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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}} }@)@
rosen_34: Example and Test
Define @(@ X : \B{R} \rightarrow \B{R}^n @)@ by @[@ X_i (t) = t^{i+1} @]@ for @(@ i = 1 , \ldots , n-1 @)@. It follows that @[@ \begin{array}{rclr} X_i(0) & = & 0 & {\rm for \; all \;} i \\ X_i ' (t) & = & 1 & {\rm if \;} i = 0 \\ X_i '(t) & = & (i+1) t^i = (i+1) X_{i-1} (t) & {\rm if \;} i > 0 \end{array} @]@ The example tests Rosen34 using the relations above:

# include <cppad/cppad.hpp>        // For automatic differentiation

namespace {
    class Fun {
    public:
        // constructor
        Fun(bool use_x_) : use_x(use_x_)
        { }

        // compute f(t, x) both for double and AD<double>
        template <class Scalar>
        void Ode(
            const Scalar                    &t,
            const CPPAD_TESTVECTOR(Scalar) &x,
            CPPAD_TESTVECTOR(Scalar)       &f)
        {   size_t n  = x.size();
            Scalar ti(1);
            f[0]   = Scalar(1);
            size_t i;
            for(i = 1; i < n; i++)
            {   ti *= t;
                // convert int(size_t) to avoid warning
                // on _MSC_VER systems
                if( use_x )
                    f[i] = int(i+1) * x[i-1];
                else
                    f[i] = int(i+1) * ti;
            }
        }

        // compute partial of f(t, x) w.r.t. t using AD
        void Ode_ind(
            const double                    &t,
            const CPPAD_TESTVECTOR(double) &x,
            CPPAD_TESTVECTOR(double)       &f_t)
        {   using namespace CppAD;

            size_t n  = x.size();
            CPPAD_TESTVECTOR(AD<double>) T(1);
            CPPAD_TESTVECTOR(AD<double>) X(n);
            CPPAD_TESTVECTOR(AD<double>) F(n);

            // set argument values
            T[0] = t;
            size_t i;
            for(i = 0; i < n; i++)
                X[i] = x[i];

            // declare independent variables
            Independent(T);

            // compute f(t, x)
            this->Ode(T[0], X, F);

            // define AD function object
            ADFun<double> fun(T, F);

            // compute partial of f w.r.t t
            CPPAD_TESTVECTOR(double) dt(1);
            dt[0] = 1.;
            f_t = fun.Forward(1, dt);
        }

        // compute partial of f(t, x) w.r.t. x using AD
        void Ode_dep(
            const double                    &t,
            const CPPAD_TESTVECTOR(double) &x,
            CPPAD_TESTVECTOR(double)       &f_x)
        {   using namespace CppAD;

            size_t n  = x.size();
            CPPAD_TESTVECTOR(AD<double>) T(1);
            CPPAD_TESTVECTOR(AD<double>) X(n);
            CPPAD_TESTVECTOR(AD<double>) F(n);

            // set argument values
            T[0] = t;
            size_t i, j;
            for(i = 0; i < n; i++)
                X[i] = x[i];

            // declare independent variables
            Independent(X);

            // compute f(t, x)
            this->Ode(T[0], X, F);

            // define AD function object
            ADFun<double> fun(X, F);

            // compute partial of f w.r.t x
            CPPAD_TESTVECTOR(double) dx(n);
            CPPAD_TESTVECTOR(double) df(n);
            for(j = 0; j < n; j++)
                dx[j] = 0.;
            for(j = 0; j < n; j++)
            {   dx[j] = 1.;
                df = fun.Forward(1, dx);
                for(i = 0; i < n; i++)
                    f_x [i * n + j] = df[i];
                dx[j] = 0.;
            }
        }

    private:
        const bool use_x;

    };
}

bool rosen_34(void)
{   bool ok = true;     // initial return value
    size_t i;           // temporary indices

    using CppAD::NearEqual;
    double eps99 = 99.0 * std::numeric_limits<double>::epsilon();

    size_t  n = 4;      // number components in X(t) and order of method
    size_t  M = 2;      // number of Rosen34 steps in [ti, tf]
    double ti = 0.;     // initial time
    double tf = 2.;     // final time

    // xi = X(0)
    CPPAD_TESTVECTOR(double) xi(n);
    for(i = 0; i <n; i++)
        xi[i] = 0.;

    size_t use_x;
    for( use_x = 0; use_x < 2; use_x++)
    {   // function object depends on value of use_x
        Fun F(use_x > 0);

        // compute Rosen34 approximation for X(tf)
        CPPAD_TESTVECTOR(double) xf(n), e(n);
        xf = CppAD::Rosen34(F, M, ti, tf, xi, e);

        double check = tf;
        for(i = 0; i < n; i++)
        {   // check that error is always positive
            ok    &= (e[i] >= 0.);
            // 4th order method is exact for i < 4
            if( i < 4 ) ok &=
                NearEqual(xf[i], check, eps99, eps99);
            // 3rd order method is exact for i < 3
            if( i < 3 )
                ok &= (e[i] <= eps99);

            // check value for next i
            check *= tf;
        }
    }
    return ok;
}

Input File: example/general/rosen_34.cpp