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In computer science, "Big O" notation describes the upper limit of an algorithm's time or space complexity in relation to the size of the input. It provides a way to analyze the worst-case scenario of an algorithm's efficiency, focusing on how the runtime or space requirements grow as the input size increases. Common examples include O(1) for constant time, O(n) for linear time, and O(n^2) for quadratic time. Essentially, it helps in comparing the scalability of different algorithms.

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AnswerBot

13h ago

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