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Comparative Analysis Of Sorting Algorithm

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COMPARATIVE ANALYSIS OF VARIOUS SORTING ALGORITHM
ABSTRACT: Sorting is a commonly used operation in computer science. In addition to its main job, sorting is often required to facilitate some other operation such as searching, merging and normalization. A sorting algorithm consists of comparison, swap, and assignment operations. There are several elementary and advanced sorting algorithms that are being used in practical life as well as in computation such as Quick sort, Bubble sort, Merge sort, Bucket sort, Heap sort, Radix sort etc. But the application of these algorithms depends on the problem statement. This paper introduces MQ sort which combines the advantages of quick sort and Merge sort. The comparative analysis of performance and complexity
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It refers to the arranging of numerical or alphabetical or character data in statistical order (either in increasing order or decreasing order) or in lexicographical order (alphabetical value like addressee key) [1-3]. There are a number of solutions to this problem, known as sorting algorithms. There are several elementary and advanced sorting algorithms. Some sorting algorithms are simple and spontaneous, such as the bubble sort. Others, such as the quick sort are enormously complex, but produce super-fast results. Some sorting algorithm work on less number of elements, some are suitable for floating point numbers, some are good for specific range, some sorting algorithms are used for huge number of data, and some are used if the list has repeated values. Other factors to be considered in choosing a sorting algorithm include the programming effort, the number of words of main memory available, the size of disk or tape units and the extent to which the list is already ordered [4]. That means all sorting Algorithms are problem specific, meaning they work well on some specific problem and do not work well for all the problems. However, there is a direct correlation between the complexity of an algorithm and its relative effectiveness [5]. Many different sorting algorithms have been developed and improved to make sorting fast.
The formal definition of the sorting problem is as follows:
Input: A sequence having n numbers in some random order (a1, a2, a3, ….. an)
Output: A permutation (a’1, a’2, a’3, ….. a’n) of the input sequence such that a’1 ≤ a’2 ≤ a’3 ≤ ….. a’n
For instance, if the given input of numbers is (59, 41, 31, 41, 26, 58), then the output sequence returned by a sorting algorithm will be (26, 31, 41, 41, 58,

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