# include <cppad/cppad.hpp>
# include <cppad/example/cppad_eigen.hpp>
# include <cppad/speed/det_by_minor.hpp>
# include <Eigen/Dense>

bool eigen_array(void)
{     bool ok = true;
     using CppAD::AD;
     using CppAD::NearEqual;
     using Eigen::Matrix;
     using Eigen::Dynamic;
     //
     typedef Matrix< AD<double> , Dynamic, 1 > a_vector;
     //
     // some temporary indices
     size_t i, j;

     // domain and range space vectors
     size_t n  = 10, m = n;
     a_vector a_x(n), a_y(m);

     // set and declare independent variables and start tape recording
     for(j = 0; j < n; j++)
          a_x[j] = double(1 + j);
     CppAD::Independent(a_x);

     // evaluate a component wise function
     a_y = a_x.array() + a_x.array().sin();

     // create f: x -> y and stop tape recording
     CppAD::ADFun<double> f(a_x, a_y);

     // compute the derivative of y w.r.t x using CppAD
     CPPAD_TESTVECTOR(double) x(n);
     for(j = 0; j < n; j++)
          x[j] = double(j) + 1.0 / double(j+1);
     CPPAD_TESTVECTOR(double) jac = f.Jacobian(x);

     // check Jacobian
     double eps = 100. * CppAD::numeric_limits<double>::epsilon();
     for(i = 0; i < m; i++)
     {     for(j = 0; j < n; j++)
          {     double check = 1.0 + cos(x[i]);
               if( i != j )
                    check = 0.0;
               ok &= NearEqual(jac[i * n + j], check, eps, eps);
          }
     }

     return ok;
}