADVector r(1); // return value is a scalar
				// u is [y( s[k+1] ) , a] 
				for(j = 0; j < Ny; j++)
					y[j] = u[j];
				for(j = 0; j < Na; j++)
					a[j] = u[Ny + j];
				r[0] = eval_H<ADNumber>(k+1, y, a);
				return r;
			}
			// initial condition ---------------------------------
			if( k == Nz )
			{	ADVector r(Ny), F(Ny);
				// u is [y(t), a] at t = 0 
				for(j = 0; j < Ny; j++)
					y[j] = u[j];
				for(j = 0; j < Na; j++)
					a[j] = u[Ny + j];
				F    = eval_F(a);
				for(j = 0; j < Ny; j++)
					r[j]   = y[j] - F[j]; 
				return  r;
			}
			// trapezoidal approximation -------------------------
			ADVector ym(Ny), G(Ny), Gm(Ny), r(Ny);
			// r^k for k = Nz+1, ... , 2*Nz 
			// interval between data samples
			Number T = s[k-Nz] - s[k-Nz-1];
			// integration step size
			Number dt = T / Number( N_[k-Nz] );
			// u = [ y(t[i-1], a) , y(t[i], a), a ) 
			for(j = 0; j < Ny; j++)
			{	ym[j] = u[j];
				y[j]  = u[Ny + j];
			}
			for(j = 0; j < Na; j++)
				a[j] = u[2 * Ny + j];
			Gm  = eval_G(ym, a);
			G   = eval_G(y,  a);
			for(j = 0; j < Ny; j++)
				r[j] = y[j] - ym[j] - (G[j] + Gm[j]) * dt / 2.;
			return r;
		}
		// The operations sequence for r_eval does not depend on u,
		// hence retape = false should work and be faster.
		bool retape(size_t k)
		{	return retape_; }
		// size of the vector u in eval_r
		size_t domain_size(size_t k)
		{	if( k < Nz )
				return Ny + Na;   // objective function
			if( k == Nz )
				return Ny + Na;  // initial value constraint
			return 2 * Ny + Na;      // trapezodial constraints
		}
		// size of the return value from eval_r
		size_t range_size(size_t k)
		{	if( k < Nz )
				return 1;
			return Ny; 
		}
		// number of terms that use this value of k
		size_t number_terms(size_t k)
		{	if( k <= Nz )
				return 1;  // r^k used once for k <= Nz
			// r^k used N_[k-Nz] times for k > Nz
			return N_[k-Nz];
		}
		void index(size_t k, size_t ell, SizeVector& I, SizeVector& J)
		{	size_t i, j;
			// # of components of x corresponding to values for y
			size_t ny_inx = (S_[Nz] + 1) * Ny;
			// objective function -------------------------------
			if( k < Nz )
			{	// index in fg corresponding to objective	
				I[0] = 0;
				// u = [ y(t, a) , a ]
				// The first Ny components of u is y(t) at 
				// 	t = s[k+1] = t[S_[k+1]]
				// x indices corresponding to this value of y
				for(j = 0; j < Ny; j++)
					J[j] = S_[k + 1] * Ny + j;
				// components of x correspondig to a
				for(j = 0; j < Na; j++)
					J[Ny + j] = ny_inx + j; 
				return;
			}
			// initial conditions --------------------------------
			if( k == Nz )
			{	// index in fg for inidial condition constraint
				for(j = 0; j < Ny; j++)
					I[j] = 1 + j;
				// u = [ y(t, a) , a ] where t = 0
				// x indices corresponding to this value of y
				for(j = 0; j < Ny; j++)
					J[j] = j;
				// following that, u contains the vector a 
				for(j = 0; j < Na; j++)
					J[Ny + j] = ny_inx + j;
				return;
			}
			// trapoziodal approximation -------------------------
			// index of first grid point in this approximation
			i = S_[k - Nz - 1]  + ell;
			// There are Ny difference equations for each time 
			// point.  Add one for the objective function, and Ny
			// for the initial value constraints.
			for(j = 0; j < Ny; j++)
				I[j] = 1 + Ny + i * Ny + j;
			// u = [ y(t, a) , y(t+dt, a) , a ] at t = t[i]
			for(j = 0; j < Ny; j++)
			{	J[j]      = i * Ny  + j; // y^i indices
				J[Ny + j] = J[j] + Ny;   // y^{i+1} indices
			}
			for(j = 0; j < Na; j++)
				J[2 * Ny + j] = ny_inx + j; // a indices
		} 
	};

}
// END PROGRAM
# endif

