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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}} }@)@This is cppad-20221105 documentation. Here is a link to its current documentation .
Optimize Print Forward Operators: Example and Test
# 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_order() == 1; // this constructor does 0 order forward
        ok &= f.size_var() == before.n_var;
        ok &= f.size_op() == before.n_op;

        // Optimize the operation sequence
        f.optimize(options);
        ok &= f.size_order() == 0; // 0 order forward not present
        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