Time Complexity Deep Dive #3: Asymptotic Analysis Explained

#AsymptoticAnalysis #bigonotation #algorithmanalysis *Summary* In this lecture, we take a deep dive into asymptotic analysis and how it helps us evaluate algorithm efficiency. We explore how it relates to best, worst, and average case analysis, providing a structured way to compare algorithm performance. If you haven’t seen Time Complexity Deep Dive #1 or Time Complexity Deep Dive #2, watch them here: - Time Complexity Deep Dive #1 (https://youtu.be/0DagQComugE) - Time Complexity Deep Dive #2 (https://youtu.be/2iUG_tRlcT4) You'll learn: * The concept of growth of functions in computational complexity * How to conduct asymptotic analysis on algorithms * Key insights from our analysis and why they matter This video is part of an in-depth course on algorithm analysis. Access the full course, quizzes, and coding labs on GitHub: https://github.com/StructuredCS/algorithm-analysis-deep-dive. *Timestamps* 00:00 Overview 00:33 The two dimensions of time complexity analysis 02:14 Growth of functions 03:07 Growth of functions - Upper bound 17:59 Growth of functions - Lower bound 22:05 Growth of functions - Tight bound 26:50 Asymptotic analysis 34:36 Summary