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  2. In-place algorithm - Wikipedia

    en.wikipedia.org/wiki/In-place_algorithm

    Identifying the in-place algorithms with L has some interesting implications; for example, it means that there is a (rather complex) in-place algorithm to determine whether a path exists between two nodes in an undirected graph, [3] a problem that requires O(n) extra space using typical algorithms such as depth-first search (a visited bit for ...

  3. Selection sort - Wikipedia

    en.wikipedia.org/wiki/Selection_sort

    In computer science, selection sort is an in-place comparison sorting algorithm.It has a O(n 2) time complexity, which makes it inefficient on large lists, and generally performs worse than the similar insertion sort.

  4. Sorting algorithm - Wikipedia

    en.wikipedia.org/wiki/Sorting_algorithm

    In particular, some sorting algorithms are "in-place". Strictly, an in-place sort needs only O(1) memory beyond the items being sorted; sometimes O(log n) additional memory is considered "in-place". Recursion: Some algorithms are either recursive or non-recursive, while others may be both (e.g., merge sort).

  5. Fisher–Yates shuffle - Wikipedia

    en.wikipedia.org/wiki/Fisher–Yates_shuffle

    The algorithm produces an unbiased permutation: every permutation is equally likely. The modern version of the algorithm takes time proportional to the number of items being shuffled and shuffles them in place. The Fisher–Yates shuffle is named after Ronald Fisher and Frank Yates, who first described it.

  6. In-place matrix transposition - Wikipedia

    en.wikipedia.org/wiki/In-place_matrix_transposition

    To use only O(M+N) or even O(log MN) auxiliary storage, more-complex algorithms are required, and the known algorithms have a worst-case linearithmic computational cost of O(MN log MN) at best, as first proved by Knuth (Fich et al., 1995; Gustavson & Swirszcz, 2007). Such algorithms are designed to move each data element exactly once.

  7. Flashsort - Wikipedia

    en.wikipedia.org/wiki/Flashsort

    Flashsort is an efficient in-place implementation of histogram sort, itself a type of bucket sort. It assigns each of the n input elements to one of m buckets, efficiently rearranges the input to place the buckets in the correct order, then sorts each bucket. The original algorithm sorts an input array A as follows:

  8. Cycle sort - Wikipedia

    en.wikipedia.org/wiki/Cycle_sort

    The following Python implementation [1] [circular reference] performs cycle sort on an array, counting the number of writes to that array that were needed to sort it. Python def cycle_sort ( array ) -> int : """Sort an array in place and return the number of writes.""" writes = 0 # Loop through the array to find cycles to rotate.

  9. Binary heap - Wikipedia

    en.wikipedia.org/wiki/Binary_heap

    Binary heaps are also commonly employed in the heapsort sorting algorithm, which is an in-place algorithm because binary heaps can be implemented as an implicit data structure, storing keys in an array and using their relative positions within that array to represent child–parent relationships.