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    University of Chicago students lined up in height order


    As it's name suggesting, sorting algorithms rearrange the items in a collection in a given order. Often this order is numerical, either ascending or descending, or lexicographical, but there are many options beyond that, depending on the use the sorted collection will be put to. Items to be sorted can be any kind of object with at least one rankable attribute - database records, people, colors, house numbers, etc. The item being sorted may have more than one value in it, e.g. it could be a tuple, but the value that is used to sort in these cases is known as the key. Some tasks may even require more than one sort, such as ordering a deck of cards first by number and then by suit. In this case, those two attributes are the primary and secondary keys, depending on which is tended to first.

    The same values may be ranked differently by different sorting algorithms, e.g. colors could be ranked alphabetically, or by preference, but the order of the separate values should not change if the same sorting algorithm is applied multiple times. Otherwise it would be a shuffle rather than a sort. However, values with equal rank may or may not remain in the same order upon consecutive sorts. This is knows as the stability of the algorithm. An stable sort will preserve the original order of equal-rank items each time it is run so their order will not change, while they are considered interchangeable in a non-stable sort.

    Due to the prevalence of sorting in programs everywhere, much research has gone into finding the most efficient sorting algorithms for different cases. Many consider the sorting problem solved by mergesort, quicksort and heapsort which run on average in O(n log n) time, but in reality these are often slower than they need to be for partially-sorted data and so many hybrids have risen to plug these gaps, such as Timsort (the standard sort routine for Python and Java) and introsort. Some of these hybrids can sort partially-sorted data in as little as O(n) time, and new shades of sorting algorithms are being developed all the time.

    Many basic sorting algorithms, such as the first 3 mentioned above and bubblesort, see widespread use as introductory material in classes teaching computer programming due to their universality and easily demonstrable nature. Sorting in general is of huge use to programmers as it allows not only a more efficient traversal of data for applications, but is much easier for humans to understand should they need to examine or analyse the data by hand as well.

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    BrainMass Categories within Sorting


    Solutions: 1

    A divide and conquer algorithm that sorts subsections as it partitions them off of the original list based on a pivot value.


    Solutions: 0

    A divide and conquer algorithm that breaks the original list down into subsections and sorts them as it rejoins the parts in order.


    Solutions: 0

    An algorithm that sorts the list in place by comparing each element with its next neighbor.

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