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{{Short description|Pairing where no unchosen pair prefers each other over their choice}}
In [[mathematics]], [[economics]], and [[computer science]], the '''stable
{{Ordered list|list-style-type=numeric
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Algorithms for finding solutions to the stable marriage problem have applications in a variety of real-world situations, perhaps the best known of these being in the assignment of graduating medical students to their first hospital appointments.<ref>[http://www.dcs.gla.ac.uk/research/algorithms/stable/ Stable Matching Algorithms]</ref> In 2012, the [[Nobel Memorial Prize in Economic Sciences]] was awarded to [[Lloyd S. Shapley]] and [[Alvin E. Roth]] "for the theory of stable allocations and the practice of market design."<ref>{{cite web|url=https://www.nobelprize.org/nobel_prizes/economics/laureates/2012/ |title=The Prize in Economic Sciences 2012 |publisher=Nobelprize.org |access-date=2013-09-09}}</ref>
An important and large-scale application of stable marriage is in assigning users to servers in a large distributed Internet service.<ref name=nuggets>{{cite journal | author=
The [[
==Different stable matchings==
{{
In general, there may be many different stable matchings. For example, suppose there are three men (A, B, C) and three women (X, Y, Z) which have preferences of:
: A: YXZ B: ZYX C: XZY
: X: BAC Y: CBA Z: ACB
There are three stable solutions to this matching arrangement:
* men get their first choice and women their third
* all participants get their second choice
* women get their first choice and men their third
All three are stable, because instability requires both of the participants to be happier with an alternative match. Giving one group their first choices ensures that the matches are stable because they would be unhappy with any other proposed match. Giving everyone their second choice ensures that any other match would be disliked by one of the parties. In general, the family of solutions to any instance of the stable marriage problem can be given the structure of a finite [[distributive lattice]],
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| title = Proceedings of the 50th Symposium on Theory of Computing (STOC 2018)
| year = 2018| arxiv = 1711.01032
| isbn = 978-1-4503-5559-9 }}</ref>
Counting the number of stable matchings in a given instance is [[♯P-complete|#P-complete]].<ref>{{cite journal
| last1 = Irving | first1 = Robert W.
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==Algorithmic solution==
{{
[[File:Gale-Shapley.gif|thumb|right|Animation showing an example of the Gale–Shapley algorithm]]
In 1962, [[David Gale]] and [[Lloyd Shapley]] proved that, for any equal number in different groups, in the context of
The [[Gale–Shapley algorithm]] (also known as the deferred acceptance algorithm) involves a number of "rounds" (or "[[iteration]]s"):
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| editor1-last = Azar | editor1-first = Yossi
| editor2-last = Erlebach | editor2-first = Thomas
| contribution = Cheating by men in the
| doi = 10.1007/11841036_39
| mr = 2347162
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| publisher = Springer
| series = Lecture Notes in Computer Science
| title = Algorithms
| volume = 4168
| year = 2006
}}</ref>
The GS algorithm is non-truthful for the women (the reviewing side): each woman may be able to misrepresent her preferences and get a better match.
==
{{
The rural hospitals theorem concerns a more general variant of the stable matching problem, like that applying in the problem of matching doctors to positions at hospitals, differing in the following ways from the basic {{mvar|n}}-to-{{mvar|n}} form of the stable marriage problem:
* Each participant may only be willing to be matched to a subset of the participants on the other side of the matching.
* The participants on one side of the matching (the hospitals) may have a numerical capacity, specifying the number of doctors they are willing to hire.
* The total number of participants on one side might not equal the total capacity to which they are to be matched on the other side.
* The resulting matching might not match all of the participants.
In this case, the condition of stability is that no unmatched pair prefer each other to their situation in the matching (whether that situation is another partner or being unmatched). With this condition, a stable matching will still exist, and can still be found by the Gale–Shapley algorithm.
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* Any hospital that has some empty positions in some stable matching, receives exactly the same set of doctors in ''all'' stable matchings.
==
In '''[[stable matching with indifference]]''', some men might be indifferent between two or more women and vice versa.
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The '''matching with contracts''' problem is a generalization of matching problem, in which participants can be matched with different terms of contracts.<ref>{{cite journal |first1=John William |last1=Hatfield |first2=Paul |last2=Milgrom |title=Matching with Contracts |journal=[[American Economic Review]] |volume=95 |issue=4 |year=2005 |pages=913–935 |jstor=4132699 |doi=10.1257/0002828054825466}}</ref> An important special case of contracts is matching with flexible wages.<ref>{{cite journal |first1=Vincent |last1=Crawford |first2=Elsie Marie |last2=Knoer |title=Job Matching with Heterogeneous Firms and Workers |year=1981 |journal=[[Econometrica]] |volume=49 |issue=2 |pages=437–450 |jstor=1913320 |doi=10.2307/1913320}}</ref>
==
* [[Matching (graph theory)]] – matching between different vertices of the graph; usually unrelated to preference-ordering.▼
* [[Envy-free matching]] – a relaxation of stable matching for many-to-one matching problems
▲*[[Matching (graph theory)]] – matching between different vertices of the graph; usually unrelated to preference-ordering.
* [[
* [[
* [[Lattice of stable matchings]]▼
* [[Secretary problem]] (also called '''marriage problem''') – deciding when to stop to obtain the best reward in a sequence of options▼
▲*[[Lattice of stable matchings]]
▲*[[Secretary problem]] (also called '''marriage problem''') – deciding when to stop to obtain the best reward in a sequence of options
==References==
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