this post was submitted on 01 Dec 2023
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Learn Programming
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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