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What’s the problem? Something like stable marriage problem … but without sex.

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Presentation on theme: "What’s the problem? Something like stable marriage problem … but without sex."— Presentation transcript:

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2 What’s the problem? Something like stable marriage problem … but without sex.

3 What’s the problem?Stable Marriage Problem (SM) Ant: Bea, Ann, Cat Bob: Bea, Cat, Ann Cal: Ann, Bea, Cat Ann: Bob, Ant, Cal Bea: Cal, Ant, Bob Cat: Cal, Bob, Ant -Men rank women, -Women rank men -Match men to women in a matching M such that there is no incentive for a (m,w) pair not in M to divorce and elope - i.e. it is stable, there are no blocking pairs Order n squared

4 What’s the problem?Stable Marriage Problem (SM) Ant: Bea, Ann, Cat Bob: Bea, Cat, Ann Cal: Ann, Bea, Cat Ann: Bob, Ant, Cal Bea: Cal, Ant, Bob Cat: Cal, Bob, Ant -Men rank women, -Women rank men -Match men to women in a matching M such that there is no incentive for a (m,w) pair not in M to divorce and elope - i.e. it is stable, there are no blocking pairs Order n squared

5 What’s the problem?Stable Marriage Problem (SM) Ant: Bea, Ann, Cat Bob: Bea, Cat, Ann Cal: Ann, Bea, Cat Ann: Bob, Ant, Cal Bea: Cal, Ant, Bob Cat: Cal, Bob, Ant -Men rank women, -Women rank men -Match men to women in a matching M such that there is no incentive for a (m,w) pair not in M to divorce and elope - i.e. it is stable, there are no blocking pairs Order n squared

6 What’s the problem?Stable Marriage Problem (SM) Ant: Bea, Ann, Cat Bob: Bea, Cat, Ann Cal: Ann, Bea, Cat Ann: Bob, Ant, Cal Bea: Cal, Ant, Bob Cat: Cal, Bob, Ant -Men rank women, -Women rank men -Match men to women in a matching M such that there is no incentive for a (m,w) pair not in M to divorce and elope - i.e. it is stable, there are no blocking pairs Order n squared

7 What’s the problem?Stable Roommates (SR)

8 What’s the problem?Stable Roommates (SR) Order n squared (Knuth thought not)

9 What’s the problem?Stable Roommates (SR) Order n squared (Rob thought so)

10 What’s the problem?Stable Roommates (SR) The green book

11 What’s the problem?Stable Roommates (SR) Taken from “The green book” 10 agents, each ranks 9 others, gender-free (n=10, n should be even)

12 What’s the problem?Stable Roommates (SR) Taken from “The green book” 7 stable matchings

13 What’s the problem?Stable Roommates (SR) The Algorithm (Pascal) 1985 Code

14 What’s the problem?Stable Roommates (SR) The Algorithm (Pascal) 1985 Code

15 What’s the problem?Stable Roommates (SR) The Algorithm (Pascal)

16 What’s the problem?Stable Roommates (SR) The Algorithm (Pascal)

17 What’s the problem?Stable Roommates (SR) The Algorithm (Pascal)

18 What’s the problem?Stable Roommates (SR) The Algorithm (Pascal)

19 Stephan & Ciaran spotted something!

20 A simple constraint model Stable Roommates (SR)

21 A simple constraint modelStable Roommates (SR) Preference list for agent i

22 A simple constraint modelStable Roommates (SR) Preference list for agent i agent j is agent i’s kth choice

23 A simple constraint modelStable Roommates (SR) Preference list for agent i

24 A simple constraint modelStable Roommates (SR) Preference list for agent i The 5 th preference of agent 3 is agent 1

25 A simple constraint modelStable Roommates (SR) Preference list for agent i agent j is agent i’s kth choice

26 A simple constraint modelStable Roommates (SR) Preference list for agent i agent j is agent i’s kth choice NOTE: a rank value that is low is a preferred choice (large numbers are bad)

27 A simple constraint modelStable Roommates (SR) Preference list for agent i agent j is agent i’s kth choice agent 5 is agent 3’s 1st choice

28 A simple constraint modelStable Roommates (SR) Preference list for agent i agent j is agent i’s kth choice constrained integer variable agent i with a domain of ranks

29 A simple constraint modelStable Roommates (SR) Preference list for agent i agent j is agent i’s kth choice agent 7 gets 6 th choice and that is agent 10

30 A simple constraint modelStable Roommates (SR) Given two agents, i and j, if agent i is matched to an agent he prefers less than agent j then agent j must match up with an agent he prefers to agent i

31 A simple constraint modelStable Roommates (SR) Given two agents, i and j, if agent i is matched to an agent he prefers less than agent j then agent j must match up with an agent he prefers to agent i

32 A simple constraint modelStable Roommates (SR) Given two agents, i and j, if agent i is matched to an agent he prefers less than agent j then agent j must match up with an agent he prefers to agent i (1) agent variables, actually we allow incomplete lists!

