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Timsort is a stable sorting algorithm (order of elements with same key is kept) and strives to perform balanced merges (a merge thus merges runs of similar sizes). In order to achieve sorting stability, only consecutive runs are merged. Between two non-consecutive runs, there can be an element with the same key inside the runs.
In computer science, counting sort is an algorithm for sorting a collection of objects according to keys that are small positive integers; that is, it is an integer sorting algorithm. It operates by counting the number of objects that possess distinct key values, and applying prefix sum on those counts to determine the positions of each key ...
Such a component or property is called a sort key. For example, the items are books, the sort key is the title, subject or author, and the order is alphabetical. A new sort key can be created from two or more sort keys by lexicographical order. The first is then called the primary sort key, the second the secondary sort key, etc.
As a result, multi-key sorting (sorting by primary, secondary, tertiary keys, etc.) can be achieved by sorting the least significant key first and the most significant key last. For example, to sort the table by the "Text" column and then by the "Numbers" column, you would first click on and sort by the "Numbers" column, the secondary key, and ...
Radix sort is an algorithm that sorts numbers by processing individual digits. n numbers consisting of k digits each are sorted in O(n · k) time. Radix sort can process digits of each number either starting from the least significant digit (LSD) or starting from the most significant digit (MSD). The LSD algorithm first sorts the list by the ...
As the JavaScript array object is suitable for this sorting method, the difference in data structure is related to the speed of data access and thus the time required for sorting. The linear time Θ(n) is used when the values in the array to be sorted are evenly distributed.
Pigeonhole sort, counting sort, radix sort, and Van Emde Boas tree sorting all work best when the key size is small; for large enough keys, they become slower than comparison sorting algorithms. However, when the key size or the word size is very large relative to the number of items (or equivalently when the number of items is small), it may ...
Disperse: Read over A, dropping each key into its bucket in A2; insertion sorting as needed. Collect: Visit the subarrays in order and put all the elements back into the original array, or simply use A2. Note: "keys" may also contain other data, for instance an array of Student objects that contain the key plus a student ID and name.