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# include <cstdio> # include <string> # include <cppad/swig/cppad_swig.hpp> bool sparse_hes_xam(void) { using cppad_swig::a_double; using cppad_swig::vec_bool; using cppad_swig::vec_int; using cppad_swig::vec_double; using cppad_swig::vec_a_double; using cppad_swig::a_fun; using cppad_swig::sparse_rc; using cppad_swig::sparse_rcv; using cppad_swig::sparse_jac_work; using cppad_swig::sparse_hes_work; using std::string; // // initialize return variable bool ok = true; //------------------------------------------------------------------------ // number of dependent and independent variables int n = 3; // // create the independent variables ax vec_double x = cppad_swig::vec_double(n); for(int i = 0; i < n ; i++) { x[i] = i + 2.0; } vec_a_double ax = cppad_swig::independent(x); // // ay[i] = j * ax[j] * ax[i] // where i = mod(j + 1, n) vec_a_double ay = cppad_swig::vec_a_double(n); for(int j = 0; j < n ; j++) { int i = j+1; if( i >= n ) { i = i - n; } a_double aj = cppad_swig::a_double(j); a_double ax_j = ax[j]; a_double ax_i = ax[i]; ay[i] = aj * ax_j * ax_i; } // // define af corresponding to f(x) a_fun af = cppad_swig::a_fun(ax, ay); // // Set select_d (domain) to all true, // initial select_r (range) to all false // initialize r to all zeros vec_bool select_d = cppad_swig::vec_bool(n); vec_bool select_r = cppad_swig::vec_bool(n); vec_double r = cppad_swig::vec_double(n); for(int i = 0; i < n; i++) { select_d[i] = true; select_r[i] = false; r[i] = 0.0; } // // only select component n-1 of the range function // f_0 (x) = (n-1) * x_{n-1} * x_0 select_r[0] = true; r[0] = 1.0; // // sparisty pattern for Hessian sparse_rc pattern = cppad_swig::sparse_rc(); af.for_hes_sparsity(select_d, select_r, pattern); // // compute all possibly non-zero entries in Hessian // (should only compute lower triangle becuase matrix is symmetric) sparse_rcv subset = cppad_swig::sparse_rcv(pattern); // // work space used to save time for multiple calls sparse_hes_work work = cppad_swig::sparse_hes_work(); // af.sparse_hes(subset, x, r, pattern, work); // // check that result is sparsity pattern for Hessian of f_0 (x) ok = ok && subset.nnz() == 2 ; vec_int row = subset.row(); vec_int col = subset.col(); vec_double val = subset.val(); for(int k = 0; k < 2; k++) { int i = row[k]; int j = col[k]; double v = val[k]; if( i <= j ) { ok = ok && i == 0; ok = ok && j == n-1; } if( i >= j ) { ok = ok && i == n-1; ok = ok && j == 0; } ok = ok && v == n-1; } // return( ok ); }