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I know the definitions of both of them, but what is the reason sometimes I see O(1) and other times Θ(1) written in textbooks?
Thanks.
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Big O notation is used for the worst case analysis of an algorithm. Big Omega is used for the best case analysis of an algorithm. Big Theta is used for the analysis of an algorithm when the the best case and worst case analysis is the same.
The notation o(1) means ``a function that converges to 0. '' This means that there is some input size past which the function is always between -0.1 and 0.1; there is some input size past which the function is always between -0.01 and 0.01; and so on.
In short, O(1) means that it takes a constant time, like 14 nanoseconds, or three minutes no matter the amount of data in the set. O(n) means it takes an amount of time linear with the size of the set, so a set twice the size will take twice the time.
What does "Requires Theta(1) operations" mean? It means, that something requires constant amount of operations in the average-case scenario. What is Big Theta? Big Theta notation ( Θ ) is an Asymptotic Notation, which denotes the Average Case Complexity of an algorithm.
O(1) and Θ(1) aren't necessarily the same if you are talking about functions over real numbers. For example, consider the function f(n) = 1/n. This function is O(1) because for any n ≥ 1, f(n) ≤ 1. However, it is not Θ(1) for the following reason: one definition of f(n) = Θ(g(n)) is that the limit of |f(n) / g(n)| as n goes to infinity is some finite value L satisfying 0 < L. Plugging in f(n) = 1/n and g(n) = 1, we take the limit of |1/n| as n goes to infinity and get that it's 0. Therefore, f(n) ≠ Θ(1).
Hope this helps!
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