def hessianstructure(self):
if not self._hessian_available:
return np.zeros(0), np.zeros(0)
row = np.compress(self._hess_lower_mask, self._hess_lag.row)
col = np.compress(self._hess_lower_mask, self._hess_lag.col)
return row, col
def hessian(self, x, y, obj_factor):
if not self._hessian_available:
raise ValueError("Hessian requested, but not supported by the NLP")
self._set_primals_if_necessary(x)
self._set_duals_if_necessary(y)
self._set_obj_factor_if_necessary(obj_factor)
self._nlp.evaluate_hessian_lag(out=self._hess_lag)
data = np.compress(self._hess_lower_mask, self._hess_lag.data)
return data