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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 sparse_hes_fun.cpp}
sparse_hes_fun: Example and test
################################
{xrst_literal
// BEGIN C++
// END C++
}
{xrst_end sparse_hes_fun.cpp}
*/
// BEGIN C++
# include <cppad/speed/sparse_hes_fun.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/cppad.hpp>
bool sparse_hes_fun(void)
{ using CppAD::NearEqual;
bool ok = true;
typedef CppAD::AD<double> ADScalar;
size_t j, k;
double eps = 10. * CppAD::numeric_limits<double>::epsilon();
size_t n = 5;
size_t m = 1;
size_t K = 2 * n;
CppAD::vector<size_t> row(K), col(K);
CppAD::vector<double> x(n), ypp(K);
CppAD::vector<ADScalar> a_x(n), a_y(m);
// choose x
for(j = 0; j < n; j++)
a_x[j] = x[j] = double(j + 1);
// choose row, col
for(k = 0; k < K; k++)
{ row[k] = k % 3;
col[k] = k / 3;
}
for(k = 0; k < K; k++)
{ for(size_t k1 = 0; k1 < K; k1++)
assert( k == k1 || row[k] != row[k1] || col[k] != col[k1] );
}
// declare independent variables
Independent(a_x);
// evaluate function
size_t order = 0;
CppAD::sparse_hes_fun<ADScalar>(n, a_x, row, col, order, a_y);
// evaluate Hessian
order = 2;
CppAD::sparse_hes_fun<double>(n, x, row, col, order, ypp);
// use AD to evaluate Hessian
CppAD::ADFun<double> f(a_x, a_y);
CppAD::vector<double> hes(n * n);
// compoute Hessian of f_0 (x)
hes = f.Hessian(x, 0);
for(k = 0; k < K; k++)
{ size_t index = row[k] * n + col[k];
ok &= NearEqual(hes[index], ypp[k] , eps, eps);
}
return ok;
}
// END C++
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