Big O Notation: Space and Time Complexity

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Introduction

Analysis of the runtime of the algorithm is performed in three ways they are Big O notation, Theta notation, and Omega notation. This article mainly explains Big O notation. It is the worst-case time complexity of the algorithm. It is calculated when huge inputs are given to the computer program. Big O notation is represented in terms of the given input to the algorithm. Generally, as O(N), where N is the size of the input.

The mathematical definition of Big O notation is as follows:

The function f is said to be O(g) (read big-oh of g), where g and f are the functions from the set of natural numbers to itself if there is a constant c > 0 and a natural number n0 such that f(n) ≤ cg(n) for all n ≥ n0.

Big O notation gives the upper bound of the function as shown below:

f(n)= O(g(n)), f there exists a positive integer n0 and a positive constant c, such that f(n)≤c.g(n) ∀ n≥n0.

Examples of Big O time complexity are as follows:

S.No

Algorithm 

Big O time complexity

1

Linear Search

O(N) Runtime grows linearly in nature

2

Binary Search

O(Log N) Runtime grows logarithmically to N

3

Selection Sort

O(N2) Runtime is growing in a polynomial way.

4

Merge  Sort

O(N log N) A super linear algorithm where Runtime grows directly with N.

5

Heap Sort

O(N Log N) A super linear algorithm 

6

Tower of Hanoi 

O(cN) Runtime grows even faster than polynomial algorithm based on N. 

7

Factorial algorithm

O(N!) Runtime grows very fast as compared to all other time complexity and is not considered to be efficient.

It is clearly explained in the below image:

Source: GeeksforGeeks

Conclusion

This article mainly explained Big O notation analysis in detail. Big O notation is the upper bound analysis of the computer program. It is calculated when huge input is given for the algorithm. It is considered to be the worst-case time complexity of the algorithm.

AlgorithmComputer ScienceC++ ProgrammingTime ComplexitySpace Complexity

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