The term o(n^2) represents a specific class of algorithmic complexity indicating that the growth rate of an algorithm's runtime is less than n squared as the input size increases. This notation is used in the context of analyzing and comparing the efficiency of algorithms, allowing developers to gauge performance and scalability. Understanding o(n^2) helps in recognizing how certain algorithms perform relative to others, especially in terms of time complexity when dealing with larger datasets.
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