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A Simply Exponential Upper Bound on the Maximum Number of Stable Matchings Robbie Weber Joint work with Anna Karlin and Shayan Oveis Gharan

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  • A Simply Exponential Upper Bound on the Maximum Number of Stable Matchings

    Robbie Weber

    Joint work with Anna Karlin and Shayan Oveis Gharan

  • Given π‘š1, … ,π‘šπ‘› , {𝑀1, … , 𝑀𝑛} each rank the other set.

    Stable Matching Problem

  • Given π‘š1, … ,π‘šπ‘› , {𝑀1, … , 𝑀𝑛} each rank the other set.

    A matching is stable if there is no blocking pair.

    A pair (π‘š,𝑀) is blocking if π‘š prefers 𝑀 to his match, and 𝑀 prefers π‘š to her match.

    π‘€π‘š

    π‘šβ€² 𝑀′

    𝑀 ,𝑀’ π‘š ,π‘šβ€™

    Stable Matching Problem

  • Given π‘š1, … ,π‘šπ‘› , {𝑀1, … , 𝑀𝑛} each rank the other set.

    A matching is stable if there is no blocking pair.

    A pair (π‘š,𝑀) is blocking if π‘š prefers 𝑀 to his match, and 𝑀 prefers π‘š to her match.

    π‘€π‘š

    π‘šβ€² 𝑀′

    𝑀 ,𝑀’ π‘š ,π‘šβ€™

    Stable Matching Problem

  • Given π‘š1, … ,π‘šπ‘› , {𝑀1, … , 𝑀𝑛} each rank the other set.

    A matching is stable if there is no blocking pair.

    A pair (π‘š,𝑀) is blocking if π‘š prefers 𝑀 to his match, and 𝑀 prefers π‘š to her match.

    π‘€π‘š

    π‘šβ€² 𝑀′

    𝑀 ,𝑀’ π‘š ,π‘šβ€™

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    What is the maximum number of stable matchings?

    I.e. as the size of the instance increases, how fast can we make the number of stable matchings increase?

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Stable Matching Problem

  • [Gale-Shapley β€˜62]

    There is always at least one stable matching,

    and there can be more than one.

    What is the maximum number of stable matchings?

    I.e. as the size of the instance increases, how fast can we make the number of stable matchings increase?

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Stable Matching Problem

    [Knuth’76,Gusfield-Irving’89]:

  • [Knuth’76,Gusfield-Irving’89]:

    What is the maximum number of stable matchings?

  • [Knuth’76,Gusfield-Irving’89]:

    What is the maximum number of stable matchings?𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    Lower Bounds (i.e. constructions)

  • [Knuth’76,Gusfield-Irving’89]:

    What is the maximum number of stable matchings?𝑀1, 𝑀2 π‘š1 1

    2

    β‰₯ 2𝑛/2

    π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Lower Bounds (i.e. constructions)

  • [Knuth’76,Gusfield-Irving’89]:

    What is the maximum number of stable matchings?𝑀1, 𝑀2 π‘š1 1

    2

    β‰₯ 2𝑛/2

    β‰₯ 2.28𝑛 [Irving-Leather-Knuth’86, Benjamin-Converse-Krieger’95, Thurber’02]

    π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Lower Bounds (i.e. constructions)

  • [Knuth’76,Gusfield-Irving’89]:

    What is the maximum number of stable matchings?𝑀1, 𝑀2 π‘š1 1

    2

    β‰₯ 2𝑛/2

    β‰₯ 2.28𝑛 [Irving-Leather-Knuth’86, Benjamin-Converse-Krieger’95, Thurber’02]

    ≀ 𝑛! β‰ˆ 2𝑛 log 𝑛 βˆ’π‘‚(𝑛)

    π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Lower Bounds (i.e. constructions)

    Upper Bounds

  • [Knuth’76,Gusfield-Irving’89]:

    What is the maximum number of stable matchings?𝑀1, 𝑀2 π‘š1 1

    2

    β‰₯ 2𝑛/2

    β‰₯ 2.28𝑛 [Irving-Leather-Knuth’86, Benjamin-Converse-Krieger’95, Thurber’02]

    ≀ 𝑛! β‰ˆ 2𝑛 log 𝑛 βˆ’π‘‚(𝑛)

    ≀ 𝑛!/𝑐𝑛 [Stathopoulos’11]

    π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Lower Bounds (i.e. constructions)

    Upper Bounds

  • Main Result

    There is a universal constant 𝐢 such that every stable matching instance with 𝑛 men and 𝑛 women

    has ≀ 𝐢𝑛 stable matchings.

