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Recent Applications of Linear Programming in Memory of George Dantzig. Yinyu Ye Department if Management Science and Engineering Stanford University ISMP 2006. Outline. LP in Auction Pricing Parimutuel Call Auction Proving Theorems using LP Uncapacitated Facility Location

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Recent Applications of Linear Programming in Memory of George Dantzig

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Recent applications of linear programming in memory of george dantzig l.jpg

Recent Applications of Linear Programmingin Memory of George Dantzig

Yinyu Ye

Department if Management Science and Engineering

Stanford University

ISMP 2006


Outline l.jpg

Outline

  • LP in Auction Pricing

    • Parimutuel Call Auction

  • Proving Theorems using LP

    • Uncapacitated Facility Location

    • Core of Cooperative Game

  • Applications of LP Algorithms

    • Walras-Arrow-Debreu Equilibrium

    • Linear Conic Programming

  • Photo Album of George

    (Applications presented here are by no means complete)


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Outline

  • LP in Auction Pricing

    • Parimutuel Call Auction

  • Proving Theorems using LP

    • Uncapacitated Facility Location

    • Core of Cooperative Game

  • Applications of LP Algorithms

    • Walras-Arrow-Debreu equilibrium

    • Linear Conic Programming

  • Photo Album of George


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World Cup Betting Example

  • Market for World Cup Winner

    • Assume 5 teams have a chance to win the 2006 World Cup:

      Argentina, Brazil, Italy, Germany and France

    • We’d like to have a standard payout of $1 if a participant has a claim where his selected team won

  • Sample Orders


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Markets for Contingent Claims

  • A Contingent Claim Market

    • S possible states of the world (one will be realized).

    • N participants who (say j), submit orders to a market organizer containing the following information:

      • ai,j - State bid (either 1 or 0)

      • qj– Limit contract quantity

      • πj– Limit price per contract

    • Call auction mechanism is used by one market organizer.

    • If orders are filled and correct state is realized, the organizer will pay the participant a fixed amount w for each winning contract.

    • The organizer would like to determine the following:

      • pi – State price

      • xj– Order fill


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Central Organization of the Market

  • Belief-based

    • Central organizer will determine prices for each state based on his beliefs of their likelihood

    • This is similar to the manner in which fixed odds bookmakers operate in the betting world

    • Generally not self-funding

  • Parimutuel

    • A self-funding technique popular in horseracing betting


  • Parimutuel methods l.jpg

    Horse 1

    Horse 2

    Horse 3

    Bets

    Total Amount Bet = $6

    Outcome: Horse 2 wins

    Two winners earn $2 per bet plus stake back: Winners have stake returned then divide the winnings among themselves

    Parimutuel Methods

    • Definition

      • Etymology: French pari mutuel, literally, mutual stakeA system of betting on races whereby the winners divide the total amount bet, after deducting management expenses, in proportion to the sums they have wagered individually.

    • Example: Parimutuel Horseracing Betting


    Parimutuel market microstructure l.jpg

    LP pricing for the contingent claim market

    Parimutuel Market Microstructure

    Boosaerts et al. [2001], Lange and Economides [2001],

    Fortnow et al. [2003], Yang and Ng [2003], Peters et al. [2005], etc


    World cup betting results l.jpg

    World Cup Betting Results

    Orders Filled

    State Prices


    Outline10 l.jpg

    Outline

    • LP in Auction Pricing

      • Parimutuel Call Auction

    • Proving Theorems using LP

      • Uncapacitated Facility Location

      • Core of Cooperative Game

    • Applications of LP Algorithms

      • Walras-Arrow-Debreu equilibrium

      • Linear Conic Programming

    • Photo Album of George


    Facility location problem l.jpg

    Facility Location Problem

    Input

    • A set of clients or cities D

    • A set of facilities F withfacility cost fi

    • Connection cost Cij, (obey triangle inequality)

      Output

    • A subset of facilities F’

    • An assignment of clients to facilities in F’

      Objective

    • Minimize the total cost (facility + connection)


    Facility location problem12 l.jpg

    Facility Location Problem

    • location of a potential facility

      client

    (opening cost)

    (connection cost)


    Facility location problem13 l.jpg

    Facility Location Problem

    • location of a potential facility

      client

    (opening cost)

    (connection cost)


    Slide14 l.jpg

    R-Approximate Solution

    and Algorithm


    Slide15 l.jpg

    Hardness Results

    • NP-hard.

