// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-24 Bradley M. Bell
// ----------------------------------------------------------------------------

/*
{xrst_begin mul_level_ode.cpp}
{xrst_spell
   cccc
}

Taylor's Ode Solver: A Multi-Level AD Example and Test
######################################################

See Also
********
:ref:`taylor_ode.cpp-name` , :ref:`base2ad.cpp-name` , :ref:`mul_level_adolc_ode.cpp-name`

Purpose
*******
This is a realistic example using
two levels of AD; see :ref:`mul_level-name` .
The first level uses ``AD<double>`` to tape the solution of an
ordinary differential equation.
This solution is then differentiated with respect to a parameter vector.
The second level uses ``AD< AD<double> >``
to take derivatives during the solution of the differential equation.
These derivatives are used in the application
of Taylor's method to the solution of the ODE.

ODE
***
For this example the function
:math:`y : \B{R} \times \B{R}^n \rightarrow \B{R}^n` is defined by
:math:`y(0, x) = 0` and
:math:`\partial_t y(t, x) = g(y, x)` where
:math:`g : \B{R}^n \times \B{R}^n \rightarrow \B{R}^n` is defined by

.. math::

   g(y, x) =
   \left( \begin{array}{c}
         x_0     \\
         x_1 y_0 \\
         \vdots  \\
         x_{n-1} y_{n-2}
   \end{array} \right)

ODE Solution
************
The solution for this example can be calculated by
starting with the first row and then using the solution
for the first row to solve the second and so on.
Doing this we obtain

.. math::

   y(t, x ) =
   \left( \begin{array}{c}
      x_0 t                  \\
      x_1 x_0 t^2 / 2        \\
      \vdots                 \\
      x_{n-1} x_{n-2} \ldots x_0 t^n / n !
   \end{array} \right)

Derivative of ODE Solution
**************************
Differentiating the solution above,
with respect to the parameter vector :math:`x`,
we notice that

.. math::

   \partial_x y(t, x ) =
   \left( \begin{array}{cccc}
   y_0 (t,x) / x_0      & 0                   & \cdots & 0      \\
   y_1 (t,x) / x_0      & y_1 (t,x) / x_1     & 0      & \vdots \\
   \vdots               & \vdots              & \ddots & 0      \\
   y_{n-1} (t,x) / x_0  & y_{n-1} (t,x) / x_1 & \cdots & y_{n-1} (t,x) / x_{n-1}
   \end{array} \right)

Taylor's Method Using AD
************************
We define the function :math:`z(t, x)` by the equation

.. math::

   z ( t , x ) = g[ y ( t , x ) ] = h [ x , y( t , x ) ]

see :ref:`taylor_ode-name` for the method used to compute the
Taylor coefficients w.r.t :math:`t` of :math:`y(t, x)`.

Source
******
{xrst_literal
   // BEGIN C++
   // END C++
}

{xrst_end mul_level_ode.cpp}
--------------------------------------------------------------------------
*/
// BEGIN C++

# include <cppad/cppad.hpp>

// =========================================================================
// define types for each level
namespace { // BEGIN empty namespace

typedef CppAD::AD<double>          a1double;
typedef CppAD::AD<a1double>        a2double;

typedef CPPAD_TESTVECTOR(double)    d_vector;
typedef CPPAD_TESTVECTOR(a1double)  a1vector;
typedef CPPAD_TESTVECTOR(a2double)  a2vector;

// -------------------------------------------------------------------------
// class definition for C++ function object that defines ODE
class Ode {
private:
   // copy of a that is set by constructor and used by g(y)
   a1vector a1x_;
public:
   // constructor
   Ode(const a1vector& a1x) : a1x_(a1x)
   { }
   // the function g(y) is evaluated with two levels of taping
   a2vector operator()
   ( const a2vector& a2y) const
   {  size_t n = a2y.size();
      a2vector a2g(n);
      size_t i;
      a2g[0] = a1x_[0];
      for(i = 1; i < n; i++)
         a2g[i] = a1x_[i] * a2y[i-1];

      return a2g;
   }
};

