Evaluate Sparse Jacobian of a Code Gen Function: Example and Test

Loading [MathJax]/extensions/TeX/newcommand.js

code_gen_fun_sparse_jacobian.cpp

Headings

$\newcommand{\W}[1]{ \; #1 \; } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\B}[1]{ {\bf #1} } \newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} } \newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} } \newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} } \newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }$ This is cppad-20221105 documentation. Here is a link to its current documentation . Evaluate Sparse Jacobian of a Code Gen Function: Example and Test

# include <cppad/example/code_gen_fun.hpp>

bool sparse_jacobian(void)
{   bool ok = true;
    //
    typedef CppAD::cg::CG<double>     c_double;
    typedef CppAD::AD<c_double>      ac_double;
    //
    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 c_f: x -> y and stop tape recording
    CppAD::ADFun<c_double> c_f(ac_x, ac_y);

    // create compiled version of c_f
    std::string file_name = "example_lib";
    code_gen_fun::evaluation_enum eval_jac = code_gen_fun::sparse_enum;
    code_gen_fun f(file_name, c_f, eval_jac);

    // evaluate the compiled sparse_jacobian
    d_vector x(n);
    for(size_t j = 0; j < n; ++j)
        x[j] = 1.0 / double(j + 2);
    CppAD::sparse_rcv< CppAD::vector<size_t>, CppAD::vector<double> > Jrcv;
    // This assignment uses move semantics
    Jrcv = f.sparse_jacobian(x);

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

Input File: example/code_gen_fun/sparse_jacobian.cpp