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Up-> cppad_py library cpp_lib sparse cpp_jac_sparsity sparse_jac_pattern_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_jac_sparsity-> sparse_jac_pattern_xam.cpp

sparse_jac_pattern_xam.cpp

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

bool sparse_jac_pattern_xam(void) {
     using cppad_py::a_double;
     using cppad_py::vec_int;
     using cppad_py::vec_double;
     using cppad_py::vec_a_double;
     using cppad_py::d_fun;
     using cppad_py::sparse_rc;
     //
     // initialize return variable
     bool ok = true;
     //------------------------------------------------------------------------
     // number of dependent and independent variables
     int n = 3;
     //
     // create the independent variables ax
     vec_double x(n);
     for(int i = 0; i < n ; i++) {
          x[i] = i + 2.0;
     }
     vec_a_double ax = cppad_py::independent(x);
     //
     // create dependent variables ay with ay[i] = ax[j]
     // where i = mod(j + 1, n)
     vec_a_double ay(n);
     for(int j = 0; j < n ; j++) {
          int i = j+1;
          if( i >= n  ) {
               i = i - n;
          }
          a_double ay_i = ax[j];
          ay[i] = ay_i;
     }
     //
     // define af corresponding to f(x)
     d_fun f(ax, ay);
     //
     // sparsity pattern for identity matrix
     sparse_rc pat_in = sparse_rc();
     pat_in.resize(n, n, n);
     for(int k = 0; k < n; k++) {
          pat_in.put(k, k, k);
     }
     //
     // loop over forward and reverse mode
     for(int mode = 0; mode < 2; mode++) {
          sparse_rc pat_out = sparse_rc();
          if( mode == 0  ) {
               f.for_jac_sparsity(pat_in, pat_out);
          }
          if( mode == 1  ) {
               f.rev_jac_sparsity(pat_in, pat_out);
          }
          //
          // check that result is sparsity pattern for Jacobian
          ok = ok && n == pat_out.nnz();
          vec_int col_major = pat_out.col_major();
          vec_int row = pat_out.row();
          vec_int col = pat_out.col();
          for(int k = 0; k < n; k++) {
               int ell = col_major[k];
               int r = row[ell];
               int c = col[ell];
               int i = c+1;
               if( i >=  n  ) {
                    i = i - n;
               }
               ok = ok && c == k;
               ok = ok && r == i;
          }
     }
     //
     return( ok );
}