tensorflow_probability/python/optimizer/bfgs_utils.py
def _check_convergence(current_position,
                       next_position,
                       current_objective,
                       next_objective,
                       next_gradient,
                       grad_tolerance,
                       f_relative_tolerance,
                       x_tolerance):
  """Checks if the algorithm satisfies the convergence criteria."""
  grad_converged = norm(next_gradient, dims=1) <= grad_tolerance
  x_converged = norm(next_position - current_position, dims=1) <= x_tolerance
  f_converged = (norm(next_objective - current_objective, dims=0) <=
                 f_relative_tolerance * current_objective)
  return grad_converged | x_converged | f_converged
def _check_convergence(current_position,
                       next_position,
                       current_objective,
                       next_objective,
                       next_gradient,
                       grad_tolerance,
                       f_relative_tolerance,
                       x_tolerance):
  grad_converged = norm(next_gradient, dims=1) <= grad_tolerance
  x_converged = norm(next_position - current_position, dims=1) <= x_tolerance
  f_converged = (norm(next_objective - current_objective, dims=0) <=
                 f_relative_tolerance * current_objective)
  return grad_converged | x_converged | f_converged
def _check_convergence ( current_position , next_position , current_objective , next_objective , next_gradient , grad_tolerance , f_relative_tolerance , x_tolerance ) : grad_converged = norm ( next_gradient , dims = 1 ) <= grad_tolerance x_converged = norm ( next_position - current_position , dims = 1 ) <= x_tolerance f_converged = ( norm ( next_objective - current_objective , dims = 0 ) <= f_relative_tolerance * current_objective ) return grad_converged | x_converged | f_converged
Checks if the algorithm satisfies the convergence criteria.
Checks if the algorithm satisfies the convergence criteria .