Forward Mode Hessian Sparsity: Example and Test

for_hes_sparsity.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 . Forward Mode Hessian Sparsity: Example and Test

# include <cppad/cppad.hpp>

bool for_hes_sparsity(void)
{   bool ok = true;
    using CppAD::AD;
    typedef CPPAD_TESTVECTOR(size_t)     SizeVector;
    typedef CppAD::sparse_rc<SizeVector> sparsity;
    //
    // domain space vector
    size_t n = 3;
    CPPAD_TESTVECTOR(AD<double>) ax(n);
    ax[0] = 0.;
    ax[1] = 1.;
    ax[2] = 2.;

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

    // range space vector
    size_t m = 2;
    CPPAD_TESTVECTOR(AD<double>) ay(m);
    ay[0] = sin( ax[2] );
    ay[1] = ax[0] * ax[1];

    // create f: x -> y and stop tape recording
    CppAD::ADFun<double> f(ax, ay);

    // include all x components in sparsity pattern
    CPPAD_TESTVECTOR(bool) select_domain(n);
    for(size_t j = 0; j < n; j++)
        select_domain[j] = true;

    // compute sparsity pattern for H(x) = F_1''(x)
    CPPAD_TESTVECTOR(bool) select_range(m);
    select_range[0]    = false;
    select_range[1]    = true;
    bool internal_bool = true;
    sparsity pattern_out;
    f.for_hes_sparsity(
        select_domain, select_range, internal_bool, pattern_out
    );
    size_t nnz = pattern_out.nnz();
    ok        &= nnz == 2;
    ok        &= pattern_out.nr() == n;
    ok        &= pattern_out.nc() == n;
    {   // check results
        const SizeVector& row( pattern_out.row() );
        const SizeVector& col( pattern_out.col() );
        SizeVector row_major = pattern_out.row_major();
        //
        ok &= row[ row_major[0] ] ==  0  && col[ row_major[0] ] ==  1;
        ok &= row[ row_major[1] ] ==  1  && col[ row_major[1] ] ==  0;
    }
    //
    // compute sparsity pattern for H(x) = F_0''(x)
    select_range[0] = true;
    select_range[1] = false;
    f.for_hes_sparsity(
        select_domain, select_range, internal_bool, pattern_out
    );
    nnz = pattern_out.nnz();
    ok &= nnz == 1;
    ok &= pattern_out.nr() == n;
    ok &= pattern_out.nc() == n;
    {   // check results
        const SizeVector& row( pattern_out.row() );
        const SizeVector& col( pattern_out.col() );
        //
        ok &= row[0] ==  2  && col[0] ==  2;
    }
    return ok;
}

Input File: example/sparse/for_hes_sparsity.cpp