\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)

double_sparse_hessian.cpp

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Double Speed: Sparse Hessian

Specifications

See link_sparse_hessian .

Implementation

# include <cppad/utility/vector.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/speed/sparse_hes_fun.hpp>

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

bool link_sparse_hessian(
   size_t                           size     ,
   size_t                           repeat   ,
   const CppAD::vector<size_t>&     row      ,
   const CppAD::vector<size_t>&     col      ,
   CppAD::vector<double>&           x        ,
   CppAD::vector<double>&           hessian  ,
   size_t&                          n_color  )
{
   if(global_option["onetape"]||global_option["atomic"]||global_option["optimize"]||global_option["boolsparsity"])
      return false;
   // -----------------------------------------------------
   // setup
   using CppAD::vector;
   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
   CppAD::uniform_01(n, x);

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

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

   return true;
}