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An example merge sort is given in the illustration. It starts with an unsorted array of 7 integers. The array is divided into 7 partitions; each partition contains 1 element and is sorted. The sorted partitions are then merged to produce larger, sorted, partitions, until 1 partition, the sorted array, is left.
Merge two-record sublists from C and D into four-record sublists; writing these alternately to A and B. Merge four-record sublists from A and B into eight-record sublists; writing these alternately to C and D; Repeat until you have one list containing all the data, sorted—in log 2 (n) passes.
It continues doing this for each pair of adjacent elements to the end of the data set. It then starts again with the first two elements, repeating until no swaps have occurred on the last pass. [34] This algorithm's average time and worst-case performance is O(n 2), so it is rarely used to sort large, unordered data sets. Bubble sort can be ...
A further relaxation requiring only a list of the k smallest elements, but without requiring that these be ordered, makes the problem equivalent to partition-based selection; the original partial sorting problem can be solved by such a selection algorithm to obtain an array where the first k elements are the k smallest, and sorting these, at a total cost of O(n + k log k) operations.
An early two-subproblem D&C algorithm that was specifically developed for computers and properly analyzed is the merge sort algorithm, invented by John von Neumann in 1945. [ 7 ] Another notable example is the algorithm invented by Anatolii A. Karatsuba in 1960 [ 8 ] that could multiply two n - digit numbers in O ( n log 2 3 ...
Merge-insertion sort also performs fewer comparisons than the sorting numbers, which count the comparisons made by binary insertion sort or merge sort in the worst case. The sorting numbers fluctuate between n log 2 n − 0.915 n {\displaystyle n\log _{2}n-0.915n} and n log 2 n − n {\displaystyle n\log _{2}n-n} , with the same leading ...
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Suppose that such an algorithm existed, then we could construct a comparison-based sorting algorithm with running time O(n f(n)) as follows: Chop the input array into n arrays of size 1. Merge these n arrays with the k-way merge algorithm. The resulting array is sorted and the algorithm has a running time in O(n f(n)).