Unravel the Code! 2025 Algorithms Analysis Test – Ace It Like a Pro!

Big O notation is specifically used to describe the upper bound of an algorithm's time complexity, which means it focuses on the worst-case scenario for how the algorithm performs as the input size grows. This notation allows us to evaluate the maximum time required by an algorithm, ensuring that no matter the input, the running time will not exceed a certain function of the input size.

For instance, if an algorithm has a time complexity of \( O(n^2) \), it means that the running time will not exceed a quadratic function of the input size \( n \) in the worst case. This characterization is particularly useful for understanding the performance limits of algorithms, especially when analyzing their efficiency under demanding conditions.

In contrast, Big Θ notation represents both the upper and lower bounds, characterizing an algorithm's behavior asymptotically in both the best and worst cases. Big Ω notation describes the lower bound, focusing on the best-case scenario, while little o notation provides a way to express a function that grows slower than another function but does not offer a complete understanding of the worst-case performance. Therefore, Big O notation is the correct choice for describing the worst-case scenario for an algorithm's time complexity.