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