Asymptotic bounds

Asymptotic bounds are ways to describe how an algorithm's performance grows as the size of its input increases. They provide a big-picture view, focusing on actual growth trends rather than exact times. For example, Big O notation shows the worst-case scenario, helping us understand how quickly an algorithm might slow down as data gets larger. These bounds are useful for comparing algorithms and predicting their behavior, especially for very large datasets, enabling better decisions in designing efficient computational solutions.