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Up-> CppAD Example General code_gen_fun code_gen_fun_sparse_jac_as_fun.cpp

code_gen_fun_sparse_jac_as_fun.cpp

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# include <cppad/example/code_gen_fun.hpp>

bool sparse_jac_as_fun(void)
{   bool ok = true;
    //
    typedef CppAD::cg::CG<double>     c_double;
    typedef CppAD::AD<c_double>      ac_double;
    //
    typedef CppAD::vector<size_t>     s_vector;
    typedef CppAD::vector<double>     d_vector;
    typedef CppAD::vector<ac_double> ac_vector;
    //
    double eps99 = 99.0 * std::numeric_limits<double>::epsilon();

    // domain space vector
    size_t n  = 2;
    ac_vector ac_x(n);
    for(size_t j = 0; j < n; ++j)
        ac_x[j] = 1.0 / double(j + 1);

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

    // range space vector
    size_t m = 3;
    ac_vector ac_y(m);
    for(size_t i = 0; i < m; ++i)
        ac_y[i] = double(i + 1) * sin( ac_x[i % n] );

    // create f: x -> y and stop tape recording
    CppAD::ADFun<c_double> c_f(ac_x, ac_y);

    // create a version of f that evalutes using ac_double
    CppAD::ADFun<ac_double, c_double> ac_f = c_f.base2ad();

    // Independent varialbes while evaluating Jacobian
    CppAD::Independent(ac_x);

    // ----------------------------------------------------------------
    // Sparse Jacobian evaluation using any CppAD method
    // (there are lots of choices here)
    //
    // pattern_eye (pattern for identity matrix)
    CppAD::sparse_rc<s_vector> pattern_eye(n, n, n);
    for(size_t k = 0; k < n; ++k)
        pattern_eye.set(k, k, k);
    //
    // pattern_jac
    bool transpose     = false;
    bool dependency    = false;
    bool internal_bool = true;
    CppAD::sparse_rc<s_vector> pattern_jac;
    // note that c_f and ac_f have the same sparsity pattern
    c_f.for_jac_sparsity(
        pattern_eye, transpose, dependency, internal_bool, pattern_jac
    );
    //
    // ac_Jrcv
    CppAD::sparse_rcv<s_vector, ac_vector> ac_Jrcv( pattern_jac );
    CppAD::sparse_jac_work work;
    std::string coloring = "cppad";
    size_t group_max = n;
    ac_f.sparse_jac_for(
        group_max, ac_x, ac_Jrcv, pattern_jac, coloring, work
    );
    //
    // create g: x -> non-zero elements of Jacobian
    CppAD::ADFun<c_double> c_g(ac_x, ac_Jrcv.val());

    // create compiled version of c_g
    std::string file_name = "example_lib";
    code_gen_fun g(file_name, c_g);

    // evaluate the compiled jacobian
    d_vector x(n);
    for(size_t j = 0; j < n; ++j)
        x[j] = 1.0 / double(j + 2);
    d_vector val = g(x);

    // check Jaociban values
    size_t nnz = pattern_jac.nnz();
    const s_vector& row(pattern_jac.row());
    const s_vector& col(pattern_jac.col());
    s_vector row_major = pattern_jac.row_major();
    //
    size_t k = 0;
    ok &= val.size() == nnz;
    for(size_t i = 0; i < m; ++i)
    {   for(size_t j = 0; j < n; ++j)
        {   if( j == i % n )
            {   size_t ell = row_major[k];
                double check = double(i + 1) * cos( x[i % n] );
                ok &= i == row[ell];
                ok &= j == col[ell];
                ok &= CppAD::NearEqual(val[k], check, eps99, eps99);
                ++k;
            }
        }
    }
    ok &= k == nnz;
    return ok;
}