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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
// ----------------------------------------------------------------------------
# include <cstring>
# include <cppad/utility/vector.hpp>
/*
{xrst_begin adolc_sparse_jacobian.cpp}
Adolc Speed: Sparse Jacobian
############################
Specifications
**************
See :ref:`link_sparse_jacobian-name` .
Implementation
**************
{xrst_spell_off}
{xrst_code cpp} */
// suppress conversion warnings before other includes
# include <cppad/wno_conversion.hpp>
//
# include <adolc/adolc.h>
# include <adolc/adolc_sparse.h>
# include <cppad/utility/vector.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/speed/sparse_jac_fun.hpp>
// list of possible options
# include <map>
extern std::map<std::string, bool> global_option;
namespace {
using CppAD::vector;
typedef vector<size_t> s_vector;
typedef vector<double> d_vector;
typedef vector<adouble> a_vector;
void setup(
// inputs
short tag ,
size_t size ,
size_t m ,
const s_vector& row ,
const s_vector& col ,
const d_vector& x ,
int* options , // const but adolc want non-const arg
// outputs
size_t& n_color ,
int& nnz ,
unsigned int*& rind ,
unsigned int*& cind ,
double*& values )
{ // independent variables
CPPAD_ASSERT_UNKNOWN( size = x.size() );
int keep = 0; // keep forward mode results
trace_on(tag, keep);
size_t n = size;
a_vector a_x(n);
for(size_t j = 0; j < n; ++j)
a_x[j] <<= x[j];
//
// dependent variables
a_vector a_y(m);
//
// AD computation of f(x)
size_t order = 0;
CppAD::sparse_jac_fun<adouble>(m, n, a_x, row, col, order, a_y);
//
// create function object f : x -> y
double yi;
for(size_t i = 0; i < m; i++)
a_y[i] >>= yi;
trace_off();
//
// null pointers for recalculation of sparsity pattern
free(rind);
free(cind);
free(values);
rind = nullptr;
cind = nullptr;
values = nullptr;
//
// Retrieve n_color using undocumented feature of sparsedrivers.cpp
int same_pattern = 0;
n_color = size_t( sparse_jac(tag, int(m), int(n),
same_pattern, x.data(), &nnz, &rind, &cind, &values, options
) );
}
}
bool link_sparse_jacobian(
const std::string& job ,
size_t size ,
size_t repeat ,
size_t m ,
const CppAD::vector<size_t>& row ,
const CppAD::vector<size_t>& col ,
CppAD::vector<double>& x_return ,
CppAD::vector<double>& jacobian ,
size_t& n_color )
{
// --------------------------------------------------------------------
// check global options
// Allow colpack true even though it is not used below because it is
// true durng the adolc correctness tests.
const char* valid[] = { "onetape", "optimize", "colpack"};
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;
}
}
// -----------------------------------------------------
static size_t static_size = 0;
static int static_nnz = 0;
unsigned int* static_rind = nullptr;
unsigned int* static_cind = nullptr;
double* static_values = nullptr;
// -----------------------------------------------------
short tag = 0;
//
// options that control sparse_jac
int options[4];
if( global_option["boolsparsity"] )
options[0] = 1; // sparsity by propagation of bit pattern
else
options[0] = 0; // sparsity pattern by index domains
options[1] = 0; // 0 = safe mode, 1 = tight mode
options[2] = 0; // 0 = autodetect, 1 = forward, 2 = reverse
options[3] = 0; // 0 = column compression, 1 = row compression
//
// independent variiables
size_t n = size;
d_vector x(n);
//
// default value for n_color
n_color = 0;
//
bool onetape = global_option["onetape"];
// -----------------------------------------------------
if( job == "setup" )
{ // get a value for x
CppAD::uniform_01( size_t(n), x);
//
// record the tape and run coloring problem
options[2] = -1;
setup(tag, size, m, row, col, x, options,
n_color, static_nnz, static_rind, static_cind, static_values
);
static_size = size;
//
return true;
}
if( job == "teardown" )
{ free(static_rind);
free(static_cind);
free(static_values);
static_rind = nullptr;
static_cind = nullptr;
static_values = nullptr;
return true;
}
// -----------------------------------------------------
CPPAD_ASSERT_UNKNOWN( job == "run" );
//
while (repeat--)
{ // choose a value for x
CppAD::uniform_01( size_t(n), x);
//
if ( ! onetape )
{ // retape and calculate jacobian
options[2] = -1; // stop at sparsity pattern, return n_color
setup(tag, size, m, row, col, x, options,
n_color, static_nnz, static_rind, static_cind, static_values
);
options[2] = 0;
}
else
{ if( size != static_size )
CPPAD_ASSERT_UNKNOWN( size == static_size );
}
// calculate the jacobian at this x
int same_pattern = 1;
sparse_jac(
tag, int(m), int(n), same_pattern, x.data(),
&static_nnz, &static_rind, &static_cind, &static_values,
options
);
}
// --------------------------------------------------------------------
// jacobian
CPPAD_ASSERT_UNKNOWN( size_t(static_nnz) == row.size() );
for(int ell = 0; ell < static_nnz; ell++)
{ CPPAD_ASSERT_UNKNOWN( row[ell] == size_t(static_rind[ell]) );
CPPAD_ASSERT_UNKNOWN( col[ell] == size_t(static_cind[ell]) );
jacobian[ell] = static_values[ell];
}
// x_return
for(size_t j = 0; j < n; j++)
x_return[j] = x[j];
//
return true;
}
/* {xrst_code}
{xrst_spell_on}
{xrst_end adolc_sparse_jacobian.cpp}
*/
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