Algorithmic Analysis: Asymptotic Complexity

In this video Big O notation is explained as a way to describe the complexity of an algorithm by estimating the time required to complete it based on the size of the input. The worst-case and best-case scenarios are discussed, with the goal of guaranteeing that every solution falls within certain bounds. Different types of functions are introduced, including constant, linear, polynomial, and exponential, which can be used to describe the complexity of an algorithm. Examples are provided, along with a graph illustrating the growth rates of each type. Properties of Big O notation are also discussed, such as how to combine the costs of different instructions and how to handle loops and conditional statements. The importance of focusing on the most significant term in an expression is emphasized.