This is actually a really interesting point which, I find, many junior engineers gloss over when reciting lower bounds for sorting problems. It fully depends on the problem in question!

Your example of Array[number] = true (or the more simplified version of Array[number] = number) is can be kind of practical, even when you extend it to be sorting k unique numbers from 1 N. This "counting-sort" can be thought of as a special case of radix-sort, which itself can be thought of a special case for the class of probabilistic sorting algorithms (an example of which is bucket sort) where you know some information about the set being sorted.

Basically, the theorem which places the lower-bound on sorting algorithms to be O(nlogn) is based on the assumption that the set being sorted is any totally ordered set. The "any" part of that assumption means you cannot assume prior knowledge about the set. But, if that assumption is invalid (which it is most of the time - how often in practice do you know nothing about the data you are working with) you can produce a sorting algorithm which works better than O(nlogn).

## re: Sleep Sort: Where Theory meets Sobering Reality VIEW POST

TOP OF THREAD FULL DISCUSSIONThis is actually a really interesting point which, I find, many junior engineers gloss over when reciting lower bounds for sorting problems. It fully depends on the problem in question!

Your example of Array[number] = true (or the more simplified version of Array[number] = number) is can be kind of practical, even when you extend it to be sorting k unique numbers from 1 N. This "counting-sort" can be thought of as a special case of radix-sort, which itself can be thought of a special case for the class of probabilistic sorting algorithms (an example of which is bucket sort) where you know some information about the set being sorted.

Basically, the theorem which places the lower-bound on sorting algorithms to be O(nlogn) is based on the assumption that the set being sorted is any totally ordered set. The "any" part of that assumption means you cannot assume prior knowledge about the set. But, if that assumption is invalid (which it is most of the time - how often in practice do you know nothing about the data you are working with) you can produce a sorting algorithm which works better than O(nlogn).