ADFun Assignment: Example and Test
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ADFun Assignment: Example and Test
`

    # include <cppad/cppad.hpp>
    # include <limits>

    bool fun_assign(void)
    {	bool ok = true;
    	using CppAD::AD;
    	using CppAD::NearEqual;
    	size_t i, j;

    	// ten times machine percision
    	double eps = 10. * CppAD::numeric_limits<double>::epsilon();

    	// two ADFun<double> objects
    	CppAD::ADFun<double> g;

    	// domain space vector
    	size_t n  = 3;
    	CPPAD_TESTVECTOR(AD<double>) x(n);
    	for(j = 0; j < n; j++)
    		x[j] = AD<double>(j + 2);

    	// declare independent variables and start tape recording
    	CppAD::Independent(x);

    	// range space vector
    	size_t m = 2;
    	CPPAD_TESTVECTOR(AD<double>) y(m);
    	y[0] = x[0] + x[0] * x[1];
    	y[1] = x[1] * x[2] + x[2];

    	// Store operation sequence, and order zero forward results, in f.
    	CppAD::ADFun<double> f(x, y);

    	// sparsity pattern for the identity matrix
    	CPPAD_TESTVECTOR(std::set<size_t>) r(n);
    	for(j = 0; j < n; j++)
    		r[j].insert(j);

    	// Store forward mode sparsity pattern in f
    	f.ForSparseJac(n, r);

    	// make a copy in g
    	g = f;

    	// check values that should be equal
    	ok &= ( g.size_order()       == f.size_order() );
    	ok &= ( g.size_forward_bool() == f.size_forward_bool() );
    	ok &= ( g.size_forward_set()  == f.size_forward_set() );

    	// Use zero order Taylor coefficient from f for first order
    	// calculation using g.
    	CPPAD_TESTVECTOR(double) dx(n), dy(m);
    	for(i = 0; i < n; i++)
    		dx[i] = 0.;
    	dx[1] = 1;
    	dy    = g.Forward(1, dx);
    	ok &= NearEqual(dy[0], x[0], eps, eps); // partial y[0] w.r.t x[1]
    	ok &= NearEqual(dy[1], x[2], eps, eps); // partial y[1] w.r.t x[1]

    	// Use forward Jacobian sparsity pattern from f to calculate
    	// Hessian sparsity pattern using g.
    	CPPAD_TESTVECTOR(std::set<size_t>) s(1), h(n);
    	s[0].insert(0); // Compute sparsity pattern for Hessian of y[0]
    	h =  f.RevSparseHes(n, s);

    	// check sparsity pattern for Hessian of y[0] = x[0] + x[0] * x[1]
    	ok  &= ( h[0].find(0) == h[0].end() ); // zero     w.r.t x[0], x[0]
    	ok  &= ( h[0].find(1) != h[0].end() ); // non-zero w.r.t x[0], x[1]
    	ok  &= ( h[0].find(2) == h[0].end() ); // zero     w.r.t x[0], x[2]

    	ok  &= ( h[1].find(0) != h[1].end() ); // non-zero w.r.t x[1], x[0]
    	ok  &= ( h[1].find(1) == h[1].end() ); // zero     w.r.t x[1], x[1]
    	ok  &= ( h[1].find(2) == h[1].end() ); // zero     w.r.t x[1], x[2]

    	ok  &= ( h[2].find(0) == h[2].end() ); // zero     w.r.t x[2], x[0]
    	ok  &= ( h[2].find(1) == h[2].end() ); // zero     w.r.t x[2], x[1]
    	ok  &= ( h[2].find(2) == h[2].end() ); // zero     w.r.t x[2], x[2]

    	return ok;
    }

`

Input File: example/fun_assign.cpp