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Checkpointing an ODE Solver: Example and Test

See Also
checkpoint_extended_ode.cpp ,

Purpose
In this example we checkpoint one step of an ODE solver.

Problem
We consider the initial value problem with parameter $x$ defined by, $z(0, x) = z_0 (x)$, $$\partial_t z(t, x ) = h [ x , z(t, x) ]$$ Note that if $t$ needs to be in the equation, one can define the first component of $z(t, x)$ to be equal to $t$.

ODE Solver
For this example, we consider the Fourth order Runge-Kutta ODE solver. Given an approximation solution at time $t_k$ denoted by $\tilde{z}_k (x)$, and $\Delta t = t_{k+1} - t_k$, it defines the approximation solution $\tilde{z}_{k+1} (x)$ at time $t_{k+1}$ by $$\begin{array}{rcl} h_1 & = & h [ x , \tilde{z}_k (x) ] \\ h_2 & = & h [ x , \tilde{z}_k (x) + \Delta t \; h_1 / 2 ] \\ h_3 & = & h [ x , \tilde{z}_k (x) + \Delta t \; h_2 / 2 ] \\ h_4 & = & h [ x , \tilde{z}_k (x) + \Delta t \; h_3 ] \\ \tilde{z}_{k+1} (x) & = & \tilde{z}_k (x) + \Delta t \; ( h_1 + 2 h_2 + 2 h_3 + h_4 ) / 6 \end{array}$$ If $\tilde{z}_k (x) = z_k (x)$, $\tilde{z}_{k+1} (x) = z_{k+1} (x) + O( \Delta t^5 )$. Other ODE solvers can use a similar method to the one used below.

ODE
For this example the ODE is defined by $z(0, x) = 0$ and $$h[ x, z(t, x) ] = \left( \begin{array}{c} x_0 \\ x_1 z_0 (t, x) \\ \vdots \\ x_{n-1} z_{n-2} (t, x) \end{array} \right) = \left( \begin{array}{c} \partial_t z_0 (t , x) \\ \partial_t z_1 (t , x) \\ \vdots \\ \partial_t z_{n-1} (t , x) \end{array} \right)$$

