Prev Next ode_taylor_adolc.cpp

Using Adolc with Taylor's Ode Solver: An Example and Test

Purpose
This is a realistic example using two levels of taping (see mul_level ). The first level of taping uses Adolc's adouble type to tape the solution of an ordinary differential equation. This solution is then differentiated with respect to a parameter vector. The second level of taping uses CppAD's type AD<adouble> 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. The example ode_taylor.cpp computes the same values using AD<double> and AD< AD<double> >.

ODE
For this example the ODE's are defined by the function  h : \R^n \times \R^n \rightarrow \R^n where  \[
     h[ x, y(t, x) ] = 
     \left( \begin{array}{c}
               x_0                     \\
               x_1 y_0 (t, x)          \\
               \vdots                  \\
               x_{n-1} y_{n-2} (t, x)
     \end{array} \right)
     = 
     \left( \begin{array}{c}
               \partial_t y_0 (t , x)      \\
               \partial_t y_1 (t , x)      \\
               \vdots                      \\
               \partial_t y_{n-1} (t , x) 
     \end{array} \right)
\] 
and the initial condition  y(0, x) = 0 . The value of  x is fixed during the solution of the ODE and the function  g : \R^n \rightarrow \R^n is used to define the ODE where  \[
     g(y) = 
     \left( \begin{array}{c}
               x_0     \\
               x_1 y_0 \\
               \vdots  \\
               x_{n-1} y_{n-2} 
     \end{array} \right)
\] 


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


Derivative of ODE Solution
Differentiating the solution above, with respect to the parameter vector  x , we notice that  \[
\partial_x y(t, x ) =
\left( \begin{array}{cccc}
y_0 (t,x) / x_0      & 0                   & \cdots & 0      \\
y_1 (t,x) / x_0      & y_1 (t,x) / x_1     & 0      & \vdots \\
\vdots               & \vdots              & \ddots & 0      \\
y_{n-1} (t,x) / x_0  & y_{n-1} (t,x) / x_1 & \cdots & y_{n-1} (t,x) / x_{n-1}
\end{array} \right)
\] 



An m-th order Taylor method for approximating the solution of an ordinary differential equations is  \[
     y(t + \Delta t , x) 
     \approx 
     \sum_{k=0}^p \partial_t^k y(t , x ) \frac{ \Delta t^k }{ k ! }
     =
     y^{(0)} (t , x ) + 
     y^{(1)} (t , x ) \Delta t + \cdots + 
     y^{(p)} (t , x ) \Delta t^p
\] 
where the Taylor coefficients  y^{(k)} (t, x) are defined by  \[
     y^{(k)} (t, x) = \partial_t^k y(t , x ) / k !
\] 
We define the function  z(t, x) by the equation  \[
     z ( t , x ) = g[ y ( t , x ) ] = h [ x , y( t , x ) ]
\] 
It follows that  \[
\begin{array}{rcl}
     \partial_t y(t, x) & = & z (t , x) 
     \\
      \partial_t^{k+1} y(t , x) & = & \partial_t^k z (t , x)
     \\
     y^{(k+1)} ( t , x) & = & z^{(k)} (t, x) / (k+1) 
\end{array}
\] 
where   z^{(k)} (t, x) is the k-th order Taylor coefficient for  z(t, x) . In the example below, the Taylor coefficients  \[
     y^{(0)} (t , x) , \ldots , y^{(k)} ( t , x )
\] 
are used to calculate the Taylor coefficient  z^{(k)} ( t , x ) which in turn gives the value for   y^{(k+1)} y ( t , x) .

base_adolc.hpp
The file base_adolc.hpp is implements the Base type requirements where Base is adolc.

Memory Management
Adolc uses raw memory arrays that depend on the number of dependent and independent variables. The memory management utility omp_alloc is used to manage this memory allocation.

Configuration Requirement
This example will be compiled and tested provided that the value AdolcDir is specified on the configure command line.
 
# include <adolc/adouble.h>
# include <adolc/taping.h>
# include <adolc/drivers/drivers.h>

// definitions not in Adolc distribution and required to use CppAD::AD<adouble>
# include "base_adolc.hpp"

# include <cppad/cppad.hpp>
// ==========================================================================
namespace { // BEGIN empty namespace
// define types for each level
typedef adouble            ADdouble;
typedef CppAD::AD<adouble> ADDdouble;

// -------------------------------------------------------------------------
// 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)
	CPPAD_TEST_VECTOR< ADdouble > x_; 
public:
	// constructor
	Ode( CPPAD_TEST_VECTOR< ADdouble > x) : x_(x)
	{ }
	// the function g(y) is evaluated with two levels of taping
	CPPAD_TEST_VECTOR< ADDdouble > operator()
	( const CPPAD_TEST_VECTOR< ADDdouble > &y) const
	{	size_t n = y.size();
		CPPAD_TEST_VECTOR< ADDdouble > g(n);
		size_t i;
		g[0] = x_[0];
		for(i = 1; i < n; i++)
			g[i] = x_[i] * y[i-1];

		return g;
	}
};

