1. Big O: Make it Simple
Determine how complex the algorithm is, in relative to the size of the problem (e.g: List to be sorted)
'O' Stands for 'Order'
- E.g: This algorithm costs an 'order' of X to run
The size of the problem
In sorting list problem, the size of the problem is the length of the list
The Big O expresses how complex is the algorithm in relative to the length of the list to be sorted.
Usually, the length is replaced with 'N'
E.g: O (Log N)

2. Bubble-Sort Example The number of operations needed to sort the
Size = 5
Number of Operations = 24
The number of operations needed to sort the
list with bubble-sort grows much faster than the size of the list!!

3. Big O: Make it Simple - If you double the size of the list;
→ The number of operations = = 4
- If you triple the size of the list;
→ The number of operations = = 9
So, the Big (O) notation becomes : O (N*N)
What happens when the size of the list goes infinity???
What will happen to the number of operations??
2 2
3 2

4. Big O: Made it Simple

5. Big O: Make it Simple

6. Big O: Make it Simple

7. Big O: Make it Simple

8. Big O: Make it Simple

9. Big O: Make it Simple

10. Big O: Make it Simple