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// $Id: sparse_jac_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_jac_fun.cpp$$
$spell
jac
$$
$section sparse_jac_fun: Example and test$$
$mindex sparse_jac_fun$$
$code
$srcfile%speed/example/sparse_jac_fun.cpp%0%// BEGIN C++%// END C++%1%$$
$$
$end
*/
// BEGIN C++
# include <cppad/speed/sparse_jac_fun.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/cppad.hpp>
bool sparse_jac_fun(void)
{ using CppAD::NearEqual;
using CppAD::AD;
bool ok = true;
size_t j, k;
double eps = CppAD::numeric_limits<double>::epsilon();
size_t n = 3;
size_t m = 4;
size_t K = 5;
CppAD::vector<size_t> row(K), col(K);
CppAD::vector<double> x(n), yp(K);
CppAD::vector< AD<double> > 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 % m;
col[k] = (K - k) % n;
}
// declare independent variables
Independent(a_x);
// evaluate function
size_t order = 0;
CppAD::sparse_jac_fun< AD<double> >(m, n, a_x, row, col, order, a_y);
// evaluate derivative
order = 1;
CppAD::sparse_jac_fun<double>(m, n, x, row, col, order, yp);
// use AD to evaluate derivative
CppAD::ADFun<double> f(a_x, a_y);
CppAD::vector<double> jac(m * n);
jac = f.Jacobian(x);
for(k = 0; k < K; k++)
{ size_t index = row[k] * n + col[k];
ok &= NearEqual(jac[index], yp[k] , eps, eps);
}
return ok;
}
// END C++
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