1. Pareto Optimality in House Allocation Problems David Manlove Department of Computing Science University of Glasgow David Abraham Computer Science Department Carnegie-Mellon University Katarína Cechlárová Institute of Mathematics PJ Safárik University in Košice Kurt Mehlhorn Max-Planck-Institut fűr Informatik Saarbrűcken Supported by Royal Society of Edinburgh/Scottish Executive Personal Research Fellowship and Engineering and Physical Sciences Research Council grant GR/R84597/01

2. 2 House Allocation problem (HA) Set of agents A={a 1, a 2, …, a r } Set of houses H={h 1, h 2, …, h s } Each agent a i has an acceptable set of houses A i  H a i ranks A i in strict order of preference Example: a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 Let n = r + s and let m =total length of preference lists  a 1 finds h 1 and h 2 acceptable a 3 prefers h 4 to h 3

3. 3 Applications House allocation context: Large-scale residence exchange in Chinese housing markets Yuan, 1996 Allocation of campus housing in American universities, such as Carnegie-Mellon, Rochester and Stanford Abdulkadiroğlu and Sönmez, 1998 Other matching problems: US Naval Academy: students to naval officer positions Roth and Sotomayor, 1990 Scottish Executive Teacher Induction Scheme Assigning students to projects

4. 4 The underlying graph Weighted bipartite graph G=(V,E) Vertex set V=A  H Edge set: { a i, h j }  E if and only if a i finds h j acceptable Weight of edge { a i, h j } is rank of h j in a i ’s preference list Example a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 a1a1 a2a2 a3a3 a4a4 h1h1 h2h2 h3h3 h4h4     2 1 1 2 3 2 1 1 2

5. 5 Definition of a matching A matching M is a subset of A×H such that: if (a i, h j )  M then a i finds h j acceptable each agent belongs to at most one pair in M each house belongs to at most one pair in M Example a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 a1a1 a2a2 a3a3 a4a4 h1h1 h2h2 h3h3 h4h4     M={(a 1, h 1 ), (a 2, h 4 ), (a 3, h 3 )} M(a 1 )=h 1

6. 6 Maximum matchings In many applications we wish to match as many agents as possible A maximum matching is a matching with largest size Example Some maximum matchings are “better” than others! a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4

7. 7 Pareto optimal matchings A matching M 1 is Pareto optimal if there is no matching M 2 such that: 1. Some agent is better off in M 2 than in M 1 2. No agent is worse off in M 2 than in M 1 Example M 1 is not Pareto optimal since a 1 and a 2 could swap houses – each would be better off M 2 is Pareto optimal a 1 : h 2 h 1 a 2 : h 1 h 2 a 3 : h 3 a 1 : h 2 h 1 a 2 : h 1 h 2 a 3 : h 3 M1M1 M2M2

8. 8 Testing for Pareto optimality A matching M is maximal if there is no agent a and house h, each unmatched in M, such that a finds h acceptable A matching M is trade-in-free if there is no matched agent a and unmatched house h such that a prefers h to M(a) A matching M is coalition-free if there is no coalition, i.e. a sequence of matched agents  a 0,a 1,…,a r-1  such that a i prefers M(a i ) to M(a i+1 ) (0  i  r-1) a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 Proposition: M is Pareto optimal if and only if M is maximal, trade-in-free and coalition-free M is not maximal due to a 3 and h 3

9. 9 Testing for Pareto optimality A matching M is maximal if there is no agent a and house h, each unmatched in M, such that a finds h acceptable A matching M is trade-in-free if there is no matched agent a and unmatched house h such that a prefers h to M(a) A matching M is coalition-free if there is no coalition, i.e. a sequence of matched agents  a 0,a 1,…,a r-1  such that a i prefers M(a i ) to M(a i+1 ) (0  i  r-1) a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 Proposition: M is Pareto optimal if and only if M is maximal, trade-in-free and coalition-free M is not trade-in-free due to a 2 and h 3

