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// $Id: sparse_hes_fun.cpp 3788 2016-02-09 15:50:06Z bradbell $
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-16 Bradley M. Bell
CppAD is distributed under multiple licenses. This distribution is under
the terms of the
GNU General Public License Version 3.
A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */
/*
$begin sparse_hes_fun.cpp$$
$spell
hes
$$
$section sparse_hes_fun: Example and test$$
$mindex sparse_hes_fun$$
$code
$srcfile%speed/example/sparse_hes_fun.cpp%0%// BEGIN C++%// END C++%1%$$
$$
$end
*/
// 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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