Computer Science BS Journal (CST370) : Week 2

     This week in CST370 we focused on understanding how to analyze and reason about algorithm efficiency. We began with asymptotic notations, which provide a way to describe an algorithm’s performance as input size grows. Notations like Big-O help us focus on long-term behavior rather than exact runtimes, making it easier to compare algorithms.

We then explored the analysis of nonrecursive algorithms, where we evaluate loops and statements to determine time complexity. This reinforced how simple code structures can still have significant performance impacts depending on how often operations are repeated.

Next, we studied the analysis of recursive algorithms, which introduced recurrence relations. Learning how to break a problem into smaller subproblems and analyze how recursive calls contribute to overall complexity helped clarify how recursion affects performance.

Finally, we covered brute force algorithm design, which emphasizes solving problems by exhaustively checking all possible solutions. While brute force methods are often inefficient, they are straightforward, easy to implement, and useful as a baseline for understanding more optimized approaches.

Overall, this week strengthened my ability to evaluate algorithm efficiency and think critically about performance trade-offs. I'm not too excited to be using math because I feel like it takes me longer to understand/remember these formulas. But I'm going to keep trying my best and look forward to what's coming in CST370.