// -------------------------------------------------------------------------
// Routine that uses Taylor's method to solve ordinary differential equaitons
// and allows for algorithmic differentiation of the solution.
a1vector taylor_ode(
   Ode                            G       ,  // function that defines the ODE
   size_t                         order   ,  // order of Taylor's method used
   size_t                         nstep   ,  // number of steps to take
   const a1double&                a1dt    ,  // Delta t for each step
   const a1vector& a1y_ini)  // y(t) at the initial time
{
   // some temporary indices
   size_t i, k, ell;

   // number of variables in the ODE
   size_t n = a1y_ini.size();

   // copies of x and g(y) with two levels of taping
   a2vector   a2y(n), a2z(n);

   // y, y^{(k)} , z^{(k)}, and y^{(k+1)}
   a1vector  a1y(n), a1y_k(n), a1z_k(n), a1y_kp(n);

   // initialize x
   for(i = 0; i < n; i++)
      a1y[i] = a1y_ini[i];

   // loop with respect to each step of Taylors method
   for(ell = 0; ell < nstep; ell++)
   {  // prepare to compute derivatives using a1double
      for(i = 0; i < n; i++)
         a2y[i] = a1y[i];
      CppAD::Independent(a2y);

      // evaluate ODE in a2double
      a2z = G(a2y);

      // define differentiable version of a1g: y -> z
      // that computes its derivatives using a1double objects
      CppAD::ADFun<a1double> a1g(a2y, a2z);

      // Use Taylor's method to take a step
      a1y_k            = a1y;     // initialize y^{(k)}
      a1double a1dt_kp = a1dt;  // initialize dt^(k+1)
      for(k = 0; k <= order; k++)
      {  // evaluate k-th order Taylor coefficient of y
         a1z_k = a1g.Forward(k, a1y_k);

         for(i = 0; i < n; i++)
         {  // convert to (k+1)-Taylor coefficient for x
            a1y_kp[i] = a1z_k[i] / a1double(k + 1);

            // add term for to this Taylor coefficient
            // to solution for y(t, x)
            a1y[i]    += a1y_kp[i] * a1dt_kp;
         }
         // next power of t
         a1dt_kp *= a1dt;
         // next Taylor coefficient
         a1y_k   = a1y_kp;
      }
   }
   return a1y;
}
} // END empty namespace
// ==========================================================================
// Routine that tests algorithmic differentiation of solutions computed
// by the routine taylor_ode.
bool mul_level_ode(void)
{  bool ok = true;
   double eps = 100. * std::numeric_limits<double>::epsilon();

   // number of components in differential equation
   size_t n = 4;

   // some temporary indices
   size_t i, j;

   // parameter vector in both double and a1double
   d_vector  x(n);
   a1vector  a1x(n);
   for(i = 0; i < n; i++)
      a1x[i] = x[i] = double(i + 1);

   // declare the parameters as the independent variable
   CppAD::Independent(a1x);

   // arguments to taylor_ode
   Ode G(a1x);                // function that defines the ODE
   size_t   order = n;      // order of Taylor's method used
   size_t   nstep = 2;      // number of steps to take
   a1double a1dt  = double(1.);     // Delta t for each step
   // value of y(t, x) at the initial time
   a1vector a1y_ini(n);
   for(i = 0; i < n; i++)
      a1y_ini[i] = 0.;

   // integrate the differential equation
   a1vector a1y_final(n);
   a1y_final = taylor_ode(G, order, nstep, a1dt, a1y_ini);

   // define differentiable function object f : x -> y_final
   // that computes its derivatives in double
   CppAD::ADFun<double> f(a1x, a1y_final);

   // check function values
   double check = 1.;
   double t     = double(nstep) * Value(a1dt);
   for(i = 0; i < n; i++)
   {  check *= x[i] * t / double(i + 1);
      ok &= CppAD::NearEqual(Value(a1y_final[i]), check, eps, eps);
   }

   // evaluate the Jacobian of h at a
   d_vector jac ( f.Jacobian(x) );
   // There appears to be a bug in g++ version 4.4.2 because it generates
   // a warning for the equivalent form
   // d_vector jac = f.Jacobian(x);

   // check Jacobian
   for(i = 0; i < n; i++)
   {  for(j = 0; j < n; j++)
      {  double jac_ij = jac[i * n + j];
         if( i < j )
            check = 0.;
         else
            check = Value( a1y_final[i] ) / x[j];
         ok &= CppAD::NearEqual(jac_ij, check, eps, eps);
      }
   }
   return ok;
}

// END C++