10. 10 Testing for Pareto optimality A matching M is maximal if there is no agent a and house h, each unmatched in M, such that a finds h acceptable A matching M is trade-in-free if there is no matched agent a and unmatched house h such that a prefers h to M(a) A matching M is coalition-free if there is no coalition, i.e. a sequence of matched agents  a 0,a 1,…,a r-1  such that a i prefers M(a i+1 ) to M(a i ) (0  i  r-1) a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 Proposition: M is Pareto optimal if and only if M is maximal, trade-in-free and coalition-free M is not coalition-free due to  a 1, a 2, a 4  a1a1 a3a3 a4a4 h1h1 h3h3 h4h4 a2a2 h2h2

11. 11 Testing for Pareto optimality A matching M is maximal if there is no agent a and house h, each unmatched in M, such that a finds h acceptable A matching M is trade-in-free if there is no matched agent a and unmatched house h such that a prefers h to M(a) A matching M is coalition-free if there is no coalition, i.e. a sequence of matched agents  a 0,a 1,…,a r-1  such that a i prefers M(a i+1 ) to M(a i ) (0  i  r-1) a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 Lemma: M is Pareto optimal if and only if M is maximal, trade-in-free and coalition-free

12. 12 The envy graph Straightforward to check a given matching M for the maximality and trade-in-free properties in O(m) time To check for the existence of a coalition: Form the envy graph of M, denoted by G(M) Vertex for each matched agent Edge from a i to a j if and only if a i prefers M(a j ) to M(a i ) Example a 1 : h 2 h 1 a 2 : h 3 h 4 h 2 a 3 : h 4 h 3 a 4 : h 1 h 4 M admits a coalition if and only if G(M) has a directed cycle Theorem: we may check whether a given matching M is Pareto optimal in O(m ) time a1a1 a4a4 a2a2

13. 13 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2

14. 14 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2

15. 15 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2

16. 16 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 M 1 ={(a 1,h 1 ), (a 2,h 2 )}

17. 17 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 M 1 ={(a 1,h 1 ), (a 2,h 2 )}

18. 18 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 M 1 ={(a 1,h 1 ), (a 2,h 2 )}

19. 19 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 M 1 ={(a 1,h 1 ), (a 2,h 2 )}

20. 20 Finding a Pareto optimal matching Simple greedy algorithm, referred to as the serial dictatorship mechanism by economists for each agent a in turn if a has an unmatched house on his list match a to the most-preferred such house; else report a as unmatched; Theorem: The serial dictatorship mechanism constructs a Pareto optimal matching in O(m ) time Abdulkadiroğlu and Sönmez, 1998 Example a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 a 1 : h 1 h 2 h 3 a 2 : h 1 h 2 a 3 : h 1 h 2 M 1 ={(a 1,h 1 ), (a 2,h 2 )} M 2 ={(a 1,h 3 ), (a 2,h 2 ), (a 3,h 1 )}

21. 21 Related work Rank maximal matchings Matching M is rank maximal if, in M 1. Maximum number of agents get their first-choice house; 2. Subject to (1), maximum number of agents get their second-choice house; etc. Irving, Kavitha, Mehlhorn, Michail, Paluch, SODA 04 A rank maximal matching is Pareto optimal, but need not be of maximum size Popular matchings Matching M is popular if there is no other matching M ’ such that: more agents prefer M ’ to M than prefer M to M ’ Abraham, Irving, Kavitha, Mehlhorn, SODA 05 A popular matching is Pareto optimal, but need not exist

22. 22 Related work (cont) Take underlying weighted bipartite graph G The weight of a matching M in G is the sum of the weights of the edges contained in M where A M is the set of agents who are matched in M rank a (h) is the rank of h in a ’s preference list Find a maximum cardinality minimum weight matching M in G M is Pareto optimal For if not there exists a matching M′ such that rank a (M′(a))  rank a (M(a)) for all a  A M rank a (M′(a)) < rank a (M(a)) for some a  A M so M′ contradicts the minimum weight property of M A maximum cardinality minimum weight matching may be found in O(  nmlog n) time Gabow and Tarjan, 1989

