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Find the maximum of the whole array by comparing the results of 1.1 and 1.2, and place it at the end of the list. (1.3) Sort the entire list (except for the maximum now at the end), recursively. (2) An unoptimized implementation in Haskell (purely functional) may look as follows:
The Sort/Merge utility is a mainframe program to sort records in a file into a specified order, merge pre-sorted files into a sorted file, or copy selected records. Internally, these utilities use one or more of the standard sorting algorithms , often with proprietary fine-tuned code.
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.
Initially, these indices refer to the first element, i.e., are 1. If A[i] < B[j], then the algorithm copies A[i] into C[k] and increases i and k. Otherwise, the algorithm copies B[j] into C[k] and increases j and k. A special case arises if either i or j have reached the end of A or B.
C is the origin, A and B are derivatives of C, and D is the new output version. A three-way merge is performed after an automated difference analysis between a file "A" and a file "B" while also considering the origin, or common ancestor, of both files "C".
Sorting small arrays optimally (in the fewest comparisons and swaps) or fast (i.e. taking into account machine-specific details) is still an open research problem, with solutions only known for very small arrays (<20 elements). Similarly optimal (by various definitions) sorting on a parallel machine is an open research topic.
In computer science, merge sort (also commonly spelled as mergesort and as merge-sort [2]) is an efficient, general-purpose, and comparison-based sorting algorithm.Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output.
The divide-and-conquer technique is the basis of efficient algorithms for many problems, such as sorting (e.g., quicksort, merge sort), multiplying large numbers (e.g., the Karatsuba algorithm), finding the closest pair of points, syntactic analysis (e.g., top-down parsers), and computing the discrete Fourier transform .