  • Proof Outline

    Step 1 [Irving Leather 86]: The number of stable matchings equals the number of downsets of a certain POSET.

    POSET: A set with a transitive antisymmetric relation β‰Ί, i.e., a DAG

    𝑒 dominates 𝑣 if 𝑣 β‰Ί 𝑒.

    Downset: a set of elements, and everything they dominate. Downset

  • Proof Outline

    Step 1 [Irving Leather 86]: The number of stable matchings equals the number of downsets of a certain POSET.

    POSET: A set with a transitive antisymmetric relation β‰Ί, i.e., a DAG

    𝑒 dominates 𝑣 if 𝑣 β‰Ί 𝑒.

    Downset: a set of elements, and everything they dominate. Downset

  • Proof Outline

    Step 1 [Irving Leather 86]: The number of stable matchings equals the number of downsets of a certain POSET.

    POSET: A set with a transitive antisymmetric relation β‰Ί, i.e., a DAG

    𝑒 dominates 𝑣 if 𝑣 β‰Ί 𝑒.

    Downset: a set of elements, and everything they dominate. Downset

  • Proof Outline

    Step 1 [Irving Leather 86]: The number of stable matchings equals the number of downsets of a certain POSET.

    Fact: POSETs for stable matchings have at most 𝑛2 elements.

    Does this imply a 𝐢𝑛 upper bound?

  • Proof Outline

    Step 1 [Irving Leather 86]: The number of stable matchings equals the number of downsets of a certain POSET.

    Fact: POSETs for stable matchings have at most 𝑛2 elements.

    Does this imply a 𝐢𝑛 upper bound?

  • Proof Outline

    Step 1 [Irving Leather 86]: The number of stable matchings equals the number of downsets of a certain POSET.

    Fact: POSETs for stable matchings have at most 𝑛2 elements.

    Does this imply a 𝐢𝑛 upper bound?

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing.”

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

    Our main theorem follows immediately from these three steps.

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

    Our main theorem follows immediately from these three steps.

  • Another of Knuth’s problems from 1976:

    Find a concise description of the set of stable matchings.

    Irving and Leather’s POSET

  • Another of Knuth’s problems from 1976:

    Find a concise description of the set of stable matchings.

    Idea:

    Start at the man-optimal matching, and jump from one matching to another, getting closer to the woman optimal matching.

    Irving and Leather’s POSET

  • Another of Knuth’s problems from 1976:

    Find a concise description of the set of stable matchings.

    Idea:

    Start at the man-optimal matching, and jump from one matching to another, getting closer to the woman optimal matching.

    There can be exponentially many stable matchings

    But there are only polynomially many β€œsimple transformations” to jump from one stable matching to another.

    Irving and Leather’s POSET

  • A trivial example: 𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Irving and Leather’s POSET

  • A trivial example:

    There are 2𝑛/2 stable matchings

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Irving and Leather’s POSET

  • A trivial example:

    There are 2𝑛/2 stable matchings

    but only 𝑛/2 simple transformations from one stable matching to another

    (Switching from the blue matching to the green matching in some pair)

    𝑀1, 𝑀2 π‘š1 1

    2π‘š2𝑀2, 𝑀1

    π‘š2, π‘š1

    π‘š1, π‘š2

    𝑀1

    1𝑀2

    𝑀3, 𝑀4 π‘š3 1

    2π‘š4𝑀4, 𝑀3

    π‘š4, π‘š3

    π‘š3, π‘š4

    𝑀3

    1𝑀4

    π‘€π‘›βˆ’1, 𝑀𝑛 π‘šπ‘›βˆ’1 1

    2π‘šπ‘›π‘€π‘›, π‘€π‘›βˆ’1

    π‘šπ‘›, π‘šπ‘›βˆ’1

    π‘šπ‘›βˆ’1, π‘šπ‘›

    π‘€π‘›βˆ’1

    1𝑀𝑛

    Irving and Leather’s POSET

  • RotationA rotation is an ordered list of pairs:

