Which algorithms in particular? QuoteInformally, an algorithm can be said to exhibit a growth rate on the order of a mathematical function if beyond a certain input size n, the function f(n) times a positive constant provides an upper bound or limit for the run-time of that algorithm. In other words, for a given input size n greater than some n0 and a constant c, the running time of that algorithm will never be larger than c × f(n). This concept is frequently expressed using Big O notation. For example, since the run-time of insertion sort grows quadratically as its input size increases, insertion sort can be said to be of order O(n²).Big O notation is a convenient way to express the worst-case scenario for a given algorithm, although it can also be used to express the average-case ? for example, the worst-case scenario for quicksort is O(n²), but the average-case run-time is O(n log n).
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on March 6th, 2013, 11:00 pm, edited 1 time in total.