"The No Free Lunch theorems establish that improved optimization performance over one class of problems is offset by performance over another class under the theorem’s assumptions."
The original NFL paper supports using the theorem to argue that optimization performance depends on the problem class, not that scaling itself inherently produces unknown unknowns.
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