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

sparse_jac_fun.hpp

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Source: sparse_jac_fun

# ifndef CPPAD_SPARSE_JAC_FUN_HPP

# define CPPAD_SPARSE_JAC_FUN_HPP

# include <cppad/core/cppad_assert.hpp>
# include <cppad/utility/check_numeric_type.hpp>
# include <cppad/utility/vector.hpp>

// following needed by gcc under fedora 17 so that exp(double) is defined
# include <cppad/base_require.hpp>

namespace CppAD {
   template <class Float, class FloatVector>
   void sparse_jac_fun(
      size_t                       m    ,
      size_t                       n    ,
      const FloatVector&           x    ,
      const CppAD::vector<size_t>& row  ,
      const CppAD::vector<size_t>& col  ,
      size_t                       p    ,
      FloatVector&                 fp   )
   {
      // check numeric type specifications
      CheckNumericType<Float>();
      // check value of p
      CPPAD_ASSERT_KNOWN(
         p == 0 || p == 1,
         "sparse_jac_fun: p != 0 and p != 1"
      );
      size_t K = row.size();
      CPPAD_ASSERT_KNOWN(
         K >= m,
         "sparse_jac_fun: row.size() < m"
      );
      size_t i, j, k;

      if( p == 0 )
         for(i = 0; i < m; i++)
            fp[i] = Float(0);

      Float t;
      for(k = 0; k < K; k++)
      {  i    = row[k];
         j    = col[k];
         t    = exp( x[j] * x[j] / 2.0 );
         switch(p)
         {
            case 0:
            fp[i] += t;
            break;

            case 1:
            fp[k] = t * x[j];
            break;
         }
      }
   }
}

# endif