S.No Term Called As Definition
1 O(1) Constant Time Time taken for an operation, fixed number of steps
E.g. Push and Pop operations for the stack.
Enqueue and Deque operations for Queue.
2 O(log n) Logarithmic Time Time was taken will double with each additional element in the input data set
E.g. Binary Search
Insert and Find operations in a Binary Search tree.
Insert and Remove operations for a heap
3 O(n) Linear Time A number of steps proportional to the size of the tasks.
(If the size of the task increases then no of steps increases)
E.g. Finding Max/Min element in a list.
Sequential search in an unsorted list of n elements.
Traversal of a tree with n nodes.
Calculating iteratively n-factorial.
Finding iteratively the nth Fibonacci number
4 O(n log n) Linean Arithmetic

You are performing an O(log n) operation for each item in your input.

Also called as Log-Linear, or QuasiLinear

Most (efficient) sort algorithms are an example of this.
O(n log n) . "n log n" time
E.g. Quick Sort, Merge Sort
5 O(n2) Quadratic Time The number of operations is proportional to the size of the task squared.
E.g. Selection Sort of n elements.
Comparing two-dimensional arrays of size n by n.
Finding duplicates in an unsorted list of n elements
(Implemented with two nested loops)
6 2O(n) Exponential Time Which is common for artificial intelligence tasks and is really quite bad. Exponential-time algorithms begin to run the risk of having a decent-sized input not finish before the person wanting the result retires.
7 O(n!) Factorial time  

 

Run time matrix for each Big O Term:

 

Input Constant Logarithmic Linear Linear Arithmatic Quadratic Cubic
N O(1) O(log N) O(N) O(N log N) O(N2) O(N3)
1 1 1 1 1 1 1
2 1 1 2 2 4 8
4 1 2 4 8 16 64
8 1 3 8 24 64 512
16 1 4 16 64 256 4,096
1024 1 10 1,024 10,240 1,048,576 1,073,741,824