class_docstr: A sequential domain reduction transformer based on the work by Stander, N. and Craig, K:
  "On the robustness of a simple domain reduction scheme for simulation‐based optimization"
class_docstr_tok: ["A","sequential","domain","reduction","transformer","based","on","the","work","by","Stander",",","N.","and","Craig",",","K",":","''","On","the","robustness","of","a","simple","domain","reduction","scheme","for","simulation\u2010based","optimization","''"]
docstr: Checks if global bounds are compatible with the minimum window sizes.
          
docstr_tok: ["Checks","if","global","bounds","are","compatible","with","the","minimum","window","sizes","."]
code: def _window_bounds_compatibility(
      self, global_bounds: np.array
  ) -> bool:
      """Checks if global bounds are compatible with the minimum window sizes."""
      for i, entry in enumerate(global_bounds):
          global_window_width = abs(entry[1] - entry[0])
          if global_window_width < self.minimum_window[i]:
              raise ValueError(
                  "Global bounds are not compatible with the minimum window size."
              )
  
file_path: BayesianOptimization/bayes_opt/domain_reduction.py
imports: from typing import Optional, Union, List
  import numpy
  from .target_space import TargetSpace
  from warnings import warn
label_desc: Use image node_id 5 for calling the SequentialDomainReductionTransformer obj's underlying member method code with example usage: obj._window_bounds_compatibility(global_bounds) without return types