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  2. Prüfer sequence - Wikipedia

    en.wikipedia.org/wiki/Prüfer_sequence

    For instance, in pseudo-code: Convert-Prüfer-to-Tree(a) 1 n ← length[a] 2 T ← a graph with n + 2 isolated nodes, numbered 1 to n + 2 3 degree ← an array of integers 4 for each node i in T do 5 degree[i] ← 1 6 for each value i in a do 7 degree[i] ← degree[i] + 1

  3. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    As exchanging the indices of an array is the essence of array transposition, an array stored as row-major but read as column-major (or vice versa) will appear transposed. As actually performing this rearrangement in memory is typically an expensive operation, some systems provide options to specify individual matrices as being stored transposed.

  4. Heapsort - Wikipedia

    en.wikipedia.org/wiki/Heapsort

    The heapsort algorithm can be divided into two phases: heap construction, and heap extraction. The heap is an implicit data structure which takes no space beyond the array of objects to be sorted; the array is interpreted as a complete binary tree where each array element is a node and each node's parent and child links are defined by simple arithmetic on the array indexes.

  5. Insertion sort - Wikipedia

    en.wikipedia.org/wiki/Insertion_sort

    The best case input is an array that is already sorted. In this case insertion sort has a linear running time (i.e., O(n)). During each iteration, the first remaining element of the input is only compared with the right-most element of the sorted subsection of the array. The simplest worst case input is an array sorted in reverse order.

  6. Linked list - Wikipedia

    en.wikipedia.org/wiki/Linked_list

    Growing a large array when it is full may be difficult or impossible, whereas finding space for a new linked list node in a large, general memory pool may be easier. Adding elements to a dynamic array will occasionally (when it is full) unexpectedly take linear (O(n)) instead of constant time (although it is still an amortized constant).

  7. Selection sort - Wikipedia

    en.wikipedia.org/wiki/Selection_sort

    Selection sort is not difficult to analyze compared to other sorting algorithms, since none of the loops depend on the data in the array. Selecting the minimum requires scanning n {\displaystyle n} elements (taking n − 1 {\displaystyle n-1} comparisons) and then swapping it into the first position.

  8. Bitonic sorter - Wikipedia

    en.wikipedia.org/wiki/Bitonic_sorter

    Bitonic mergesort is a parallel algorithm for sorting. It is also used as a construction method for building a sorting network.The algorithm was devised by Ken Batcher.The resulting sorting networks consist of (⁡ ()) comparators and have a delay of (⁡ ()), where is the number of items to be sorted. [1]

  9. Bubble sort - Wikipedia

    en.wikipedia.org/wiki/Bubble_sort

    Take an array of numbers "5 1 4 2 8", and sort the array from lowest number to greatest number using bubble sort. In each step, elements written in bold are being compared. Three passes will be required; First Pass ( 5 1 4 2 8 ) → ( 1 5 4 2 8 ), Here, algorithm compares the first two elements, and swaps since 5 > 1.