    𝜌 = π‘š1, 𝑀1 , π‘š2, 𝑀2 , … , π‘šπ‘˜ , π‘€π‘˜s.t., for all stable matchings 𝑀 where 𝜌 βŠ† 𝑀

    β€’ 𝑀′ = 𝑀 \ 𝜌 βˆͺ π‘š1, 𝑀2 , π‘š2, 𝑀3 , … (π‘šπ‘˜ , 𝑀1)is stable.

    β€’ Each π‘šπ‘– is worse off in 𝑀′ than in 𝑀, and each 𝑀𝑖 is better off.

    β€’ [some technical points we don’t need]

    π‘š1

    π‘š2

    𝑀1

    𝑀2

    π‘š3 𝑀3

  • RotationA rotation is an ordered list of pairs:

    𝜌 = π‘š1, 𝑀1 , π‘š2, 𝑀2 , … , π‘šπ‘˜ , π‘€π‘˜s.t., for all stable matchings 𝑀 where 𝜌 βŠ† 𝑀

    β€’ 𝑀′ = 𝑀 \ 𝜌 βˆͺ π‘š1, 𝑀2 , π‘š2, 𝑀3 , … (π‘šπ‘˜ , 𝑀1)is stable.

    β€’ Each π‘šπ‘– is worse off in 𝑀′ than in 𝑀, and each 𝑀𝑖 is better off.

    β€’ [some technical points we don’t need]

    π‘š1

    π‘š2

    𝑀1

    𝑀2

    π‘š3 𝑀3

  • RotationA rotation is an ordered list of pairs:

    𝜌 = π‘š1, 𝑀1 , π‘š2, 𝑀2 , … , π‘šπ‘˜ , π‘€π‘˜s.t., for all stable matchings 𝑀 where 𝜌 βŠ† 𝑀

    β€’ 𝑀′ = 𝑀 \ 𝜌 βˆͺ π‘š1, 𝑀2 , π‘š2, 𝑀3 , … (π‘šπ‘˜ , 𝑀1)is stable.

    β€’ Each π‘šπ‘– is worse off in 𝑀′ than in 𝑀, and each 𝑀𝑖 is better off.

    β€’ [some technical points we don’t need]

    Any stable matching instance has at most 𝑂(𝑛2)rotations that can be constructed in poly time.

    π‘š1

    π‘š2

    𝑀1

    𝑀2

    π‘š3 𝑀3

  • RotationA rotation is an ordered list of pairs:

    𝜌 = π‘š1, 𝑀1 , π‘š2, 𝑀2 , … , π‘šπ‘˜ , π‘€π‘˜s.t., for all stable matchings 𝑀 where 𝜌 βŠ† 𝑀

    β€’ 𝑀′ = 𝑀 \ 𝜌 βˆͺ π‘š1, 𝑀2 , π‘š2, 𝑀3 , … (π‘šπ‘˜ , 𝑀1)is stable.

    β€’ Each π‘šπ‘– is worse off in 𝑀′ than in 𝑀, and each 𝑀𝑖 is better off.

    β€’ [some technical points we don’t need]

    Any stable matching instance has at most 𝑂(𝑛2)rotations that can be constructed in poly time.

    π‘š1

    π‘š2

    𝑀1

    𝑀2

    π‘š3 𝑀3

  • Rotation POSET

    To jump from stable matching to stable matching, some rotations must be eliminated before others.

  • Rotation POSET

    To jump from stable matching to stable matching, some rotations must be eliminated before others.

    This induces a partial order on rotations:

    𝜌1 β‰Ί 𝜌2 iff 𝜌1 must be eliminated before 𝜌2.

  • Rotation POSET

    To jump from stable matching to stable matching, some rotations must be eliminated before others.

