\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)
fadbad_det_minor.cpp¶
View page sourceFadbad Speed: Gradient of Determinant by Minor Expansion¶
Specifications¶
See link_det_minor .
Implementation¶
// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <FADBAD++/badiff.h>
# include <cppad/speed/det_by_minor.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/utility/vector.hpp>
// list of possible options
# include <map>
extern std::map<std::string, bool> global_option;
bool link_det_minor(
const std::string& job ,
size_t size ,
size_t repeat ,
CppAD::vector<double> &matrix ,
CppAD::vector<double> &gradient )
{
// --------------------------------------------------------------------
// check not global options
typedef std::map<std::string, bool>::iterator iterator;
for(iterator itr=global_option.begin(); itr!=global_option.end(); ++itr)
{ if( itr->second )
return false;
}
// -----------------------------------------------------
// AD types
typedef fadbad::B<double> b_double;
typedef CppAD::vector<b_double> b_vector;
// object that computes the determinant
CppAD::det_by_minor<b_double> b_det(size);
// number of dependent variables
unsigned int m = 1;
// number of independent variables
size_t n = size * size;
// independent variable vector
b_vector b_A(n);
// AD value of the determinant
b_double b_detA;
// ------------------------------------------------------
while(repeat--)
{ // get the next matrix
CppAD::uniform_01(n, matrix);
// set independent variable values
for(size_t j = 0; j < n; j++)
b_A[j] = matrix[j];
// compute the determinant
b_detA = b_det(b_A);
// create function object f : A -> detA
b_detA.diff(0, m); // index 0 of m dependent variables
// evaluate and return gradient using reverse mode
for(size_t j = 0; j < n; j++)
gradient[j] = b_A[j].d(0); // partial detA w.r.t A[i]
}
// ---------------------------------------------------------
return true;
}