Week 61: CST370 - Design and Analysis of Algorithms
WK02: Weekly Reflection This week's module focused on asymptotic notation and the analysis of algorithms, which helped me understand how computer scientists compare solutions beyond just whether they work. I learned that asymptotic notations such as Big O, Big Omega, and Big Theta describe how an algorithm's running time grows as input size increases. This gave me a clearer way to talk about efficiency when comparing algorithms that solve the same problem. One thing that went well this week was learning the difference between informal and formal definitions of time efficiency. The examples made me realize that Big O is not just about counting every single step, but about understanding the overall growth pattern. I also found the exercises helpful because they gave me practice recognizing whether an algorithm grows linearly, quadratically, logarithmically, or differently. I learned how to analyze loops, nested loops, and basic operations to estimate running time. The Big The...