Library sort: Difference between revisions

Revision as of 19:03, 13 October 2020

Latest revision as of 10:17, 19 January 2025

(10 intermediate revisions by 7 users not shown)

Line 1:

{{Short description|Sorting algorithm}}

{{refimprove|date=October 2017}}

{{Infobox Algorithm

Line 10 ⟶ 11:

|optimal=?

}}

'''Library sort''', or '''gapped insertion sort''' is a [[sorting algorithm]] that uses an [[insertion sort]], but with gaps in the array to accelerate subsequent insertions. The name comes from an analogy:

<blockquote>Suppose a librarian were to store histheir books alphabetically on a long shelf, starting with the As at the left end, and continuing to the right along the shelf with no spaces between the books until the end of the Zs. If the librarian acquired a new book that belongs to the B section, once hethey findsfind the correct space in the B section, hethey will have to move every book over, from the middle of the Bs all the way down to the Zs in order to make room for the new book. This is an insertion sort. However, if hethey were to leave a space after every letter, as long as there was still space after B, hethey would only have to move a few books to make room for the new one. This is the basic principle of the Library Sort.</blockquote>

The algorithm was proposed by [[Michael A. Bender]], [[Martín Farach-Colton]], and [[Miguel Mosteiro]] in 2004<ref>{{cite arxivarXiv |arxiveprint=cs/0407003 |title=Insertion Sort is O(n log n) |date=1 July 2004 |last1=Bender |first1=Michael A. |last2=Farach-Colton |first2=Martín |authorlink2=Martin Farach-Colton |last3=Mosteiro |first3=Miguel A.}}</ref> and was published in 2006.<ref name="definition">{{cite journal | journal=Theory of Computing Systems | volume=39 | issue=3 | pages=391–397 | date=June 2006 | last1=Bender | first1=Michael A. | last2=Farach-Colton | first2=Martín | authorlink2=Martin Farach-Colton | last3=Mosteiro | first3=Miguel A. | title=Insertion Sort is O(n log n) | doi=10.1007/s00224-005-1237-z | url=http://csis.pace.edu/~mmosteiro/pub/paperToCS06.pdf | arxiv=cs/0407003 | s2cid=14701669 | access-date=2017-09-07 | archive-url=https://web.archive.org/web/20170908070035/http://csis.pace.edu/~mmosteiro/pub/paperToCS06.pdf | archive-date=2017-09-08 | url-status=dead }}</ref>

Like the insertion sort it is based on, library sort is a [[comparison sort]]; however, it was shown to have a high probability of running in O(n log n) time (comparable to [[quicksort]]), rather than an insertion sort's O(n²). There is no full implementation given in the paper, nor the exact algorithms of important parts, such as insertion and rebalancing. Further information would be needed to discuss how the efficiency of library sort compares to that of other sorting methods in reality.

Compared to basic insertion sort, the drawback of library sort is that it requires extra space for the gaps. The amount and distribution of that space would bedepend implementationon dependentimplementation. In the paper the size of the needed array is ''(1 + ε)n'',<ref name="definition" /> but with no further recommendations on how to choose ε. Moreover, it is neither adaptive nor stable. In order to warrant the with-high-probability time bounds, it requires tomust randomly permute the input, whatwhich changes the relative order of equal elements and shuffles any presorted input. Also, the algorithm uses binary search to find the insertion point for each element, which does not take profitadvantage of presorted input.

Another drawback is that it cannot be run as an [[online algorithm]], because it is not possible to randomly shufflingshuffle the input. If used without this shuffling, it could easily degenerate into quadratic behaviour.

One weakness of [[insertion sort]] is that it may require a high number of swap operations and be costly if memory write is expensive. Library sort may improve that somewhat in the insertion step, as fewer elements need to move to make room, but is also addingadds an extra cost in the rebalancing step. In addition, locality of reference will be poor compared to [[mergesort]], as each insertion from a random data set may access memory that is no longer in cache, especially with large data sets.

==Implementation==

Line 38 ⟶ 39:

'''procedure''' rebalance(A, begin, end) '''is'''

r ← end

w ← end ÷× 2

'''while''' r ≥ begin '''do'''

A[w+1] ← gap

A[w] ← A[r]

A[w+-1] ← gap

r ← r − 1

w ← w − 2

Line 50 ⟶ 51:

S ← new array of n gaps

'''for''' i ← 1 to floor(log2(n) + -1)) '''do'''

'''for'''rebalance(S, j ← 2^i to1, 2^(i + -1) '''do''')

'''for''' insj ← binarysearch2^(A[j],i-1) S,to 2^(i − 1))'''do'''

ins ← binarysearch(A[j], S, 2^i)

insert A[j] at S[ins]

Line 65 ⟶ 67:

{{sorting}}

[[Category:Sorting algorithms]]

[[Category:Comparison sorts]]

[[Category:Stable sorts]]