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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 Print Forward Operators
# include <cppad/cppad.hpp>

namespace {
     struct tape_size { size_t n_var; size_t n_op; };

     void PrintFor(
          double pos, const char* before, double var, const char* after
     )
     {     if( pos <= 0.0 )
               std::cout << before << var << after;
          return;
     }
     template <class Vector> void fun(
          const std::string& options ,
          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();

          // Argument to PrintFor is only needed
          // if we are keeping print forward operators
          scalar minus_one = x[0] - 1.0;
          before.n_var += 1; before.n_op += 1;
          if( options.find("no_print_for_op") == std::string::npos )
          {     after.n_var += 1;  after.n_op += 1;
          }

          // print argument to log function minus one, if it is <= 0
          PrintFor(minus_one, "minus_one == ", minus_one , " is <=  0\n");
          before.n_var += 0; before.n_op += 1;
          if( options.find("no_print_for_op") == std::string::npos )
          {     after.n_var += 0;  after.n_op += 1;
          }

          // now compute log
          y[0] = log( x[0] );
          before.n_var += 1; before.n_op += 1;
          after.n_var  += 1; after.n_op  += 1;
     }
}

bool print_for(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  = 1;
     CPPAD_TESTVECTOR(AD<double>) ax(n);
     ax[0] = 1.5;

     // range space vector
     size_t m = 1;
     CPPAD_TESTVECTOR(AD<double>) ay(m);

     for(size_t k = 0; k < 2; k++)
     {     // optimization options
          std::string options = "";
          if( k == 0 )
               options = "no_print_for_op";

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

          // compute function value
          tape_size before, after;
          fun(options, 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(options);
          ok &= f.size_var() == after.n_var;
          ok &= f.size_op() == after.n_op;

          // Check result for a zero order calculation for a different x
          CPPAD_TESTVECTOR(double) x(n), y(m), check(m);
          x[0] = 2.75;
          y    = f.Forward(0, x);
          fun(options, x, check, before, after);
          ok &= NearEqual(y[0], check[0], eps10, eps10);
     }
     return ok;
}

Input File: example/optimize/print_for.cpp