[Followups-to set since I don't think sci.math.num-analysis is topical anymore.]
Ralph Hartley <firstname.lastname@example.org> writes:
> On 07/24/2012 06:02 PM, Ben Bacarisse wrote: >> What's going on here? If there were any down side at to using n/2 I can >> see why one might consider alternatives -- even random ones (some people >> do for quicksort, partitioning for example) --- but since n/2 is simple, >> easy to calculate and optimal you can't be proposing an alternative. > > For searching a sorted list, I agree it doesn't make much sense, but > consider sorting. > > Quicksort is a simple algorithm, and is close to O(n log(n)) for most > inputs, but you can't use it when the input is chosen by an adversary > because in worst case it is O(n^2). > > It is reasonable to use quicksort with each pivot point chosen > randomly from a uniform distribution over the list. Unlike search, > there is no consistently good choice, and any deterministic rule could > be exploited to give O(n^2) cost *every* time.
That's not true. You can pick the median in O(n) time and that means (if you're careful about a few other details) that you can have a deterministic quicksort that is O(n log(n)) in the worst case. It's not really practical, but that's not the point.
> Worst case is *still* O(n^2), but that's ok. What you really care > about is that the average over your random choices is O(n log(n)), > even for the worst possible input.
Depends on who "you" refers to! People interested in complexity theory (this is comp.theory after all) are more likely to be interested in worst-case analysis. This is not to deny the large body of theory on probabilistic analysis -- it's just not the majority concern for complexity theorists.
> I think you can actually say something slightly stronger than that, > and show that the probability of very bad cases is small.
Yes, I think so too, but I don't know if there is an agreed measure for "bad cases are sufficiently rare". The one paper cited in this thread, proposed one, but I had trouble getting me head around it.
> It's OK to assume that your random choices follow a known > distribution, you can make them so, but it can be a disaster to assume > that the input array is anything but worse case. > > There are sorting algorithms with better worst case, but they are > often not worth the trouble. > > None of this is *remotely* new.
No, but probabilistic measures of "almost always" O(f) are newer (if not actually new). I'd be interested to hear from anyone who know what recent thinking is about such things.