Algorithm Basics
Rod Stephens · 2019
This chapter explains some of the basic algorithmic concepts one should understand if he/she wants to get the most out of his/her study of algorithms. To study an algorithm's performance, computer scientists ask how its performance changes as the size of the problem changes. To get a feeling for how problem size relates to performance, computer scientists use Big O notation. Big O notation uses a function to describe how the algorithm's worst-case performance relates to the problem size as the size grows very large. The function is written within parentheses after a capital letter O. Although theoretical behavior is important in understanding an algorithm's run time behavior, practical considerations also play an important role in real-world performance for several reasons. The analysis of an algorithm typically considers all steps as taking the same amount of time even though that may not be the case.