this post was submitted on 01 Dec 2023
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Learn Programming
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I don't agree that it's useless or misguiding. The smaller dataset, the less important it is, but it makes massive difference how the rest of the algorithm will be working and changing context around it.
Let's say that you need to sort 64 ints, in a code that starts our operating system. You need to sort it once per boot, and you boot less frequently than once per day, in fact you know instances of the OS that have 14 years of uptime, so it doesn't matter at all right? Welp. Now your OS is used by a big cloud provider and they use that code to boot the kernel 13 billions times per day. The context changed, time passed by, your silly bubble sort that doesn't matter on small numbers is still there.
While I get your point, you're still slightly misguided.
Sometimes for a smaller dataset an algorithm with worse asymptomatic complexity can be faster.
Some examples:
Big O notation only considers the largest factor. It is still important to consider the lower order factors in some cases. Assume the theoretical time complexity for an algorithm A is 2nlog(n) + 999999999n and for algorithm B it is n^2 + 7n. Clearly with a small n, B will always be faster, even though B is O(n^2) and A is O(nlog(n)).
Sorting is actually a great example to show how you should always consider what your data looks like before deciding which algorithm to use, which is one of the biggest takeaways I had from my data structures & algorithms class.
This youtube channel also has a fairly nice three-part series on the topic of sorting algorithms: https://youtu.be/_KhZ7F-jOlI?si=7o0Ub7bn8Y9g1fDx
Except the point of this post is that a different sort with worse Big O could be faster with a small dataset.
The fact that you're sorting those 64 ints billions of times simply doesn't matter. The "slower" sort is still faster in practice.
That's why it's important to realize that Big O notation can be useless for small datasets. Because it can actually just be lying to you.
It's actually mathematical. Take any equation:
y = x^2 + x
For large x the squared term dominates. The linear may as well not exists. It's O(x^2). But when x is below 1? Well suddenly that linear term is the more important one! Below 1 it's actually O(x) in practice.