/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-16 Bradley M. Bell

CppAD is distributed under the terms of the
             Eclipse Public License Version 2.0.

This Source Code may also be made available under the following
Secondary License when the conditions for such availability set forth
in the Eclipse Public License, Version 2.0 are satisfied:
      GNU General Public License, Version 2.0 or later.
---------------------------------------------------------------------------- */
/*
$begin sparse_hes_fun.cpp$$
$spell
    hes
$$


$section sparse_hes_fun: Example and test$$

$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++
