docstr: Stop the algorithm if the final function value is known
  
  Specify in options (with ``self.f_min_true = options['f_min']``)
  and the tolerance with ``f_tol = options['f_tol']``
code: def finite_precision(self):
      """
      Stop the algorithm if the final function value is known
  
      Specify in options (with ``self.f_min_true = options['f_min']``)
      and the tolerance with ``f_tol = options['f_tol']``
      """
      # If no minimizer has been found use the lowest sampling value
      if len(self.LMC.xl_maps) == 0:
          self.find_lowest_vertex()
  
      # Function to stop algorithm at specified percentage error:
      if self.f_lowest == 0.0:
          if self.f_min_true == 0.0:
              if self.f_lowest <= self.f_tol:
                  self.stop_global = True
      else:
          pe = (self.f_lowest - self.f_min_true) / abs(self.f_min_true)
          if self.f_lowest <= self.f_min_true:
              self.stop_global = True
              # 2if (pe - self.f_tol) <= abs(1.0 / abs(self.f_min_true)):
              if abs(pe) >= 2 * self.f_tol:
                  warnings.warn(
                      "A much lower value than expected f* ="
                      + " {} than".format(self.f_min_true)
                      + " the was found f_lowest ="
                      + "{} ".format(self.f_lowest)
                  )
          if pe <= self.f_tol:
              self.stop_global = True
  
      return self.stop_global
  
imports: import numpy
  import time
  import logging
  import warnings
  from scipy import spatial
  from scipy.optimize import OptimizeResult, minimize
  from scipy.optimize._shgo_lib.triangulation import Complex
label_desc: Use image node_id 10 for calling the SHGO obj's underlying member method code with example usage: obj.finite_precision() and returns: self