# include <cppad/vector.hpp>
# include <cppad/speed/det_by_lu.hpp>
# include <cppad/speed/uniform_01.hpp>

bool link_det_lu(
	size_t                           size     , 
	size_t                           repeat   , 
	CppAD::vector<double>           &matrix   ,
	CppAD::vector<double>           &gradient )
{
	// -----------------------------------------------------
	// setup
	typedef CppAD::AD<double>           ADScalar; 
	typedef CppAD::vector<ADScalar>     ADVector; 
	CppAD::det_by_lu<ADScalar>          Det(size);

	size_t i;               // temporary index
	size_t m = 1;           // number of dependent variables
	size_t n = size * size; // number of independent variables
	ADVector   A(n);        // AD domain space vector
	ADVector   detA(m);     // AD range space vector
	CppAD::ADFun<double> f; // AD function object
	
	// vectors of reverse mode weights 
	CppAD::vector<double> w(1);
	w[0] = 1.;

	// ------------------------------------------------------
	static bool printed = false;
	bool print_this_time = (! printed) & (repeat > 1) & (size >= 10);
	while(repeat--)
	{	// get the next matrix
		CppAD::uniform_01(n, matrix);
		for( i = 0; i < n; i++)
			A[i] = matrix[i];

		// declare independent variables
		Independent(A);

		// AD computation of the determinant
		detA[0] = Det(A);

		// create function object f : A -> detA
		f.Dependent(A, detA);

		extern bool global_optimize;
		if( global_optimize )
		{	size_t before, after;
			before = f.size_var();
			f.optimize();
			if( print_this_time ) 
			{	after = f.size_var();
				std::cout << "cppad_det_lu_optimize_size_" 
				          << int(size) << " = [ " << int(before) 
				          << ", " << int(after) << "]" << std::endl;
				printed         = true;
				print_this_time = false;
			}
		}

		// evaluate and return gradient using reverse mode
		f.Forward(0, matrix);
		gradient = f.Reverse(1, w);
	}
	return true;
}