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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 code_gen_fun_sparse_jac_as_fun.cpp}
Pass Sparse Jacobian as Code Gen Function: Example and Test
###########################################################
{xrst_literal
// BEGIN C++
// END C++
}
{xrst_end code_gen_fun_sparse_jac_as_fun.cpp}
*/
// BEGIN C++
# include <cppad/example/code_gen_fun.hpp>
bool sparse_jac_as_fun(void)
{ bool ok = true;
//
typedef CppAD::cg::CG<double> c_double;
typedef CppAD::AD<c_double> ac_double;
//
typedef CppAD::vector<size_t> s_vector;
typedef CppAD::vector<double> d_vector;
typedef CppAD::vector<ac_double> ac_vector;
//
double eps99 = 99.0 * std::numeric_limits<double>::epsilon();
// domain space vector
size_t n = 2;
ac_vector ac_x(n);
for(size_t j = 0; j < n; ++j)
ac_x[j] = 1.0 / double(j + 1);
// declare independent variables and start tape recording
CppAD::Independent(ac_x);
// range space vector
size_t m = 3;
ac_vector ac_y(m);
for(size_t i = 0; i < m; ++i)
ac_y[i] = double(i + 1) * sin( ac_x[i % n] );
// create f: x -> y and stop tape recording
CppAD::ADFun<c_double> c_f(ac_x, ac_y);
// create a version of f that evalutes using ac_double
CppAD::ADFun<ac_double, c_double> ac_f = c_f.base2ad();
// Independent varialbes while evaluating Jacobian
CppAD::Independent(ac_x);
// ----------------------------------------------------------------
// Sparse Jacobian evaluation using any CppAD method
// (there are lots of choices here)
//
// pattern_eye (pattern for identity matrix)
CppAD::sparse_rc<s_vector> pattern_eye(n, n, n);
for(size_t k = 0; k < n; ++k)
pattern_eye.set(k, k, k);
//
// pattern_jac
bool transpose = false;
bool dependency = false;
bool internal_bool = true;
CppAD::sparse_rc<s_vector> pattern_jac;
// note that c_f and ac_f have the same sparsity pattern
c_f.for_jac_sparsity(
pattern_eye, transpose, dependency, internal_bool, pattern_jac
);
//
// ac_Jrcv
CppAD::sparse_rcv<s_vector, ac_vector> ac_Jrcv( pattern_jac );
CppAD::sparse_jac_work work;
std::string coloring = "cppad";
size_t group_max = n;
ac_f.sparse_jac_for(
group_max, ac_x, ac_Jrcv, pattern_jac, coloring, work
);
//
// create g: x -> non-zero elements of Jacobian
CppAD::ADFun<c_double> c_g(ac_x, ac_Jrcv.val());
// create compiled version of c_g
std::string file_name = "example_lib";
code_gen_fun g(file_name, c_g);
// evaluate the compiled jacobian
d_vector x(n);
for(size_t j = 0; j < n; ++j)
x[j] = 1.0 / double(j + 2);
d_vector val = g(x);
// check Jaociban values
size_t nnz = pattern_jac.nnz();
const s_vector& row(pattern_jac.row());
const s_vector& col(pattern_jac.col());
s_vector row_major = pattern_jac.row_major();
//
size_t k = 0;
ok &= val.size() == nnz;
for(size_t i = 0; i < m; ++i)
{ for(size_t j = 0; j < n; ++j)
{ if( j == i % n )
{ size_t ell = row_major[k];
double check = double(i + 1) * cos( x[i % n] );
ok &= i == row[ell];
ok &= j == col[ell];
ok &= CppAD::NearEqual(val[k], check, eps99, eps99);
++k;
}
}
}
ok &= k == nnz;
return ok;
}
// END C++
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