    This induces a partial order on rotations:

    𝜌1 β‰Ί 𝜌2 iff 𝜌1 must be eliminated before 𝜌2.

    Nodes of the rotation POSET are rotations and 𝜌1 ← 𝜌2 if 𝜌1 β‰Ί 𝜌2.

  • Rotation POSET

    To jump from stable matching to stable matching, some rotations must be eliminated before others.

    This induces a partial order on rotations:

    𝜌1 β‰Ί 𝜌2 iff 𝜌1 must be eliminated before 𝜌2.

    Nodes of the rotation POSET are rotations and 𝜌1 ← 𝜌2 if 𝜌1 β‰Ί 𝜌2.

    Observation: All rotations with an agent form a directed path.

  • [Irving-Leather 86]

    #Stable Matchings = #Downsets Rotation POSET

    (π‘š1, 𝑀2),(m4, w3)

    (π‘š1, 𝑀1),(π‘š2, 𝑀2)

    π‘š1

    π‘š2

    π‘š3

    π‘š4

    𝑀1

    𝑀2

    𝑀3

    𝑀4 (π‘š3, 𝑀3),(π‘š4, 𝑀4)

    (π‘š2, 𝑀1),(m3, w4)

    (π‘š1, 𝑀3),(m2, w4)

    (π‘š3, 𝑀1),(m4, w2)

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

  • A POSET is k-mixing if we can decompose

    it into π‘˜ paths 𝑃1, 𝑃2, … , π‘ƒπ‘˜ s.t.,

    β€’ ⋃𝑃𝑖 includes all elements

    β€’ Every set π‘ˆ intersects β‰₯ π‘ˆ paths.

    π‘˜-Mixing POSETs

    𝑛

    𝑛

  • A POSET is k-mixing if we can decompose

    it into π‘˜ paths 𝑃1, 𝑃2, … , π‘ƒπ‘˜ s.t.,

    β€’ ⋃𝑃𝑖 includes all elements

    β€’ Every set π‘ˆ intersects β‰₯ π‘ˆ paths.

    π‘˜-Mixing POSETs

    𝑛

    𝑛

  • Each man and each woman have a path 𝑃𝑖 with all rotations containing that agent.

    Rotation POSETs are 2𝑛-Mixing

  • Each man and each woman have a path 𝑃𝑖 with all rotations containing that agent.

    There are 2𝑛 paths.

    Rotation POSETs are 2𝑛-Mixing

  • Each man and each woman have a path 𝑃𝑖 with all rotations containing that agent.

    There are 2𝑛 paths.

    β€’ These paths cover all rotations.

    Rotation POSETs are 2𝑛-Mixing

  • Each man and each woman have a path 𝑃𝑖 with all rotations containing that agent.

    There are 2𝑛 paths.

    β€’ These paths cover all rotations.

    β€’ They mix!

    1) Every rotation contains a new (man, woman) pair.

    2) We need at least π‘Ÿ men and or women to make π‘Ÿ new pairs. Thus π‘Ÿ rotations must intersect at least π‘Ÿ paths.

    Rotation POSETs are 2𝑛-Mixing

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

  • Proof Outline

    Step 1 [Irving-Leather 86]: The number of stable matchings equals the number of downsets of a POSET with ≀ 𝑛2 nodes.

    Step 2: POSETs for stable matchings are β€œ2𝑛–mixing”.

    Step 3: Every π‘˜-mixing POSET has at most πΆπ‘˜ downsets.

  • A node is 𝛼-critical if it dominates and is dominated by at least 𝛼 nodes.

    Fact: By inspecting an 𝛼-critical node, we can eliminate 𝛼 nodes:

    Main Idea: Critical Nodes

  • A node is 𝛼-critical if it dominates and is dominated by at least 𝛼 nodes.

    Fact: By inspecting an 𝛼-critical node, we can eliminate 𝛼 nodes:

    Main Idea: Critical Nodes

    9-critical

  • A node is 𝛼-critical if it dominates and is dominated by at least 𝛼 nodes.

    Fact: By inspecting an 𝛼-critical node, we can eliminate 𝛼 nodes:

    Main Idea: Critical Nodes

    9-critical

  • A node is 𝛼-critical if it dominates and is dominated by at least 𝛼 nodes.

