$\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }$
# include <cppad/speed/ode_evaluate.hpp> # include <cppad/speed/uniform_01.hpp> # include <cppad/cppad.hpp> bool ode_evaluate(void) { using CppAD::NearEqual; using CppAD::AD; bool ok = true; size_t n = 3; CppAD::vector<double> x(n); CppAD::vector<double> ym(n * n); CppAD::vector< AD<double> > X(n); CppAD::vector< AD<double> > Ym(n); // choose x size_t j; for(j = 0; j < n; j++) { x[j] = double(j + 1); X[j] = x[j]; } // declare independent variables Independent(X); // evaluate function size_t m = 0; CppAD::ode_evaluate(X, m, Ym); // evaluate derivative m = 1; CppAD::ode_evaluate(x, m, ym); // use AD to evaluate derivative CppAD::ADFun<double> F(X, Ym); CppAD::vector<double> dy(n * n); dy = F.Jacobian(x); size_t k; for(k = 0; k < n * n; k++) ok &= NearEqual(ym[k], dy[k] , 1e-7, 1e-7); return ok; }