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Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. Big O is a member of a family of notations invented by German mathematicians Paul Bachmann, [1] Edmund Landau, [2] and others, collectively called Bachmann–Landau notation or asymptotic notation.
For typical serial sorting algorithms, good behavior is O(n log n), with parallel sort in O(log 2 n), and bad behavior is O(n 2). Ideal behavior for a serial sort is O(n), but this is not possible in the average case. Optimal parallel sorting is O(log n). Swaps for "in-place" algorithms. Memory usage (and use of other computer resources).
For example, the problem of deciding whether a graph G contains H as a minor, where H is fixed, can be solved in a running time of O(n 2), [25] where n is the number of vertices in G. However, the big O notation hides a constant that depends superexponentially on H.
This is an example of an asymptotic expansion. It is not a convergent series ; for any particular value of n {\displaystyle n} there are only so many terms of the series that improve accuracy, after which accuracy worsens.
Big O notation is an asymptotic measure of function complexity, where () = (()) roughly means the time requirement for an algorithm is proportional to (), omitting lower-order terms that contribute less than () to the growth of the function as grows arbitrarily large.
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For a set of random variables X n and corresponding set of constants a n (both indexed by n, which need not be discrete), the notation = means that the set of values X n /a n converges to zero in probability as n approaches an appropriate limit. Equivalently, X n = o p (a n) can be written as X n /a n = o p (1), i.e.