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In computer science, integer sorting is the algorithmic problem of sorting a collection of data values by integer keys. Algorithms designed for integer sorting may also often be applied to sorting problems in which the keys are floating point numbers, rational numbers, or text strings. [1]
Shuffling can also be implemented by a sorting algorithm, namely by a random sort: assigning a random number to each element of the list and then sorting based on the random numbers. This is generally not done in practice, however, and there is a well-known simple and efficient algorithm for shuffling: the Fisher–Yates shuffle .
Timsort is a hybrid, stable sorting algorithm, derived from merge sort and insertion sort, designed to perform well on many kinds of real-world data. It was implemented by Tim Peters in 2002 for use in the Python programming language. The algorithm finds subsequences of the data that are already ordered (runs) and uses them to sort the ...
If the sort key values are totally ordered, the sort key defines a weak order of the items: items with the same sort key are equivalent with respect to sorting. See also stable sorting. If different items have different sort key values then this defines a unique order of the items. Workers sorting parcels in a postal facility
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.
An example is adaptive heap sort, a sorting algorithm based on Cartesian trees. It takes time O ( n log k ) {\displaystyle O(n\log k)} , where k {\displaystyle k} is the average, over all values x {\displaystyle x} in the sequence, of the number of times the sequence jumps from below x {\displaystyle x} to above x {\displaystyle x} or vice ...
To devise a samplesort implementation, one needs to decide on the number of buckets p. When this is done, the actual algorithm operates in three phases: [4] Sample p−1 elements from the input (the splitters). Sort these; each pair of adjacent splitters then defines a bucket. Loop over the data, placing each element in the appropriate bucket.
The conjecture was disproved in 1959 by L. R. Ford Jr. and Selmer M. Johnson, who found a different sorting algorithm, the Ford–Johnson merge-insertion sort, using fewer comparisons. [1] The same sequence of sorting numbers also gives the worst-case number of comparisons used by merge sort to sort items. [2]