Prev Next sacado_ode.cpp

@(@\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 .
Sacado Speed: Gradient of Ode Solution

Specifications
See link_ode .

Implementation 
// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <Sacado.hpp>
# include <cassert>
# include <cppad/utility/vector.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/speed/ode_evaluate.hpp>

// list of possible options
# include <map>
extern std::map<std::string, bool> global_option;

bool link_ode(
    size_t                     size       ,
    size_t                     repeat     ,
    CppAD::vector<double>      &x         ,
    CppAD::vector<double>      &jacobian
)
{
    // speed test global option values
    if( global_option["atomic"] )
        return false;
    if( global_option["memory"] || global_option["onetape"] || global_option["optimize"] )
        return false;
    // -------------------------------------------------------------
    // setup
    assert( x.size() == size );
    assert( jacobian.size() == size * size );

    typedef Sacado::Fad::DFad<double>  ADScalar;
    typedef CppAD::vector<ADScalar>    ADVector;

    size_t i, j;
    size_t p = 0;          // use ode to calculate function values
    size_t n = size;       // number of independent variables
    size_t m = n;          // number of dependent variables
    ADVector X(n), Y(m);   // independent and dependent variables

    // -------------------------------------------------------------
    while(repeat--)
    {   // choose next x value
        CppAD::uniform_01(n, x);
        for(j = 0; j < n; j++)
        {   // set up for X as the independent variable vector
            X[j] = ADScalar(int(n), int(j), x[j]);
        }

        // evaluate function
        CppAD::ode_evaluate(X, p, Y);

        // return values with Y as the dependent variable vector
        for(i = 0; i < m; i++)
        {   for(j = 0; j < n; j++)
                jacobian[ i * n + j ] = Y[i].dx(j);
        }
    }
    return true;
}
Input File: speed/sacado/ode.cpp