BIG-O Notation

Programming and program flows complexity

Transcript

1. Big O Notation A simple explanation
2. Algorithm Efficiency + Time Complexity + Space Complexity
3. Time complexity describes the amount of computer time it takes to run a function
4. So, What is Big O Notation? It allows as to talk about how the runtime of the algorithm grows as the inputs grow.
5. Why is important to know this? being aware of the efficiency of an algorithm so programmer can create applications with good performance
7. Constant-Time O(1) + regardless of the complexity (number of items), the time (iterations) is constant. + no loops
8. Other examples: + Pushing to an array + getting an item at a particular index + adding a child element
9. Linear-Time O(N) Worst case time (iterations) grows on par with the number of items.
10. Other examples:
11. Quadratic-Time O(N^2 ) + The time grows exponentially related to the number of inputs. + Nested loops of the same input
12. Logarithmic-Time O(log n) + the most efficient approach when dealing with large collections + Divide-and-conquer strategy + -ideally- find a specific element in a collection of one million elements using less than 20 iterations, if we scale the size of the collection to a billion, we will require only less than 30 iterations.
13. Exponential Time O(2^n) + double the number of operations performed relative to the input size.
14. Factorial Time O(n!) + the most expensive one. Adding a nested loop for every elements.
16. Real World Problem
17. Discussion + Sharing pengalaman / seberapa berguna ini di real project lain? + Kapan udh perlu aware tentang ini? + Ada kasus nyata menarik? ada yg pernah? + Kenapa pas di interview-interview ditanya tentang hal ini, padahal kalau di FE hampir gak mungkin ada data ribuan, biasanya di BE