Chapter 1.2 Asymptotic and Big-O Complexity

One of the key concepts in the study of algorithms is asymptotic notation and big-O complexity. Asymptotic notation is used to describe the growth of a function or algorithm as the input size increases. The most commonly used asymptotic notation is big-O notation, which provides an upper bound on the growth of a function.

Big-O complexity is a measure of the efficiency of an algorithm, taking into account the size of the input and the amount of computational resources required. Algorithms with lower big-O complexity are generally considered to be more efficient, as they require less computational resources and have better scalability.

In this course, you will learn about asymptotic notation and big-O complexity, and how to use these concepts to analyze and compare algorithms.