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Up-> cppad_py library cpp_lib sparse cpp_sparse_rc sparse_rc_xam.cpp

library-> py_lib cpp_lib

cpp_lib-> a_double vector cpp_fun sparse cpp_utility more_cpp

sparse-> cpp_sparse_rc cpp_sparse_rcv cpp_jac_sparsity cpp_sparsity cpp_sparse_jac cpp_sparse_hes

cpp_sparse_rc-> sparse_rc_xam.cpp

sparse_rc_xam.cpp

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# include <cstdio>
# include <cppad/py/cppad_py.hpp>

bool sparse_rc_xam(void) {
     using cppad_py::vec_int;
     using cppad_py::sparse_rc;
     //
     // initialize return variable
     bool ok = true;
     //------------------------------------------------------------------------
     //
     // create a sparsity pattern
     sparse_rc pattern = sparse_rc();
     //
     int nr = 6;
     int nc = 5;
     int nnz = 4;
     //
     // resize
     pattern.resize(nr, nc, nnz);
     //
     ok = ok && pattern.nr()  == nr ;
     ok = ok && pattern.nc()  == nc ;
     ok = ok && pattern.nnz() == nnz ;
     //
     // indices corresponding to upper-diagonal
     for(int k = 0; k < nnz; k++) {
          pattern.put(k, k, k+1);
     }
     //
     // row and column indices
     vec_int row = pattern.row();
     vec_int col = pattern.col();
     //
     // check row and column indices
     for(int k = 0; k < nnz; k++) {
          ok = ok && row[k] == k;
          ok = ok && col[k] == k+1;
     }
     //
     // For this case, row_major and col_major order are same as original order
     vec_int row_major = pattern.row_major();
     vec_int col_major = pattern.col_major();
     for(int k = 0; k < nnz; k++) {
          ok = ok && row_major[k] == k;
          ok = ok && col_major[k] == k;
     }
     //
     return( ok );
}