In college in the 1970's, I took a combinatorial algorithms class with Herb Wilf. He explained that choosing a subset of k distinct elements from 1..n uniformly at random was likely k log k, because you needed to detect collisions, and "sorting" k elements was k log k.
(Always check your assumptions. Sorting has an k log k lower bound if all you can do is compare elements. With integers 1..n, one can do much more.)
I showed up at 5pm at his office, as he was bracing himself for the drive from Swarthmore back to Philly, wondering why he ever took this gig. I proposed a linear algorithm: Break 1..n into k bins, and only sort to detect collisions within each bin. Bins would have an average occupancy of 1, so it didn't matter what sort you used.
(I found this idea later in Knuth, and was briefly disillusioned that adults didn't read everything.)
I looked like George Harrison post-Beatles, I probably smelled of pot smoke, and he dismissed my idea. Then my dorm hall phone rang at 10pm, a professor of mine relaying an apology. For that and other ideas, the second edition of his book opened with thanks to me.
I'm surprised now that I realized then that bubble sort actually had the least overhead, in such an application. People don't use Strassen to multiple matrices unless the matrices are really big. For sorting, "really big" is 2 or 3 elements.