23. 23 Faster algorithm for finding a maximum Pareto optimal matching Three-phase algorithm with O(  nm) overall complexity Phase 1 – O(  nm) time Find a maximum matching in G Classical O(  nm) augmenting path algorithm Hopcroft and Karp, 1973 Phase 2 – O(m) time Enforce trade-in-free property Phase 3 – O(m) time Enforce coalition-free property Extension of Gale’s Top-Trading Cycles (TTC) algorithm Shapley and Scarf, 1974

24. 24 Phase 1 a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Maximum matching M in G has size 8 M must be maximal No guarantee that M is trade-in-free or coalition-free

25. 25 Phase 1 a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Maximum matching M in G has size 9 M must be maximal No guarantee that M is trade-in-free or coalition-free

26. 26 Phase 1 a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Maximum matching M in G has size 9 M must be maximal No guarantee that M is trade-in-free or coalition-free M not coalition-free M not trade-in-free

27. 27 Phase 2 description For each house h, maintain a list L h, initially containing those pairs ( a, r ) such that: a is a matched agent who prefers h to M(a) r is the rank of h in a ’s list Maintain a stack S of unmatched houses h whose list L h is nonempty Each matched agent a maintains a pointer curr a to the rank of M(a) Example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 6, 6), (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  (a 7, 7)  L h 11 =  (a 6, 8)  S =  h 6, h 7  curr a 6 =10 curr a 7 =9

28. 28 Phase 2 description a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 6, 6), (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  (a 7, 7)  L h 11 =  (a 6, 8)  S =  h 6, h 7  curr a 6 =10 curr a 7 =9

29. 29 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 6, 6), (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  (a 7, 7)  L h 11 =  (a 6, 8)  S =  h 6, h 7  curr a 6 =10 curr a 7 =9

30. 30 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  (a 7, 7)  L h 11 =  (a 6, 8)  S =  h 6, h 10  curr a 6 =6 curr a 7 =9

31. 31 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  (a 7, 7)  L h 11 =  (a 6, 8)  S =  h 6, h 10  curr a 6 =6 curr a 7 =9

32. 32 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  (a 6, 8)  S =  h 6, h 11  curr a 6 =6 curr a 7 =7

33. 33 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  (a 6, 8)  S =  h 6, h 11  curr a 6 =6 curr a 7 =7

34. 34 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 6, 5), (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  S =  h 6  curr a 6 =6 curr a 7 =7

35. 35 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  S =  h 7  curr a 6 =5 curr a 7 =7

36. 36 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 7, 4), (a 8, 6)  L h 7 =  (a 7, 5), (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  S =  h 7  curr a 6 =5 curr a 7 =7

37. 37 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 7, 4), (a 8, 6)  L h 7 =  (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  S =  curr a 6 =5 curr a 7 =5

38. 38 Phase 2 example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 while S is nonempty pop a house h from S; remove the first pair (a,r) from the head of L h ; if r < curr a // a prefers h to M(a) let h’= M(a); remove (a,h’) from M and add (a,h) to M; curr a := h; h := h’; push h onto the stack if L h is nonempty; L h 6 =  (a 7, 4), (a 8, 6)  L h 7 =  (a 8, 5)  L h 8 =  (a 6, 4)  L h 10 =  L h 11 =  S =  curr a 6 =5 curr a 7 =5

39. 39 Once Phase 2 terminates, matching is trade-in-free Data structures may be initialised in O(m) time Main loop takes O(m) time overall  Phase 2 is O(m) Coalitions may remain… Phase 2 termination

40. 40 Once Phase 2 terminates, matching is trade-in-free Data structures may be initialised in O(m) time Main loop takes O(m) time overall  Phase 2 is O(m) Coalitions may remain… Phase 2 termination Example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9

