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Up-> cppad_py library cpp_lib sparse cpp_sparse_rcv sparse_rcv_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_rcv-> sparse_rcv_xam.cpp

sparse_rcv_xam.cpp

Headings

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

bool sparse_rcv_xam(void) {
     using cppad_py::vec_int;
     using cppad_py::vec_double;
     using cppad_py::sparse_rc;
     using cppad_py::sparse_rcv;
     //
     // initialize return variable
     bool ok = true;
     //------------------------------------------------------------------------
     //
     // create sparsity pattern for n by n identity matrix
     sparse_rc pattern = sparse_rc();
     int n = 5;
     pattern.resize(n, n, n);
     for(int k = 0; k < n; k++) {
          pattern.put(k, k, k);
     }
     //
     // create n by n sparse representation of identity matrix
     sparse_rcv matrix = sparse_rcv(pattern);
     for(int k = 0; k < n; k++) {
          matrix.put(k, 1.0);
     }
     //
     // row, column indices
     vec_int row = matrix.row();
     vec_int col = matrix.col();
     vec_double val = matrix.val();
     //
     // check results
     for(int k = 0; k < n; k++) {
          ok = ok && row[k] == k;
          ok = ok && col[k] == k;
          ok = ok && val[k] == 1.0;
     }
     //
     // For this case, row_major and col_major order are same as original order
     vec_int row_major = matrix.row_major();
     vec_int col_major = matrix.col_major();
     for(int k = 0; k < n; k++) {
          ok = ok && row_major[k] == k;
          ok = ok && col_major[k] == k;
     }
     //
     return( ok );
}