Prerequisite – Asymptotic Notations, Properties of Asymptotic Notations **1. ****Big Oh notation (O)**** :**

Big oh notation is used to describe asymptotic upper bound.

Mathematically, if f(n) describe running time of an algorithm; f(n) is O(g(n)) if there exist positive constant C and no such that

0 <=f(n) <= c g(n) for all n>=n0

n = used to give upper bound an a function.

If a function is O(n), it is automatically O(n-square) as well !

Graphic example for Big oh (O) :

**2. Big Omega notation (Ω) :**

Just like O notation provide an asymptotic upper bound, Ω notation provides asymptotic lower bound.

Let f(n) define running time of an algorithm;

f(n) is said to be Ω(g (n)) if there exists positive constant C and (n 0 ) such that

O<= C g(n) <= f(n) for all n>=n 0

n= used to given lower bound on a function

If a function is O(n-square ) it is automatically O(n) as well.

Graphical example for Big Omega (Ω) :

**3. Big Theta notation (Θ) :**

Let f(n) define running time of an algorithm.

f(n) is said to be Θ(g(n)) if f(n) is O(g(n)) and f(n) is Ω(g(n))

Mathematically,

O<=f(n)<=C 1 g(n) for n>=n 0 O<= C 2 g(n)<=f(n) for n >=n 0

Merging both the equation, we get :

O<=C 2 g(n)<=f(n)<=C 1 g(n) for n>=n 0

The equation simply means there exist positive constants C 1 and C 2 such that f(n) is sandwich between C 2 g(n) and C 1 g(n).

Graphic example of Big Theta (Θ) :

**Difference Between Big oh, Big Omega and Big Theta :**

S.No. | Big Oh | Big Omega | Big Theta |
---|---|---|---|

1. | It is like <= rate of growth of an algorithm is less than or equal to a specific value | It is like >= rate of growth is greater than or equal to a specified value | It is like == meaning the rate of growth is equal to a specified value |

2. | The upper bound of algorithm is represented by Big O notation. Only the above function is bounded by Big O. asymptotic upper bond is it given by Big O notation. | The algorithm’s lower bound is represented by Omega notation. The asymptotic lower bond is given by Omega notation | The bounding of function from above and below is represented by theta notation. The exact asymptotic behavior is done by this theta notation. |

3. | Big oh (O) – Worst case | Big Omega (Ω) – Best case | Big Theta (Θ) – Average case |

4. | Big-O is a measure of the longest amount of time it could possibly take for the algorithm to complete. | Big- Ω is take a small amount of time as compare to Big-O it could possibly take for the algorithm to complete. | Big- Θ is take very short amount of time as compare to Big-O and Big-? it could possibly take for the algorithm to complete. |

5. | Mathematically – Big Oh is 0 <=f(n) <= c g(n) for all n>=n0 | Mathematically – Big Omega is O<= C g(n) <= f(n) for all n>=n 0 | Mathematically – Big Theta is O<=C 2 g(n)<=f(n)<=C 1 g(n) for n>=n 0 |

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