    Fact: By inspecting an 𝛼-critical node, we can eliminate 𝛼 nodes:

    Main Idea: Critical Nodes

    9-critical

  • T(|V|) ≀ 2T(|𝑉| βˆ’ 𝛼) if |𝑉| > π‘˜

    2π‘˜ if |𝑉| ≀ π‘˜

    Critical Nodes β‡’ Main Theorem

  • T(|V|) ≀ 2T(|𝑉| βˆ’ 𝛼) if |𝑉| > π‘˜

    2π‘˜ if |𝑉| ≀ π‘˜

    Main Lem: Every π‘˜-mixing poset (𝑉, β‰Ί) with paths 𝑃1, 𝑃2, … , π‘ƒπ‘˜ has a

    Ξ©(𝑑3/2)-critical element, where 𝑑 =|𝑉|

    π‘˜is ”average” length of a path.

    Critical Nodes β‡’ Main Theorem

  • T(|V|) ≀ 2T(|𝑉| βˆ’ 𝛼) if |𝑉| > π‘˜

    2π‘˜ if |𝑉| ≀ π‘˜

    Main Lem: Every π‘˜-mixing poset (𝑉, β‰Ί) with paths 𝑃1, 𝑃2, … , π‘ƒπ‘˜ has a

    Ξ©(𝑑3/2)-critical element, where 𝑑 =|𝑉|

    π‘˜is ”average” length of a path.

    Unfolding the recurrence leads to a geometric series.

    Which gives 𝑇 |𝑉| ≀ 𝐢|𝑉|.

    Critical Nodes β‡’ Main Theorem

  • 𝑆1 𝑆2

    Constructing Disjoint Subpaths

    We construct a partition into subpaths 𝑆1, … , π‘†π‘˜ s.t.,

    β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If a node 𝑣 dominates π‘š nodes

    in its subpath 𝑆𝑖 then it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆3 𝑆4

    𝑃1𝑃1

  • >1

    2of nodes

    Finding a half-Critical Node

    Claim: Most nodes dominates Ξ© 𝑑3/2 nodes.

    k-mixing: Every set π‘ˆ intersects |π‘ˆ| of paths 𝑃1, … , π‘ƒπ‘˜

    Partition: Define subpaths 𝑆1, 𝑆2, … , π‘†π‘˜ such that:β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If 𝑣 dominates π‘š nodes in 𝑆𝑖 then

    it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆1 𝑆2 𝑆3 𝑆4 π‘†π‘˜β€¦

    𝑑

    2levels

  • >1

    2of nodes

    Finding a half-Critical Node

    Claim: Most nodes dominates Ξ© 𝑑3/2 nodes.

    k-mixing: Every set π‘ˆ intersects |π‘ˆ| of paths 𝑃1, … , π‘ƒπ‘˜

    Partition: Define subpaths 𝑆1, 𝑆2, … , π‘†π‘˜ such that:β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If 𝑣 dominates π‘š nodes in 𝑆𝑖 then

    it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆1 𝑆2 𝑆3 𝑆4 π‘†π‘˜β€¦

    𝑑

    2levels

  • 𝑑

    4levels

    >1

    2of nodes

    Finding a half-Critical Node

    𝑑

    4levels

    Claim: Most nodes dominates Ξ© 𝑑3/2 nodes.

    β€’ Red node dominates 𝑑/4 green nodes

    k-mixing: Every set π‘ˆ intersects |π‘ˆ| of paths 𝑃1, … , π‘ƒπ‘˜

    Partition: Define subpaths 𝑆1, 𝑆2, … , π‘†π‘˜ such that:β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If 𝑣 dominates π‘š nodes in 𝑆𝑖 then

    it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆1 𝑆2 𝑆3 𝑆4 π‘†π‘˜β€¦

  • 𝑑

    4levels

    >1

    2of nodes

    Finding a half-Critical Node

    𝑑

    4levels

    Claim: Most nodes dominates Ξ© 𝑑3/2 nodes.

