I am going to base this article around a couple of function examples. But firstly, what does Big-O notation do? Big-O Notation will take into account the time your algorithm takes to run and complete as well as the extra space it requires for the process, aka. how efficient is the algorithm?

So, new to Big-O Notation. Here is a basic break down of the start of a journey into Big-O Notation.

I am going to base this article around a couple of function examples. But firstly, what does Big-O notation do?

It is used to show how efficient your algorithm is in its very worst case scenario relative to the input. For example:

Looking at this function. You would assume finding Ben would be quick and easy… and you would be right! There is only one name in the array so it will find it incredibly quickly. BUT we need to take the worst-case scenario relative to the input, so imagine this array contains all the names in the world of which only one person is called Ben and he’s at the very end. Then the time this function takes will be a lot, lot longer.

Big-O Notation will take into account the time your algorithm takes to run and complete as well as the extra space it requires for the process, aka. how efficient is the algorithm?

So our function above’s Big-O Notation is O(n). But how did I figure that out? What is (n)?

(n) represents the potential input. So if the input was 10 then it would be O(10), if our input was 1000 it would be O(1000). Our function is a linear function which means, for every extra element in the input our code has to run one extra time. This graph below explains it:

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