A Worst-Case Boundary inside a Partitioned Constraint Model
Abstract
Partition regression can approximate feasibility locally, while worst-case selection evaluates it over uncertain inputs. Their comparison should target candidates near both boundaries. Independent joint outputs reveal whether a failure arises from local approximation or the chosen input scenario. Increasing partition precision does not justify an implausible ambiguity description. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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