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Introduction to Asymptotic Analysis and Big (O)

Explore the core concepts of asymptotic analysis and Big O notation to understand how to compare algorithm complexities for large input sizes. This lesson helps you simplify and apply Big O notation, enabling you to evaluate and optimize algorithm performance in Java coding interviews.

We have seen that the time complexity of an algorithm can be expressed as a polynomial. To compare two algorithms, we can compare the respective polynomials. However, the analysis performed in the previous lessons is a bit cumbersome and would become intractable for bigger algorithms that we tend to encounter in practice.

Asymptotic analysis

If the input size is really small, how bad can a poorly designed algorithm get, right? Therefore, mathematicians have a tool for this sort of analysis called the asymptotic notation. The asymptotic notation compares two functions say, f(n)f(n) and g(n)g(n) for very large values of nn ...