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  2. General number field sieve - Wikipedia

    en.wikipedia.org/wiki/General_number_field_sieve

    When using such algorithms to factor a large number n, it is necessary to search for smooth numbers (i.e. numbers with small prime factors) of order n 1/2. The size of these values is exponential in the size of n (see below). The general number field sieve, on the other hand, manages to search for smooth numbers that are subexponential in the ...

  3. AVL tree - Wikipedia

    en.wikipedia.org/wiki/AVL_tree

    The time required is O(log n) for lookup, plus a maximum of O(log n) retracing levels (O(1) on average) on the way back to the root, so the operation can be completed in O(log n) time. Set operations and bulk operations

  4. Splay tree - Wikipedia

    en.wikipedia.org/wiki/Splay_tree

    A splay tree is a binary search tree with the additional property that recently accessed elements are quick to access again. Like self-balancing binary search trees, a splay tree performs basic operations such as insertion, look-up and removal in O(log n) amortized time.

  5. Exponential search - Wikipedia

    en.wikipedia.org/wiki/Exponential_search

    The asymptotic runtime does not change for the variations, running in O(log i) time, as with the original exponential search algorithm. Also, a data structure with a tight version of the dynamic finger property can be given when the above result of the k-nested binary search is used on a sorted array. [4]

  6. Nearest neighbor search - Wikipedia

    en.wikipedia.org/wiki/Nearest_neighbor_search

    For constant dimension query time, average complexity is O(log N) [6] in the case of randomly distributed points, worst case complexity is O(kN^(1-1/k)) [7] Alternatively the R-tree data structure was designed to support nearest neighbor search in dynamic context, as it has efficient algorithms for insertions and deletions such as the R* tree. [8]

  7. Sorting algorithm - Wikipedia

    en.wikipedia.org/wiki/Sorting_algorithm

    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).

  8. Introsort - Wikipedia

    en.wikipedia.org/wiki/Introsort

    Introsort or introspective sort is a hybrid sorting algorithm that provides both fast average performance and (asymptotically) optimal worst-case performance. It begins with quicksort, it switches to heapsort when the recursion depth exceeds a level based on (the logarithm of) the number of elements being sorted and it switches to insertion sort when the number of elements is below some threshold.

  9. Batcher odd–even mergesort - Wikipedia

    en.wikipedia.org/wiki/Batcher_odd–even_mergesort

    Batcher's odd–even mergesort [1] is a generic construction devised by Ken Batcher for sorting networks of size O(n (log n) 2) and depth O((log n) 2), where n is the number of items to be sorted. Although it is not asymptotically optimal, Knuth concluded in 1998, with respect to the AKS network that "Batcher's method is much better, unless n ...