Counterpoint: Stop trying to force Big O into software development - Triplebyte Blog

Editor's note: We started a bit of a discussion with our recent blog about why engineers should embrace and understand Big-O notation in order to best implement algorithms in their work. To bring the other side of this argument to our blog pages, I welcomed in one commenter (a writer and engineer in his own right) to write their case for why Big-O's usefulness in software development – and software developer hiring – is often overplayed. (Oh, and since he prefers the styling Big O, it will henceforth be shown here as such. Works for me!)

This is a companion discussion topic for the original entry at

You don’t need to know Stirling’s formula to see that log(n!) is O(n log n). It’s pretty obvious: the last n/2 terms in the sum are all >= log(n/2), so the sum is >= (n/2) log(n/2) ~ O(n log n).