sacado_ode.cpp

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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( int(j) );
        }
    }
    return true;
}