Last Updated on August 29, 2023 by Mayank Dham
Asymptotic notation is a fundamental concept in computer science and mathematics that allows us to describe the behavior of algorithms and functions as their input size approaches infinity. Asymptotic notation provides a simplified way to analyze and compare the efficiency of algorithms, focusing on their growth rates without being concerned with constant factors and lowerorder terms. In this article, we delve into the properties of asymptotic notation, namely Big O, Omega, and Theta notation. By understanding these properties, we can gain valuable insights into algorithm performance and make informed decisions when designing or analyzing algorithms.
Best Case − Minimum time for the execution.
Average Case − Average time for the execution.
Worst Case − Maximum time for the execution.
Asymptotic notations are mathematical tools to express the time complexity of algorithms for asymptotic analysis.
Note: if there’s no input to the algorithm, Then it is considered to work in a constant time. Other than the "input" all other factors are considered to be constant.
Three Main Asymptotic Notation
 Ο Big Oh Notation
 Ω OmegaNotation
 θ Theta Notation
Big O Asymptotic Notation, Ο
The Asymptotic Notation Ο(n) represents the upper bound of an algorithm’s running time. It measures or calculates the worstcase time complexity or the maximum or longest amount of time an algorithm can possibly take to complete. For example, O(log n) represents a binary search algorithm’s upper bound.
g(n) is an Asymptotic upper bound for f(n)
O(g(n))={f(n): there exist a positive constants c and No such that 0<=f(n) <=cg(n)for all n>=No
Omega Asymptotic Notation, Ω
The Omega Asymptotic Notation Ο(n) represents the lower bound of an algorithm’s running time. It measures the bestcase time complexity or the minimum amount of time an algorithm can possibly take to complete. example, a Bubble Sort algorithm has a running time of Ω(N) because in the bestcase scenario, the list is already sorted, and the bubble sort will terminate after the first iteration.
g(n) is an asymptotic lower bound for f(n)
Ω(f(n)) ≥ { g(n) : there exists c > 0 and n0 such that g(n) ≤ c.f(n) for all n > n0. }
Theta Asymptotic Notation, θ
The Theta Asymptotic Notation Ο(n) represents the both lower bound and upper bound of an algorithm’s running time. It measures the average case of time complexity. When we use bigΘ notation, we’re saying that we have an asymptotically tight bound on the running time.
g(n) is an asymptotic tight bound for f(n)
The function f is said to be Θ(g), if there are constants c1, c2 > 0 and a natural number n0 such that c1 g(n) ≤ f(n) ≤ c2 g(n) for all n ≥ n0
Properties of Asymptotic Notation:

General Properties:
If f(n) is O(g(n)) and k is constant then k*f(n) is also O(g(n)). 
Transitive Properties:
If g(n) is O(h(n)) and f(n) is O(g(n)) then f(n) = O(h(n)). 
Reflexive Properties:
If f(n) is given then f(n) is O(f(n)). Since the max value of f(n) will be f(n) itself
Hence x = f(n) and y = O(f(n) tie themselves in reflexive relation always. 
Symmetric Properties:
If f(n) is Θ(g(n)) then g(n) is Θ(f(n)). 
Transpose Symmetric Properties:
If f(n) is O(g(n)) then g(n) is Ω (f(n)). 
Some More Properties:
 If f(n) = O(g(n)) and f(n) = Ω(g(n)) then f(n) = Θ(g(n))
 If f(n) = O(g(n)) and d(n)=O(e(n)) then f(n) + d(n) = O( max( g(n), e(n) ))
Conclusion:
In the realm of algorithm analysis, the properties of asymptotic notation—Big O, Omega, and Theta—serve as indispensable tools for quantifying the efficiency of algorithms as input sizes become large. By abstracting away constant factors and lowerorder terms, these notations allow us to focus on the fundamental growth rates of algorithms. Big O notation helps us establish upper bounds on an algorithm’s runtime, Omega notation provides lower bounds, and Theta notation offers a tight range of growth rates. Armed with these notations, we can make informed decisions about algorithm selection, optimization, and scalability, contributing to the development of more efficient and effective software solutions.
FAQ (Frequently Asked Questions) Related to asymptotic notation
Here are some FAQs related to asymptotic notation.
Q1: What is asymptotic notation?
Asymptotic notation is a mathematical framework used to analyze the performance of algorithms and functions as their input size approaches infinity. It abstracts away constant factors and lowerorder terms, focusing on the fundamental growth rates of these algorithms and functions.
Q2: What is Big O notation?
Big O notation, often denoted as O(), describes the upper bound or worstcase behavior of an algorithm. It provides an estimation of how an algorithm’s runtime grows in relation to the input size, ignoring constant factors and lowerorder terms.
Q3: What is Omega notation?
Omega notation, represented as Ω(), signifies the lower bound or bestcase behavior of an algorithm. It indicates the minimum rate at which the algorithm’s runtime will grow with increasing input size.
Q4: What is Theta notation?
Theta notation, denoted as Θ(), combines both upper and lower bounds to provide a tight range of growth rates for an algorithm. It gives a more precise description of an algorithm’s performance than Big O or Omega notation alone.
Q5: Why are asymptotic notations important?
Asymptotic notations provide a standardized way to compare and analyze algorithms’ efficiency, making it easier to assess their scalability and performance characteristics. They help us focus on the core trends in algorithm behavior as input sizes become large, which is essential for designing robust and efficient software.