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O(1) is an algorithm that will always execute in the same amount of time regardless of the input.

O(n) is an algorithm that will take longer as larger input sets are provided. The amount of time grows linearly and directly in proportion to the size of the input data.

O(n2) is an algorithm that takes longer in proportion to the square of the size of the input. This is your case. These algorithms work well for small sets of data but take dramatically longer as the input data grows. You typically run into O(n2) algorithms when using nested loops.O(2n) is an algorithm that takes twice as long for each incremental size increase of the input data. These guys grow really quickly and can often be written better.

There are many more but this should give you an idea (as well as answer your immediate question).