Comment by grogers
4 hours ago
Well it is hedged with the word "fancy". I think a charitable reading is to understand the problem domain. If N is always small then trying to minimize the big-O is just showing off and likely counterproductive in many ways. If N is large, it might be a requirement.
Most people don't need FFT algorithm for multiplying large numbers, Karatsuba's algorithm is fine. But in some domains the difference does matter.
Personally I usually see the opposite effect - people first reach for a too-naive approach and implement some O(n^2) algorithm where it wouldn't have even been more complex to implement something O(n) or O(n log n). And n is almost always small so it works fine, until it blows up spectacularly.
> Personally I usually see the opposite effect - people first reach for a too-naive approach and implement some O(n^2) algorithm where it wouldn't have even been more complex to implement something O(n) or O(n log n). And n is almost always small so it works fine, until it blows up spectacularly.
Same. People solve in ways that are very obviously going to cause serious problems in only a few short weeks or months and it’s endlessly frustrating. If you’re building a prototype, fine, but if you’re building for production, very far from fine.
Most frustrating because often there’s next to no cost in selecting and implementing the correct architecture, domain model, data structure, or algorithm up front.