1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
|
// 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_lu.cpp}
Fadbad 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 <FADBAD++/badiff.h>
# include <cppad/speed/det_by_lu.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_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
//
// object for computing determinant
typedef fadbad::B<double> ADScalar;
typedef CppAD::vector<ADScalar> ADVector;
CppAD::det_by_lu<ADScalar> Det(size);
size_t i; // temporary index
size_t m = 1; // number of dependent variables
size_t n = size * size; // number of independent variables
ADScalar detA; // AD value of the determinant
ADVector A(n); // AD version of matrix
// ------------------------------------------------------
while(repeat--)
{ // get the next matrix
CppAD::uniform_01(n, matrix);
// set independent variable values
for(i = 0; i < n; i++)
A[i] = matrix[i];
// compute the determinant
detA = Det(A);
// create function object f : A -> detA
detA.diff(0, (unsigned int) m); // index 0 of m dependent variables
// evaluate and return gradient using reverse mode
for(i =0; i < n; i++)
gradient[i] = A[i].d(0); // partial detA w.r.t A[i]
}
// ---------------------------------------------------------
return true;
}
/* {xrst_code}
{xrst_spell_on}
{xrst_end fadbad_det_lu.cpp}
*/
|