41. 41 Elimination of coalitions Repeatedly finding and eliminating coalitions takes O(m 2 ) time Cycle detection in G(M) takes O(m) time O(m) coalitions in the worst case Faster method: extension of TTC algorithm An agent matched to his/her first-choice house cannot be in a coalition Such an agent can be removed from consideration Houses matched to such agents are no longer exchangeable Such a house can be removed from consideration This rule can be recursively applied until either No agent remains (matching is coalition-free) A coalition exists, which can be found and removed Phase 3 description

42. 42 Build a path P of agents (represented by a stack) Each house is initially unlabelled Each agent a has a pointer p(a) pointing to M(a) or the first unlabelled house on a ’s preference list (whichever comes first) Keep a counter c(a) for each agent a (initially c(a)=0 ) This represents the number of times a appears on the stack Outer loop iterates over each matched agent a such that p(a)  M(a) Initialise P to contain agent a Inner loop iterates while P is nonempty Pop an agent a ’ from P If c(a ’ )=2 we have a coalition (CYCLE) Remove by popping the stack and label the houses involved Else if p(a ’ )=M(a ’ ) we reach a dead end (BACKTRACK) Label M(a ’ ) Else add a ’’ where p(a ’ )=M(a ’’ ) to the path (EXTEND) Push a ’ and a ’’ onto the stack Increment c(a ’’ ) Phase 3 description

43. 43 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 P=a1P=a1 a1a1

44. 44 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 0h 3 a 4 1h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 P=a1, a4P=a1, a4 EXTEND a4a4

45. 45 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 1h 3 a 4 1h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 EXTEND a4a4 P=  a 1, a 4, a 3  a3a3

46. 46 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 1h 3 a 4 1h 4 a 5 1h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 EXTEND a4a4 P=  a 1, a 4, a 3, a 5  a3a3 a5a5

47. 47 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 1h 3 a 4 2h 4 a 5 1h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 EXTEND a4a4 P=  a 1, a 4, a 3, a 5, a 4  a3a3 a5a5

48. 48 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 1h 3 a 4 2h 4 a 5 1h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 CYCLE a4a4 P=  a 1, a 4, a 3, a 5  a3a3 a5a5

49. 49 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 CYCLE a4a4 a3a3 a5a5 P=  a 1, a 4, a 3, a 5 

50. 50 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 P=a1P=a1

51. 51 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 1h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 EXTEND P=  a 1, a 2  a2a2

52. 52 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 1h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 1h 9 a1a1 EXTEND P=  a 1, a 2, a 9  a2a2 a9a9

53. 53 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 1h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 1h 9 a1a1 BACKTRACK P=  a 1, a 2  a2a2 a9a9

54. 54 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 1h 1 a 2 1h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 P=  a 1, a 2  a2a2

55. 55 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 2h 1 a 2 1h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 EXTEND P=  a 1, a 2, a 1  a2a2

56. 56 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 2h 1 a 2 1h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 CYCLE P=  a 1, a 2  a2a2

57. 57 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 a1a1 CYCLE P=  a 1, a 2  a2a2

58. 58 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 1h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 P=a6P=a6 a6a6

59. 59 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 1h 6 a 7 0h 7 a 8 1h 8 a 9 0h 9 P=  a 6, a 8  EXTEND a6a6 a8a8

60. 60 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 1h 6 a 7 1h 7 a 8 1h 8 a 9 0h 9 P=  a 6, a 8, a 7  EXTEND a6a6 a8a8 a7a7

61. 61 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 2h 6 a 7 1h 7 a 8 1h 8 a 9 0h 9 P=  a 6, a 8, a 7, a 6  EXTEND a6a6 a8a8 a7a7

62. 62 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 2h 6 a 7 1h 7 a 8 1h 8 a 9 0h 9 P=  a 6, a 8, a 7  CYCLE a6a6 a8a8 a7a7 Agent counter House label

63. 63 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 P=  a 6, a 8, a 7  CYCLE a6a6 a8a8 a7a7

