[dahart]

> In my “real world” we normally don’t care about things like big-O complexity. We worry about doing dumb things and not taking more time than we have available.

Big-O is just a formal word for how much time you’re going to take, a way to figure out if you’re likely to take more time than available. So, it sounds like you do care about it, you’re just saying it doesn’t matter if you’re formal about it?

If your team’s n^2 worst case really is in the noise, then you’re absolutely right, the senior is prematurely optimizing.

But without more context, I have to assume there could be reasons your senior “nerd” might be right. Is there a noticeable difference to the user in run time with the largest n you can possibly have? Is the other team you’re delivering to going to call your n^2 routine from one of their own, one that has it’s own O(n) or O(n^2) outer loop?

I’d say big-O notation isn’t difficult, doesn’t take long to learn, doesn’t require school, and knowledge of it doesn’t make someone a wizard. It’s easy. It’s a low enough bar, that I do expect everyone on my team to feel comfortable looking at a function with loops and being able to tell whether something is O(n^2) or O(n^3).

The difficult cases are determining complexity of an architecture full of lots of interconnecting O(1..n) functions, when the complexity isn’t all in one place. I watch unnecessary O(n^3) algorithms get made even on teams of people who all know big-O.

[rafiki6]

> Big-O is just a formal word for how much time you’re going to take, a way to figure out if you’re likely to take more time than available

This is a common misunderstanding about big-O. It's not about how much time you're gonna take but it's actually a measure of how complexity affects time growth as the data grows.

"Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. "

From: https://en.wikipedia.org/wiki/Big_O_notation

I generally don't like wikipedia, but they got this one right.

[dahart]

The quote from Wikipedia is factually correct, but it seems you misunderstood my comment.

> It’s not about how much time you’re gonna take

It most definitely is about predicting running time, or memory consumption. Big-O is literally a straightforward way to write the formula for best/avg/worst case time or memory of a program, modulo a constant. The second part of your sentence contradicts the first part.

I’m not sure I buy that there’s any common misconception about Big-O either, I’ve never seen much evidence of that. Anyone who’s learned Big-O in school, or has used it at work, or read past the first sentence on Wikipedia, or commented about it on HN, knows what Big-O means and that for small n, O(n) can be bigger than O(n^2). It’s just not that tricky.

I like Wikipedia a lot, and I agree this one’s right. Why do you dislike it, and why is that relevant?

[caspar]

I'm not the person you're replying to, but it seems to me that there may be an example of a common misconception about Big-O in your comment here:

> Big-O is literally a straightforward way to write the formula [describing] best/avg/worst case time or memory of a program

Technically, Big-O notation should only be used to provide an upper bound on the best/avg/worst case[0]. And typically people only use it to discuss the worse case or average case, just because talking about the upper bound on an algorithm when its inputs are restricted to its best possible inputs is typically much less useful.

But providing an upper bound is not quite "writing the formula" (giving a complete picture), because you haven't specified the lower bound (Big/Little-Omega) - very loosely speaking, if you only ever talk in terms of Big/Little[1]-O, you've only provided half the picture. Of course to be fair, Big-O of an algorithm is the side of the picture that is way more likely to bite you in production!

Disclaimer: not a mathematician, so salt to taste :)

0: https://stackoverflow.com/a/12338937/775982 1: https://en.wikipedia.org/wiki/Big_O_notation#Family_of_Bachm...

[dahart]

Ugh.

The “worst case” is the part referring to the upper bound. Big-O is referring to the “order” of the run time, meaning the largest term in the formula for large n.

Again, everyone knows this if they know Big-O at all. I am intentionally not being picky and pedantic with my words, because the context of this thread was people complaining about unnecessary formality and unnecessary and unhelpful over-concern about academic correctness rather than practicality. There is no widespread misconception, but there are people who like to wax philosophical and try to demonstrate their superior knowledge...

My side intention was to detail why @rafiki6 might have been incorrect without knowing it, since they claimed correctness and complained about downvotes.

[rafiki6]

Not entirely sure why I am getting downvoted here. It's factually correct and relevant to the discussion. I'm starting to think someone is actively trying to downvote my comments.