$\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }$
Double Speed: Sparse Hessian

Specifications

Implementation
# include <cppad/utility/vector.hpp>

// Note that CppAD uses global_option["memory"] at the main program level
# include <map>
extern std::map<std::string, bool> global_option;

size_t                           size     ,
size_t                           repeat   ,
size_t&                          n_sweep  )
{
if(global_option["onetape"]||global_option["atomic"]||global_option["optimize"]||global_option["boolsparsity"])
return false;
// -----------------------------------------------------
// setup
size_t order = 0;          // derivative order corresponding to function
size_t n     = size;       // argument space dimension
size_t m     = 1;          // range space dimension
vector<double> y(m);       // function value

// choose a value for x

// ------------------------------------------------------

while(repeat--)
{
// computation of the function
CppAD::sparse_hes_fun<double>(n, x, row, col, order, y);
}
hessian[0] = y[0];

return true;
}

Input File: speed/double/sparse_hessian.cpp