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

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

	// copies of x and g(y) with two levels of taping
	CPPAD_TEST_VECTOR< ADDdouble >   Y(n), Z(n);

	// y, y^{(k)} , z^{(k)}, and y^{(k+1)}
	CPPAD_TEST_VECTOR< ADdouble >  y(n), y_k(n), z_k(n), y_kp(n);
	
	// initialize x
	for(i = 0; i < n; i++)
		y[i] = y_ini[i];

	// loop with respect to each step of Taylors method
	for(ell = 0; ell < nstep; ell++)
	{	// prepare to compute derivatives of in ADdouble
		for(i = 0; i < n; i++)
			Y[i] = y[i];
		CppAD::Independent(Y);

		// evaluate ODE in ADDdouble
		Z = G(Y);

		// define differentiable version of g: X -> Y
		// that computes its derivatives in ADdouble
		CppAD::ADFun<ADdouble> g(Y, Z);

		// Use Taylor's method to take a step
		y_k            = y;     // initialize y^{(k)}
		ADdouble dt_kp = dt;    // initialize dt^(k+1)
		for(k = 0; k <= order; k++)
		{	// evaluate k-th order Taylor coefficient of y
			z_k = g.Forward(k, y_k);
 
			for(i = 0; i < n; i++)
			{	// convert to (k+1)-Taylor coefficient for x
				y_kp[i] = z_k[i] / ADdouble(k + 1);

				// add term for to this Taylor coefficient
				// to solution for y(t, x)
				y[i]    += y_kp[i] * dt_kp;
			}
			// next power of t
			dt_kp *= dt;
			// next Taylor coefficient
			y_k   = y_kp;
		}
	}
	return y;
}
} // END empty namespace
// ==========================================================================
// Routine that tests algorithmic differentiation of solutions computed
// by the routine taylor_ode.
bool ode_taylor_adolc(void)
{	// initialize the return value as true	
	bool ok = true;

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

	// some temporary indices
	size_t i, j;

	// set up for omp_alloc memory allocator
	using CppAD::omp_alloc; // the allocator
	size_t capacity;        // capacity of an allocation

	// the vector x with lenght n (or greater) in double 
	double* x = omp_alloc::create_array<double>(n, capacity);
	// the vector x with lenght n in ADouble
	CPPAD_TEST_VECTOR<ADdouble> X(n);
	for(i = 0; i < n; i++)
		X[i] = x[i] = double(i + 1);

	// declare the parameters as the independent variable
	int tag = 0;                     // Adolc setup
	int keep = 1;
	trace_on(tag, keep);
	for(i = 0; i < n; i++)
		X[i] <<= double(i + 1);  // X is independent for adouble type

	// arguments to taylor_ode_adolc 
	Ode G(X);                // function that defines the ODE
	size_t   order = n;      // order of Taylor's method used
	size_t   nstep = 2;      // number of steps to take
	ADdouble DT    = 1.;     // Delta t for each step
	// value of y(t, x) at the initial time
	CPPAD_TEST_VECTOR< ADdouble > Y_INI(n);
	for(i = 0; i < n; i++)
		Y_INI[i] = 0.;

	// integrate the differential equation
	CPPAD_TEST_VECTOR< ADdouble > Y_FINAL(n);
 	Y_FINAL = taylor_ode_adolc(G, order, nstep, DT, Y_INI);

	// declare the differentiable fucntion f : A -> Y_FINAL
	// (corresponding to the tape of adouble operations)
	double* y_final = omp_alloc::create_array<double>(n, capacity);
	for(i = 0; i < n; i++)
		Y_FINAL[i] >>= y_final[i];
	trace_off();

	// check function values
	double check = 1.;
	double t     = nstep * DT.value();
	for(i = 0; i < n; i++)
	{	check *= x[i] * t / double(i + 1);
		ok &= CppAD::NearEqual(y_final[i], check, 1e-10, 1e-10);
	}

	// memory where Jacobian will be returned
	double* jac_ = omp_alloc::create_array<double>(n * n, capacity); 
	double** jac = omp_alloc::create_array<double*>(n, capacity);
	for(i = 0; i < n; i++)
		jac[i] = jac_ + i * n;

	// evaluate Jacobian of h at a
	size_t m = n;              // # dependent variables
	jacobian(tag, int(m), int(n), x, jac); 
	
	// check Jacobian 
	for(i = 0; i < n; i++)
	{	for(j = 0; j < n; j++)
		{	if( i < j )
				check = 0.;
			else	check = y_final[i] / x[j];
			ok &= CppAD::NearEqual(jac[i][j], check, 1e-10, 1e-10);
		}
	}

	// make memroy avaiable for other use by this thread
	omp_alloc::delete_array(x);
	omp_alloc::delete_array(y_final);
	omp_alloc::delete_array(jac_);
	omp_alloc::delete_array(jac);
	return ok;
}


Input File: example/ode_taylor_adolc.cpp