tensorflow_probability/python/optimizer/bfgs_utils.py
def _update_position(state,
                     position_delta,
                     next_objective,
                     next_gradient,
                     grad_tolerance,
                     f_relative_tolerance,
                     x_tolerance):
  """Updates the state advancing its position by a given position_delta."""
  failed = state.failed | ~tf.math.is_finite(next_objective) | ~tf.reduce_all(
      input_tensor=tf.math.is_finite(next_gradient), axis=-1)

  next_position = state.position + position_delta
  converged = ~failed & _check_convergence(state.position,
                                           next_position,
                                           state.objective_value,
                                           next_objective,
                                           next_gradient,
                                           grad_tolerance,
                                           f_relative_tolerance,
                                           x_tolerance)
  return update_fields(
      state,
      converged=state.converged | converged,
      failed=failed,
      position=next_position,
      objective_value=next_objective,
      objective_gradient=next_gradient)
def _update_position(state,
                     position_delta,
                     next_objective,
                     next_gradient,
                     grad_tolerance,
                     f_relative_tolerance,
                     x_tolerance):
  """Updates the state advancing its position by a given position_delta."""
  failed = state.failed | ~tf.math.is_finite(next_objective) | ~tf.reduce_all(
      input_tensor=tf.math.is_finite(next_gradient), axis=-1)

  next_position = state.position + position_delta
  converged = ~failed & _check_convergence(state.position,
                                           next_position,
                                           state.objective_value,
                                           next_objective,
                                           next_gradient,
                                           grad_tolerance,
                                           f_relative_tolerance,
                                           x_tolerance)
  return update_fields(
      state,
      converged=state.converged | converged,
      failed=failed,
      position=next_position,
      objective_value=next_objective,
      objective_gradient=next_gradient)
def _update_position ( state , position_delta , next_objective , next_gradient , grad_tolerance , f_relative_tolerance , x_tolerance ) : failed = state . failed | ~ tf . math . is_finite ( next_objective ) | ~ tf . reduce_all ( input_tensor = tf . math . is_finite ( next_gradient ) , axis = - 1 ) next_position = state . position + position_delta converged = ~ failed & _check_convergence ( state . position , next_position , state . objective_value , next_objective , next_gradient , grad_tolerance , f_relative_tolerance , x_tolerance ) return update_fields ( state , converged = state . converged | converged , failed = failed , position = next_position , objective_value = next_objective , objective_gradient = next_gradient )
Updates the state advancing its position by a given position_delta.
Updates the state advancing its position by a given position_delta .