\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)

code_gen_fun_jacobian.cpp

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Evaluate Jacobian of a Code Gen Function: Example and Test

# include <cppad/example/code_gen_fun.hpp>

bool jacobian(void)
{  bool ok = true;
   //
   typedef CppAD::cg::CG<double>     c_double;
   typedef CppAD::AD<c_double>      ac_double;
   //
   typedef CppAD::vector<double>     d_vector;
   typedef CppAD::vector<ac_double> ac_vector;
   //
   double eps99 = 99.0 * std::numeric_limits<double>::epsilon();

   // domain space vector
   size_t n  = 2;
   ac_vector ac_x(n);
   for(size_t j = 0; j < n; ++j)
      ac_x[j] = 1.0 / double(j + 1);

   // declare independent variables and start tape recording
   CppAD::Independent(ac_x);

   // range space vector
   size_t m = 3;
   ac_vector ac_y(m);
   for(size_t i = 0; i < m; ++i)
      ac_y[i] = double(i + 1) * sin( ac_x[i % n] );

   // create c_f: x -> y and stop tape recording
   CppAD::ADFun<c_double> c_f(ac_x, ac_y);

   // create compiled version of c_f
   std::string file_name = "example_lib";
   code_gen_fun::evaluation_enum eval_jac = code_gen_fun::dense_enum;
   code_gen_fun f(file_name, c_f, eval_jac);

   // evaluate the compiled jacobian
   d_vector x(n), J;
   for(size_t j = 0; j < n; ++j)
      x[j] = 1.0 / double(j + 2);
   J = f.jacobian(x);

   // check Jaociban values
   for(size_t i = 0; i < m; ++i)
   {  for(size_t j = 0; j < n; ++j)
      {  double check = 0.0;
         if( j == i % n )
            check = double(i + 1) * cos( x[i % n] );
         ok &= CppAD::NearEqual(J[i * n + j] , check, eps99, eps99);
      }
   }
   return ok;
}