The term o(n) represents an upper bound on the growth rate of a function in algorithm analysis, indicating that a function grows slower than a linear function as the input size, n, increases. This concept is crucial for understanding the efficiency of algorithms and their performance in relation to the size of the input data. It helps categorize algorithms based on how their execution time or space requirements increase with larger datasets, particularly in the context of various sorting techniques and data structures.
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