Big O notation

Before you dive into algorithms, it is important to understand what this means. It sounds fancy, but in fact, it’s really easy to understand this concept.

Big O notation does not directly indicate the exact time an algorithm takes. Instead, it says how the time complexity of the algorithm will grow as the amount of data on input increases

What is the complexity of this code?

It’s O(2n) right? Wait a minute…

We don’t care about constants and small numbers because what matters to us is how much the algorithm will grow. Let’s take a look at the example.

We have two algorithms. In this case, N represents the number of elements on the input.

You can see from the picture that in the first case, 2 becomes unimportant, and in the second case, N becomes a significantly smaller number compared to the square.

Thank you for taking the time to read my article. I appreciate your interest in the topic and hope that it was informative. If you have any feedback or comments on the article, please don’t hesitate to share them with me.

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