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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 adolc_det_lu.cpp}
Adolc Speed: Gradient of Determinant Using Lu Factorization
###########################################################
Specifications
**************
See :ref:`link_det_lu-name` .
Implementation
**************
{xrst_spell_off}
{xrst_code cpp} */
// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <adolc/adolc.h>
# include <cppad/speed/det_by_lu.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/utility/track_new_del.hpp>
// list of possible options
# include <map>
extern std::map<std::string, bool> global_option;
bool link_det_lu(
size_t size ,
size_t repeat ,
CppAD::vector<double> &matrix ,
CppAD::vector<double> &gradient )
{
// speed test global option values
if( global_option["onetape"] || global_option["atomic"] )
return false;
if( global_option["memory"] || global_option["optimize"] )
return false;
// -----------------------------------------------------
// setup
short tag = 0; // tape identifier
int keep = 1; // keep forward mode results in buffer
int m = 1; // number of dependent variables
int n = int(size * size); // number of independent variables
double f; // function value
int j; // temporary index
// set up for thread_alloc memory allocator (fast and checks for leaks)
using CppAD::thread_alloc; // the allocator
size_t size_min; // requested number of elements
size_t size_out; // capacity of an allocation
// object for computing determinant
typedef adouble ADScalar;
typedef ADScalar* ADVector;
CppAD::det_by_lu<ADScalar> Det(size);
// AD value of determinant
ADScalar detA;
// AD version of matrix
size_min = size_t(n);
ADVector A = thread_alloc::create_array<ADScalar>(size_min, size_out);
// vectors of reverse mode weights
size_min = size_t(m);
double* u = thread_alloc::create_array<double>(size_min, size_out);
u[0] = 1.;
// vector with matrix value
size_min = size_t(n);
double* mat = thread_alloc::create_array<double>(size_min, size_out);
// vector to receive gradient result
size_min = size_t(n);
double* grad = thread_alloc::create_array<double>(size_min, size_out);
// ------------------------------------------------------
while(repeat--)
{ // get the next matrix
CppAD::uniform_01( size_t(n), mat);
// declare independent variables
trace_on(tag, keep);
for(j = 0; j < n; j++)
A[j] <<= mat[j];
// AD computation of the determinant
detA = Det(A);
// create function object f : A -> detA
detA >>= f;
trace_off();
// evaluate and return gradient using reverse mode
fos_reverse(tag, m, n, u, grad);
}
// ------------------------------------------------------
// return matrix and gradient
for(j = 0; j < n; j++)
{ matrix[j] = mat[j];
gradient[j] = grad[j];
}
// tear down
thread_alloc::delete_array(grad);
thread_alloc::delete_array(mat);
thread_alloc::delete_array(u);
thread_alloc::delete_array(A);
return true;
}
/* {xrst_code}
{xrst_spell_on}
{xrst_end adolc_det_lu.cpp}
*/
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