    β€’ Red node dominates 𝑑/4 green nodes

    β€’ Green nodes mix, so belong to Ξ©( 𝑑) paths.

    k-mixing: Every set π‘ˆ intersects |π‘ˆ| of paths 𝑃1, … , π‘ƒπ‘˜

    Partition: Define subpaths 𝑆1, 𝑆2, … , π‘†π‘˜ such that:β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If 𝑣 dominates π‘š nodes in 𝑆𝑖 then

    it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆1 𝑆2 𝑆3 𝑆4 π‘†π‘˜β€¦

  • 𝑑

    4levels

    >1

    2of nodes

    Finding a half-Critical Node

    𝑑

    4levels

    Claim: Most nodes dominates Ξ© 𝑑3/2 nodes.

    β€’ Red node dominates 𝑑/4 green nodes

    β€’ Green nodes mix, so belong to Ξ©( 𝑑) paths.β€’ Every green node dominates at least 𝑑/4 in

    every path that it appears.

    k-mixing: Every set π‘ˆ intersects |π‘ˆ| of paths 𝑃1, … , π‘ƒπ‘˜

    Partition: Define subpaths 𝑆1, 𝑆2, … , π‘†π‘˜ such that:β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If 𝑣 dominates π‘š nodes in 𝑆𝑖 then

    it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆1 𝑆2 𝑆3 𝑆4 π‘†π‘˜β€¦

  • 𝑑

    4levels

    >1

    2of nodes

    Finding a half-Critical Node

    𝑑

    4levels

    Claim: Most nodes dominates Ξ© 𝑑3/2 nodes.

    β€’ Red node dominates 𝑑/4 green nodes

    β€’ Green nodes mix, so belong to Ξ©( 𝑑) paths.β€’ Every green node dominates at least 𝑑/4 in

    every path that it appears.

    β€’ The red node dominates Ξ©(𝑑3/2) blue nodes.

    k-mixing: Every set π‘ˆ intersects |π‘ˆ| of paths 𝑃1, … , π‘ƒπ‘˜

    Partition: Define subpaths 𝑆1, 𝑆2, … , π‘†π‘˜ such that:β€’ Each 𝑆𝑖 βŠ† 𝑃𝑖.β€’ If 𝑣 dominates π‘š nodes in 𝑆𝑖 then

    it dominates π‘š nodes in all 𝑆𝑗 where 𝑣 ∈ 𝑃𝑗.

    𝑆1 𝑆2 𝑆3 𝑆4 π‘†π‘˜β€¦

  • Existence of a Critical Node

    Main Lem: Every π‘˜-mixing POSET with paths has a Ξ©(𝑑3/2)-critical element, where 𝑑 is ”average” length of a path.

    Most nodes dominate Ξ©(𝑑3/2) nodes.

    Most nodes are dominated by Ξ©(𝑑3/2) nodes.

    Therefore, there is an Ξ©(𝑑3/2)-critical node.

  • Proof Summaryπ‘˜- mixing POSETs have Ξ©(𝑑3/2) critical nodes

    π‘˜-mixing POSETs have at most πΆπ‘˜ downsets

    #downsets in rotation POSETs is at most 𝐢𝑛

    #stable matchings is at most 𝐢𝑛

    Rotation POSETs are 2n-mixing

  • β€’ Getting close to the 2.28𝑛 lower bound?β€’ Our current bound is about 217𝑛

    β€’ Counting algorithms for estimating

    β€’ #Stable Matchings [Dyer-Goldberg-Greenhill-Jerrum’04,Chebolu-Goldberg-Martin’12]

    β€’ General case equivalent to β€’ #Downsets of a POSET

    β€’ #Independent sets in bipartite graphs

    Future directions

  • β€’ Getting close to the 2.28𝑛 lower bound?β€’ Our current bound is about 217𝑛

    β€’ Counting algorithms for estimating

    β€’ #Stable Matchings [Dyer-Goldberg-Greenhill-Jerrum’04,Chebolu-Goldberg-Martin’12]

    β€’ General case equivalent to β€’ #Downsets of a POSET

    β€’ #Independent sets in bipartite graphs

    Future directions

    Any questions?