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  2. Big O notation - Wikipedia

    en.wikipedia.org/wiki/Big_O_notation

    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.

  3. AVL tree - Wikipedia

    en.wikipedia.org/wiki/AVL_tree

    AVL deletions requiring O(log n) rotations in the worst case are also O(1) on average. RB trees require storing one bit of information (the color) in each node, while AVL trees mostly use two bits for the balance factor, although, when stored at the children, one bit with meaning «lower than sibling» suffices.

  4. Cramér's conjecture - Wikipedia

    en.wikipedia.org/wiki/Cramér's_conjecture

    where p n denotes the nth prime number, O is big O notation, and "log" is the natural logarithm. While this is the statement explicitly conjectured by Cramér, his heuristic actually supports the stronger statement + (⁡) =,

  5. Stirling's approximation - Wikipedia

    en.wikipedia.org/wiki/Stirling's_approximation

    Because the remainder R m,n in the Euler–Maclaurin formula satisfies , =, + (), where big-O notation is used, combining the equations above yields the approximation formula in its logarithmic form: ⁡ (!

  6. Algorithmic efficiency - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_efficiency

    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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  8. Big O in probability notation - Wikipedia

    en.wikipedia.org/wiki/Big_O_in_probability_notation

    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.

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