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Merge sort is a divide-and-conquer algorithm that was invented by John von Neumann in 1945. [3] A detailed description and analysis of bottom-up merge sort appeared in a report by Goldstine and von Neumann as early as 1948. [4]
As for time analysis above, analyze the algorithm, typically using space complexity analysis to get an estimate of the run-time memory needed as a function as the size of the input data. The result is normally expressed using Big O notation .
The original merge sort implementation is not in-place and it has a space overhead of N (data size). In-place merge sort implementations exist, but have a high time overhead. In order to achieve a middle term, Timsort performs a merge sort with a small time overhead and smaller space overhead than N.
This is a linear-time, analog algorithm for sorting a sequence of items, requiring O(n) stack space, and the sort is stable. This requires n parallel processors. See spaghetti sort#Analysis. Sorting network: Varies: Varies: Varies: Varies: Varies (stable sorting networks require more comparisons) Yes
This is particularly used in hybrid algorithms, like Timsort, which use an asymptotically efficient algorithm (here merge sort, with time complexity ), but switch to an asymptotically inefficient algorithm (here insertion sort, with time complexity ) for small data, as the simpler algorithm is faster on small data.
Also, when implemented with the "shortest first" policy, the worst-case space complexity is instead bounded by O(log(n)). Heapsort has O(n) time when all elements are the same. Heapify takes O(n) time and then removing elements from the heap is O(1) time for each of the n elements. The run time grows to O(nlog(n)) if all elements must be distinct.
Repeatedly merge sublists to create a new sorted sublist until the single list contains all elements. The single list is the sorted list. The merge algorithm is used repeatedly in the merge sort algorithm. 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 ...
[1]: 226 Since this function is generally difficult to compute exactly, and the running time for small inputs is usually not consequential, one commonly focuses on the behavior of the complexity when the input size increases—that is, the asymptotic behavior of the complexity. Therefore, the time complexity is commonly expressed using big O ...