Lecture
This lecture covers the concept of algorithmic complexity, focusing on the worst-case time complexity as the number of basic instructions used by an algorithm. It explores how to compare algorithms based on their complexity evolution with input size, analyzing asymptotic orders of magnitude and the dominant term. The lecture introduces the Theta notation for comparing algorithms' growth rates and characterizes different complexity classes, such as constant, logarithmic, linear, quasi-linear, polynomial, and exponential. It also explains the comparison between functions using the Theta notation, highlighting the significance of understanding algorithmic complexity for analyzing and designing efficient algorithms.