33 A simple constraint modelStable Roommates (SR) Given two agents, i and j, if agent i is matched to an agent he prefers less than agent j then agent j must match up with an agent he prefers to agent i (1)agent variables, actually we allow incomplete lists! (2)If agent i is matched to agent he prefers less than agent j then agent j must match with someone better than agent i

34 A simple constraint modelStable Roommates (SR) Given two agents, i and j, if agent i is matched to an agent he prefers less than agent j then agent j must match up with an agent he prefers to agent i (1)agent variables, actually we allow incomplete lists! (2)If agent i is matched to agent he prefers less than agent j then agent j must match with someone better than agent i (3) If agent i is matched to agent j then agent j is matched to agent i

35 3:5682171049 1:8293645710 (2) Given two agents, 1 and 3, if agent 1 is matched to an agent he prefers less than agent 3 then agent 3 must match with an agent he prefers to agent 1

36 3:5682171049 1:8293645710 (3) Given two agents, 1 and 3, if agent 1 is matched to agent 3 then agent 3 is matched to agent 1

37 choco

38 Read in the problem

39 choco Build the model

40 choco Find and print first matching

41 Neat

42 Can model SMI as SRI Ant: Bea, Ann, Cat Bob: Bea, Cat, Ann Cal: Ann, Bea, Cat Ann: Bob, Ant, Cal Bea: Cal, Ant, Bob Cat: Cal, Bob, Ant men women women+6 SMSRI

43 Yes, but what’s new here?

44 1.Model appeared twice in workshops 2.Applied to SM but not SR! (two sets of variables, more complicated) 3.One model for SM, SMI, SR & SRI 4.Simple & elegant

45 Yes, but what’s new here? But this is hard to believe … it is slower than Rob’s 1985 results! 1.Model appeared twice in workshops 2.Applied to SM but not SR! (two sets of variables, more complicated) 3.One model for SM, SMI, SR & SRI 4.Simple & elegant

46 Yes, but what’s new here? But this is hard to believe … it is slower than Rob’s 1985 results! 1.Model appeared twice in workshops 2.Applied to SM but not SR! (two sets of variables, more complicated) 3.One model for SM, SMI, SR & SRI 4.Simple & elegant

47 Cubic to achieve phase-1 table Not so neat 

48 A specialised constraint When an agent’s domain is filtered AC revises all constraints that involve that variable.

49 A specialised constraint When an agent’s domain is filtered AC revises all constraints that involve that variable. In this case that is n constraints

50 A specialised constraint When an agent’s domain is filtered AC revises all constraints that involve that variable. In this case that is n constraints We can do better than this, reducing complexity of model by O(n)

51 A specialised constraint I implemented a specialised binary SR constraint and an n-ary SR constraint This deals with incomplete lists This is presented in the paper You can also download and run this

52 A specialised constraint Here’s the code. Not much to it

53 A specialised constraint Constructor

54 A specialised constraint awakening

55 A specialised constraint lower bound changes

56 A specialised constraint upper bound changes

57 A specialised constraint removal of a value

58 A specialised constraint instantiate

59

60

61 Empirical study When I was younger, my mother did things to annoy me

62 Empirical study SR: simple constraint model, enumerated domains SRB: simple constraint model, bound domains SRN: specialised n-ary constraint, enumerated domains

63 10 < n < 100: read, build, find all stable matchings

64 100 < n < 1000: read, build, find all stable matchings

65 This is new (so says Rob and David) n, average run time, nodes (maximum), proportion with matchings, maximum number of matchings

66 So?

67 Well, think on this …

68 What’s still to do? Prove that the model finds a stable matching in quadratic time …

69 This was all my own work …

70 … well, with some help from David Manlove Rob Irving, Jeremy Singer Ian Gent Chris Unsworth Stephan Mertens Ciaran McCreesh Paul Cockshott Joe Sventek Augustine Kwanashie Andrea

71

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