Solution
The solution of the ODE for this example, which is used to check the results, 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 $$z(t, x) = \left( \begin{array}{c} x_0 t \\ x_1 x_0 t^2 / 2 \\ \vdots \\ x_{n-1} x_{n-2} \ldots x_0 t^n / n ! \end{array} \right)$$  # include <cppad/cppad.hpp> namespace { using CppAD::AD; typedef AD<double> a1double; typedef AD<a1double> a2double; // typedef CPPAD_TESTVECTOR( double ) a0vector; typedef CPPAD_TESTVECTOR( a1double ) a1vector; typedef CPPAD_TESTVECTOR( a2double ) a2vector; // // set once by main and kept that way double delta_t_ = std::numeric_limits<double>::quiet_NaN(); size_t n_ = 0; // // The function h( x , y) template <class FloatVector> FloatVector h(const FloatVector& x, const FloatVector& y) { assert( size_t( x.size() ) == n_ ); assert( size_t( y.size() ) == n_ ); FloatVector result(n_); result[0] = x[0]; for(size_t i = 1; i < n_; i++) result[i] = x[i] * y[i-1]; return result; } // The 4-th Order Runge-Kutta Step template <class FloatVector> FloatVector Runge4(const FloatVector& x, const FloatVector& z0 ) { assert( size_t( x.size() ) == n_ ); assert( size_t( z0.size() ) == n_ ); // typedef typename FloatVector::value_type Float; // Float dt = Float(delta_t_); size_t m = z0.size(); // FloatVector h1(m), h2(m), h3(m), h4(m), result(m); h1 = h( x, z0 ); // for(size_t i = 0; i < m; i++) h2[i] = z0[i] + dt * h1[i] / 2.0; h2 = h( x, h2 ); // for(size_t i = 0; i < m; i++) h3[i] = z0[i] + dt * h2[i] / 2.0; h3 = h( x, h3 ); // for(size_t i = 0; i < m; i++) h4[i] = z0[i] + dt * h3[i]; h4 = h( x, h4 ); // for(size_t i = 0; i < m; i++) { Float dz = dt * ( h1[i] + 2.0*h2[i] + 2.0*h3[i] + h4[i] ) / 6.0; result[i] = z0[i] + dz; } return result; } // pack x and z into an ode_info vector template <class FloatVector> void pack( FloatVector& ode_info , const FloatVector& x , const FloatVector& z ) { assert( size_t( ode_info.size() ) == n_ + n_ ); assert( size_t( x.size() ) == n_ ); assert( size_t( z.size() ) == n_ ); // size_t offset = 0; for(size_t i = 0; i < n_; i++) ode_info[offset + i] = x[i]; offset += n_; for(size_t i = 0; i < n_; i++) ode_info[offset + i] = z[i]; } // unpack an ode_info vector template <class FloatVector> void unpack( const FloatVector& ode_info , FloatVector& x , FloatVector& z ) { assert( size_t( ode_info.size() ) == n_ + n_ ); assert( size_t( x.size() ) == n_ ); assert( size_t( z.size() ) == n_ ); // size_t offset = 0; for(size_t i = 0; i < n_; i++) x[i] = ode_info[offset + i]; offset += n_; for(size_t i = 0; i < n_; i++) z[i] = ode_info[offset + i]; } // Algorithm that z(t, x) void ode_algo(const a1vector& ode_info_in, a1vector& ode_info_out) { assert( size_t( ode_info_in.size() ) == n_ + n_ ); assert( size_t( ode_info_out.size() ) == n_ + n_ ); // // initial ode information a1vector x(n_), z0(n_); unpack(ode_info_in, x, z0); // // advance z(t, x) a1vector z1 = Runge4(x, z0); // // final ode information pack(ode_info_out, x, z1); // return; } } // bool ode(void) { bool ok = true; using CppAD::NearEqual; double eps = std::numeric_limits<double>::epsilon(); // // number of terms in the differential equation n_ = 6; // // step size for the differentiail equation size_t n_step = 10; double T = 1.0; delta_t_ = T / double(n_step); // // set parameter value and initial value of the ode a1vector ax(n_), az0(n_); for(size_t i = 0; i < n_; i++) { ax[i] = a1double(i + 1); az0[i] = a1double(0); } // // pack ode information input vector a1vector ode_info_in(2 * n_); pack(ode_info_in, ax, az0); // // create checkpoint version of the algorithm a1vector ode_info_out(2 * n_); CppAD::checkpoint<double> ode_check( "ode", ode_algo, ode_info_in, ode_info_out ); // // set the independent variables for recording CppAD::Independent( ax ); // // repack to get dependence on ax pack(ode_info_in, ax, az0); // // Now run the checkpoint algorithm n_step times for(size_t k = 0; k < n_step; k++) { ode_check(ode_info_in, ode_info_out); ode_info_in = ode_info_out; } // // Unpack the results (must use ax1 so do not overwrite ax) a1vector ax1(n_), az1(n_); unpack(ode_info_out, ax1, az1); // // We could record a complicated funciton of x and z(T, x) in f, // but make this example simpler we record x -> z(T, x). CppAD::ADFun<double> f(ax, az1); // // check function values a0vector x(n_), z1(n_); for(size_t j = 0; j < n_; j++) x[j] = double(j + 1); z1 = f.Forward(0, x); // // separate calculation of z(t, x) a0vector check_z1(n_); check_z1[0] = x[0] * T; for(size_t i = 1; i < n_; i++) check_z1[i] = x[i] * T * check_z1[i-1] / double(i+1); // // expected accuracy for each component of of z(t, x) a0vector acc(n_); for(size_t i = 0; i < n_; i++) { if( i < 4 ) { // Runge-Kutta methos is exact for this case acc[i] = 10. * eps; } else { acc[i] = 1.0; for(size_t k = 0; k < 5; k++) acc[i] *= x[k] * delta_t_; } } // check z1(T, x) for(size_t i = 0; i < n_; i++) ok &= NearEqual(z1[i] , check_z1[i], acc[i], acc[i]); // // Now use f to compute a derivative. For this 'simple' example it is // the derivative of z_{n-1} (T, x) respect to x of the a0vector w(n_), dw(n_); for(size_t i = 0; i < n_; i++) { w[i] = 0.0; if( i == n_ - 1 ) w[i] = 1.0; } dw = f.Reverse(1, w); for(size_t j = 0; j < n_; j++) { double check = z1[n_ - 1] / x[j]; ok &= NearEqual(dw[j] , check, 100.*eps, 100.*eps); } // return ok; } 
Input File: example/atomic/ode.cpp