sparse_jac_fun: Example and test
Keywords: sparse_jac_fun: example test sparse_jac_fun

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sparse_jac_fun: Example and test
`

    # 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;
    }

`

Input File: speed/example/sparse_jac_fun.cpp