64. 64 Phase 3: example a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9 Agent counter House label a 1 0h 1 a 2 0h 2 a 3 0h 3 a 4 0h 4 a 5 0h 5 a 6 0h 6 a 7 0h 7 a 8 0h 8 a 9 0h 9 P= 

65. 65 Once Phase 3 terminates, matching is coalition-free Data structures may be initialised in O(m) time Nested loops take O(m) time overall  Phase 3 is O(m) Phase 3 termination a 1 : h 4 h 5 h 3 h 2 h 1 a 2 : h 3 h 4 h 5 h 9 h 1 h 2 a 3 : h 5 h 4 h 1 h 2 h 3 a 4 : h 3 h 5 h 4 a 5 : h 4 h 3 h 5 a 6 : h 2 h 3 h 5 h 8 h 6 h 7 h 1 h 11 h 4 h 10 a 7 : h 1 h 4 h 3 h 6 h 7 h 2 h 10 h 5 h 11 a 8 : h 1 h 5 h 4 h 3 h 7 h 6 h 8 a 9 : h 4 h 3 h 5 h 9

66. 66 Initial property rights Suppose A ’  A and each member of A ’ owns a house initially For each agent a  A ’, denote this house by h(a) Truncate a ’s list at h(a) Form matching M by pre-assigning a to h(a) Use Hopcroft-Karp algorithm to augment M to a maximum cardinality matching M ’ in restricted HA instance Then proceed with Phases 2 and 3 as before Constructed matching M ’ is individually rational If A ’= A then we have a housing market TTC algorithm finds the unique matching that belongs to the core Shapley and Scarf, 1974 Roth and Postlewaite, 1977

67. 67 Minimum Pareto optimal matchings Problem of finding a minimum Pareto optimal matching is NP-hard Result holds even if all preference lists have length 3 Reduction from Minimum Maximal Matching Problem is approximable within a factor of 2 Follows since any Pareto optimal matching is a maximal matching in the underlying graph G Any two maximal matchings differ in size by at most a factor of 2 Korte and Hausmann, 1978

68. 68 Interpolation of Pareto optimal matchings Given an HA instance I, p - (I) and p + (I) denote the sizes of a minimum and maximum Pareto optimal matching Theorem: I admits a Pareto optimal matching of size k, for each k such that p - (I)  k  p + (I) Given a Pareto optimal matching of size k, O ( m ) algorithm constructs a Pareto optimal matching of size k+1 or reports that k = p + (I) Based on assigning a vector  r 1, …, r k  to an augmenting path P =  a 1, h 1, …, a k, h k  where r i =rank a i (h i ) Examples:  1,3,2   1,2,2  Find a lexicographically smallest augmenting path h1h1 h2h2 h3h3 h4h4 1 2 1 h5h5 a1a1 a2a2 a3a3 a4a4 2 3 1 1 2

69. 69 Summary Definition of the House Allocation problem (HA) O(m) algorithm for testing a matching for Pareto optimality O(m) algorithm for finding a Pareto optimal matching O(  nm) algorithm for finding a maximum Pareto optimal matching NP-hardness of finding a minimum Pareto optimal matching Interpolation result More information: D.J. Abraham, K. Cechlarova, D.F.M., K. Mehlhorn: Pareto Optimality in House Allocation Problems, to appear at ISAAC 2004: the 15 th International Symposium on Algorithms and Computation, Hong Kong, December 20-22, 2004

70. 70 Open problems Finding a maximum Pareto optimal matching Ties in the preference lists Solvable in O(  nmlog n) time Solvable in O(  nm) time? One-many case (houses may have capacity >1 ) Non-bipartite case Solvable in O(  (n  (m, n))mlog 3/2 n) time D.J. Abraham, D.F. Manlove Pareto optimality in the Roommates problem Technical Report TR-2004-182 of the Computing Science Department of Glasgow University Solvable in O(  nm) time?