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@(@\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}} }@)@
Example Optimization and Reverse Activity Analysis
# include <cppad/cppad.hpp>
namespace {
     struct tape_size { size_t n_var; size_t n_op; };

     template <class Vector> void fun(
          const Vector& x, Vector& y, tape_size& before, tape_size& after
     )
     {     typedef typename Vector::value_type scalar;

          // phantom variable with index 0 and independent variables
          // begin operator, independent variable operators and end operator
          before.n_var = 1 + x.size(); before.n_op  = 2 + x.size();
          after.n_var  = 1 + x.size(); after.n_op   = 2 + x.size();

          // initilized product of even and odd variables
          scalar prod_even = x[0];
          scalar prod_odd  = x[1];
          before.n_var += 0; before.n_op  += 0;
          after.n_var  += 0; after.n_op   += 0;
          //
          // compute product of even and odd variables
          for(size_t i = 2; i < size_t( x.size() ); i++)
          {     if( i % 2 == 0 )
               {     // prod_even will affect dependent variable
                    prod_even = prod_even * x[i];
                    before.n_var += 1; before.n_op += 1;
                    after.n_var  += 1; after.n_op  += 1;
               }
               else
               {     // prod_odd will not affect dependent variable
                    prod_odd  = prod_odd * x[i];
                    before.n_var += 1; before.n_op += 1;
                    after.n_var  += 0; after.n_op  += 0;
               }
          }

          // dependent variable for this operation sequence
          y[0] = prod_even;
          before.n_var += 0; before.n_op  += 0;
          after.n_var  += 0; after.n_op   += 0;
     }
}

bool reverse_active(void)
{     bool ok = true;
     using CppAD::AD;
     using CppAD::NearEqual;
     double eps10 = 10.0 * std::numeric_limits<double>::epsilon();

     // domain space vector
     size_t n  = 6;
     CPPAD_TESTVECTOR(AD<double>) ax(n);
     for(size_t i = 0; i < n; i++)
          ax[i] = AD<double>(i + 1);

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

     // range space vector
     size_t m = 1;
     CPPAD_TESTVECTOR(AD<double>) ay(m);
     tape_size before, after;
     fun(ax, ay, before, after);

     // create f: x -> y and stop tape recording
     CppAD::ADFun<double> f(ax, ay);
     ok &= f.size_var() == before.n_var;
     ok &= f.size_op()  == before.n_op;

     // Optimize the operation sequence
     f.optimize();
     ok &= f.size_var() == after.n_var;
     ok &= f.size_op()  == after.n_op;

     // check zero order forward with different argument value
     CPPAD_TESTVECTOR(double) x(n), y(m), check(m);
     for(size_t i = 0; i < n; i++)
          x[i] = double(i + 2);
     y    = f.Forward(0, x);
     fun(x, check, before, after);
     ok &= NearEqual(y[0], check[0], eps10, eps10);

     return ok;
}

Input File: example/optimize/reverse_active.cpp