Unit 2: Introduction to Analysis of Algorithms
In this unit, we explore how we can express an algorithm's efficiency as a function of its input size. The order of growth of running time of an algorithm gives a simple characterization of algorithm's efficiency and allows us to relate performance of alternative algorithms. Asymptotic analysis is based on the idea that as the problem size grows, the complexity will eventually settle down to a simple proportionality to some known function. This idea is incorporated in the "Big Oh", "Big Omega", and "Big Theta" notations for asymptotic performance. These notations are useful for expressing the complexity of an algorithm without getting lost in unnecessary detail.
Completing this unit should take you approximately 9 hours.
2.1: Introduction to Algorithms
2.2: Asymptotic Analysis
2.3: Introduction to Analysis of Algorithms
2.4: Master Theorem