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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: 2003-24 Bradley M. Bell
// ----------------------------------------------------------------------------
/*
{xrst_begin cppadcg_det_minor.cpp}
cppadcg Speed: Gradient of Determinant by Minor Expansion
#########################################################
Specifications
**************
See :ref:`link_det_minor-name` .
PASS_JACOBIAN_TO_CODE_GEN
*************************
If this is one, the Jacobian of the determinant is the function passed
to CppADCodeGen. In this case, the ``code_gen_fun``
:ref:`code_gen_fun@Syntax@function` is used to calculate
the Jacobian of the determinant.
Otherwise, this flag is zero and the determinant function is passed
to CppADCodeGen. In this case, the ``code_gen_fun``
:ref:`code_gen_fun@Syntax@jacobian` is used to calculate
the Jacobian of the determinant.
{xrst_spell_off}
{xrst_code cpp} */
# define PASS_JACOBIAN_TO_CODE_GEN 1
/* {xrst_code}
{xrst_spell_on}
Implementation
**************
{xrst_spell_off}
{xrst_code cpp} */
# include <cppad/speed/det_by_minor.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/utility/vector.hpp>
# include <cppad/example/code_gen_fun.hpp>
# include <map>
extern std::map<std::string, bool> global_option;
namespace {
//
// typedefs
typedef CppAD::cg::CG<double> c_double;
typedef CppAD::AD<c_double> ac_double;
typedef CppAD::vector<double> d_vector;
typedef CppAD::vector<ac_double> ac_vector;
//
// setup
void setup(
// inputs
size_t size ,
// outputs
code_gen_fun& fun )
{ // optimization options
std::string optimize_options =
"no_conditional_skip no_compare_op no_print_for_op";
//
// object for computing determinant
CppAD::det_by_minor<ac_double> ac_det(size);
//
// number of independent variables
size_t nx = size * size;
//
// choose a matrix
CppAD::vector<double> matrix(nx);
CppAD::uniform_01(nx, matrix);
//
// copy to independent variables
ac_vector ac_A(nx);
for(size_t j = 0; j < nx; ++j)
ac_A[j] = matrix[j];
//
// declare independent variables for function computation
bool record_compare = false;
size_t abort_op_index = 0;
CppAD::Independent(ac_A, abort_op_index, record_compare);
//
// AD computation of the determinant
ac_vector ac_detA(1);
ac_detA[0] = ac_det(ac_A);
//
// create function objects for f : A -> detA
CppAD::ADFun<c_double> c_f;
c_f.Dependent(ac_A, ac_detA);
if( global_option["optimize"] )
c_f.optimize(optimize_options);
# if ! PASS_JACOBIAN_TO_CODE_GEN
// f(x) is the determinant function
code_gen_fun::evaluation_enum eval_jac = code_gen_fun::dense_enum;
code_gen_fun f_tmp("det_minor", c_f, eval_jac);
fun.swap(f_tmp);
# else
CppAD::ADFun<ac_double, c_double> ac_f;
ac_f = c_f.base2ad();
//
// declare independent variables for gradient computation
CppAD::Independent(ac_A, abort_op_index, record_compare);
//
// vectors of reverse mode weights
CppAD::vector<ac_double> ac_w(1);
ac_w[0] = ac_double(1.0);
//
// AD computation of the gradient
ac_vector ac_gradient(nx);
ac_f.Forward(0, ac_A);
ac_gradient = ac_f.Reverse(1, ac_w);
//
// create function objects for g : A -> det'( detA )
CppAD::ADFun<c_double> c_g;
c_g.Dependent(ac_A, ac_gradient);
if( global_option["optimize"] )
c_g.optimize(optimize_options);
// g(x) is the Jacobian of the determinant
code_gen_fun g_tmp("det_minor", c_g);
fun.swap(g_tmp);
# endif
}
}
bool link_det_minor(
const std::string& job ,
size_t size ,
size_t repeat ,
CppAD::vector<double> &matrix ,
CppAD::vector<double> &gradient )
{ CPPAD_ASSERT_UNKNOWN( matrix.size() == size * size );
CPPAD_ASSERT_UNKNOWN( gradient.size() == size * size );
// --------------------------------------------------------------------
// check global options
const char* valid[] = { "onetape", "optimize"};
size_t n_valid = sizeof(valid) / sizeof(valid[0]);
typedef std::map<std::string, bool>::iterator iterator;
//
for(iterator itr=global_option.begin(); itr!=global_option.end(); ++itr)
{ if( itr->second )
{ bool ok = false;
for(size_t i = 0; i < n_valid; i++)
ok |= itr->first == valid[i];
if( ! ok )
return false;
}
}
// --------------------------------------------------------------------
//
// function object mapping matrix to gradient of determinant
static code_gen_fun static_fun;
//
// size corresponding static_fun
static size_t static_size = 0;
//
// number of independent variables
size_t nx = size * size;
//
// onetape
bool onetape = global_option["onetape"];
// ----------------------------------------------------------------------
if( job == "setup" )
{ if( onetape )
{ setup(size, static_fun);
static_size = size;
}
else
{ static_size = 0;
}
return true;
}
if( job == "teardown" )
{ code_gen_fun fun;
static_fun.swap(fun);
return true;
}
// -----------------------------------------------------------------------
CPPAD_ASSERT_UNKNOWN( job == "run" );
if( onetape ) while(repeat--)
{ // use if before assert to avoid warning that static_size is not used
if( size != static_size )
{ CPPAD_ASSERT_UNKNOWN( size == static_size );
}
// get next matrix
CppAD::uniform_01(nx, matrix);
// evaluate the gradient
# if PASS_JACOBIAN_TO_CODE_GEN
gradient = static_fun(matrix);
# else
gradient = static_fun.jacobian(matrix);
# endif
}
else while(repeat--)
{ setup(size, static_fun);
static_size = size;
// get next matrix
CppAD::uniform_01(nx, matrix);
// evaluate the gradient
# if PASS_JACOBIAN_TO_CODE_GEN
gradient = static_fun(matrix);
# else
gradient = static_fun.jacobian(matrix);
# endif
}
return true;
}
/* {xrst_code}
{xrst_spell_on}
{xrst_end cppadcg_det_minor.cpp}
*/
|
