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// SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
// SPDX-FileCopyrightText: Bradley M. Bell <bradbell@seanet.com>
// SPDX-FileContributor: 2003-22 Bradley M. Bell
// ----------------------------------------------------------------------------
/*
{xrst_begin fadbad_det_minor.cpp}
Fadbad Speed: Gradient of Determinant by Minor Expansion
########################################################
Specifications
**************
See :ref:`link_det_minor-name` .
Implementation
**************
{xrst_spell_off}
{xrst_code cpp} */
// 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;
}
/* {xrst_code}
{xrst_spell_on}
{xrst_end fadbad_det_minor.cpp}
*/
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