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@(@\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 .
sparse_rcv: Example and Test
# include <cppad/utility/sparse_rcv.hpp>
# include <vector>

bool sparse_rcv(void)
{   bool ok = true;
    typedef std::vector<size_t> SizeVector;
    typedef std::vector<double> ValueVector;

    // sparsity pattern for a 5 by 5 diagonal matrix
    size_t nr  = 5;
    size_t nc  = 5;
    size_t nnz = 5;
    CppAD::sparse_rc<SizeVector> pattern(nr, nc, nnz);
    for(size_t k = 0; k < nnz; k++)
    {   size_t r = nnz - k - 1; // reverse or column-major order
        size_t c = nnz - k - 1;
        pattern.set(k, r, c);
    }

    // sparse matrix
    CppAD::sparse_rcv<SizeVector, ValueVector> matrix(pattern);
    for(size_t k = 0; k < nnz; k++)
    {   double v = double(k);
        matrix.set(nnz - k - 1, v);
    }

    // row, column, and value vectors
    const SizeVector&  row( matrix.row() );
    const SizeVector&  col( matrix.row() );
    const ValueVector& val( matrix.val() );
    SizeVector col_major = matrix.col_major();

    // check row,  column, and value
    for(size_t k = 0; k < nnz; k++)
    {   ok &= row[ col_major[k] ] == k;
        ok &= col[ col_major[k] ] == k;
        ok &= val[ col_major[k] ] == double(k);
    }

    // pat
    const CppAD::sparse_rc<SizeVector> pat( matrix.pat() );
    ok &= nnz == pat.nnz();
    ok &= nr  == pat.nr();
    ok &= nc  == pat.nc();
    for(size_t k = 0; k < nnz; k++)
    {   ok &= row[k] == pat.row()[k];
        ok &= col[k] == pat.col()[k];
    }

    // create an empty matrix
    CppAD::sparse_rcv<SizeVector, ValueVector> other;
    ok &= other.nnz() == 0;
    ok &= other.nr()  == 0;
    ok &= other.nc()  == 0;

    // now swap other with matrix
    matrix.swap(other);
    ok &= matrix.nnz() == 0;
    ok &= matrix.nr()  == 0;
    ok &= matrix.nc()  == 0;
    for(size_t k = 0; k < nnz; k++)
    {   ok &= other.row()[ col_major[k] ] == k;
        ok &= other.col()[ col_major[k] ] == k;
        ok &= other.val()[ col_major[k] ] == double(k);
    }

    // now use the assignment statement
    matrix = other;
    ok    &= other.nr()  == matrix.nr();
    ok    &= other.nc()  == matrix.nc();
    ok    &= other.nnz() == matrix.nnz();
    for(size_t k = 0; k < nnz; k++)
    {   ok &= matrix.row()[k] == other.row()[k];
        ok &= matrix.col()[k] == other.col()[k];
        ok &= matrix.val()[k] == other.val()[k];
    }

    // now use the copy constructor
    CppAD::sparse_rcv<SizeVector, ValueVector> copy(matrix);
    ok    &= copy.nr()  == matrix.nr();
    ok    &= copy.nc()  == matrix.nc();
    ok    &= copy.nnz() == matrix.nnz();
    for(size_t k = 0; k < nnz; k++)
    {   ok &= matrix.row()[k] == copy.row()[k];
        ok &= matrix.col()[k] == copy.col()[k];
        ok &= matrix.val()[k] == copy.val()[k];
    }
    return ok;
}
Input File: example/utility/sparse_rcv.cpp