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Time Complexity (Big O) simplified:
- When your calculation is not dependent on the input size, it is a constant time complexity (O(1)).
- When the input size is reduced by half, maybe when iterating, handling recursion, or whatsoever, it is a logarithmic time complexity (O(log n)).
- When you have a single loop within your algorithm, it is linear time complexity (O(n)).
- When you have nested loops within your algorithm, meaning a loop in a loop, it is quadratic time complexity (O(n^2)).
- When the growth rate doubles with each addition to the input, it is exponential time complexity (O2^n).
