Introduction to Asymptotic Analysis and Big O
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.
One observation that helps us is that we want to worry about large input sizes only. If the input size is really small, how bad can a poorly-designed algorithm get, right? Mathematicians have a tool for this sort of analysis called the asymptotic notation. The asymptotic notation compares two functions, say, and for very large values of . This fits in nicely with our need for comparing algorithms for very large input sizes.
Big O notation
One of the asymptotic notations is the Big O notation. A function is considered , read as big oh of , if there exists some positive real constant and an integer , such that the following inequality holds for all :
The following graph shows a plot of a function and that demonstrates this inequality.