Think from an information theory perspective. It is rarely true that you cannot say anything more about the data than what is assumed by classical algorithms. We almost always have some more information depending on the specific domain under consideration. Eg: Sorting a list of ages might be very different from sorting a list of account balances.
Any time I have information that reduces the entropy of the dataset, I want to be able to leverage that into runtime improvements of algorithms for pertinent questions. And it would be great to develop a structured framework for that instead of handling special cases in an ad-hoc manner.
Any time I have information that reduces the entropy of the dataset, I want to be able to leverage that into runtime improvements of algorithms for pertinent questions. And it would be great to develop a structured framework for that instead of handling special cases in an ad-hoc manner.