      Cornuejols, Nemhauser & Wolsey [1990].

    • 1.463 polynomial approximation algorithm implies NP =P.

      Guha & Khuller [1998], Sviridenko [1998].


    Slide16 l.jpg

    ILP Formulation

    • Each client should be assigned to one facility.

    • Clients can only be assigned to open facilities.


    Lp relaxation and its dual l.jpg

    LP Relaxation and its Dual

    Interpretation:clients share the cost to open a facility, and pay the connection cost.


    Slide18 l.jpg

    Bi-Factor Dual Fitting

    A bi-factor (Rf,Rc)-approximate algorithm is a max(Rf,Rc)-approximate algorithm


    Slide19 l.jpg

    Simple Greedy Algorithm

    Jain et al [2003]

    Introduce a notion of time, such that each event can be associated with the time at which it happened. The algorithm start at time 0. Initially, all facilities are closed; all clients are unconnected; all set to 0. Let C=D

    While , increase simultaneously for all , until one of the following events occurs:

    (1). For some client , and a open facility , then connect client j to facility i and remove j from C;

    (2). For some closed facility i, , then open

    facility i, and connect client with to facility i, and remove j from C.


    Time 0 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 0


    Time 1 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 1


    Time 2 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 2


    Time 3 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 3


    Time 4 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 4


    Time 5 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 5


    Time 526 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 5

    Open the facility on left, and connect clients “green” and “red” to it.


    Time 6 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    Time = 6

    Continue increase the budget of client “blue”


    Time 628 l.jpg

    F1=3

    F2=4

    3

    5

    4

    3

    6

    4

    5

    5

    6

    Time = 6

    The budget of “blue” now covers its connection cost to an opened facility; connect blue to it.


    Slide29 l.jpg

    In particular, if

    The Bi-Factor Revealing LP

    Jain et al [2003], Mahdian et al [2006]

    Given , is bounded above by

    Subject to:


    Slide30 l.jpg

    Approximation Results


    Other revealing lp examples l.jpg

    Other Revealing LP Examples

    • N. Bansal et al. on “Further improvements in competitive guarantees for QoS buffering,” 2004.

    • Mehta et al on “Adwords and Generalized Online Matching,” 2005


    Core of cooperative game l.jpg

    Core of Cooperative Game

    • A set of alliance-proof allocations of profit (Scarf [1967])

    • Deterministic game (using linear programming duality, Dantzig/Von Neumann[1948])

      • Linear Production, MST, flow game, some location games (Owen [1975]), Samet and Zemel [1984],Tamir [1991], Deng et al. [1994], Feigle et al. [1997], Goemans and Skutella [2004], etc.)

    • Stochastic game (using stochastic linear programming duality, Rockafellar and Wets [1976])

      • Inventory game, Newsvendor (Anupindi et al. [2001], Muller et al. [2002], Slikker et al. [2005], Chen and Zhang [2006], etc. )


    Outline33 l.jpg

    Outline

    • LP in Auction Pricing

      • Parimutuel Call Auction

    • Proving Theorems using LP

      • Uncapacitated Facility Location

      • Core of Alliance

    • Applications of LP Algorithms

      • Walras-Arrow-Debreu equilibrium

      • Linear Conic Programming

    • Photo Album of George


    Walras arrow debreu equilibrium l.jpg

    Walras-Arrow-Debreu Equilibrium

    The problem was first formulated by Leon Walrasin 1874,Elements of Pure Economics, or the Theory of Social Wealth

    n players, each with

    • an initial endowment of a divisible good

    • utility function for consuming all goods—own and others.

      Every player

    • sells the entire initial endowment

    • uses the revenue to buy a bundle of goods such that his or her utility function is maximized.

      Walras asked:

      Can prices be set for all the goods such that the market clears?

      Answer by Arrow and Debreu in 1954:

      yes, under mild conditions if the utility functions are concave.


    Walras arrow debreu equilibrium35 l.jpg

    Walras-Arrow-Debreu Equilibrium

    P1

    P1

    1 unit

    1

    1

    U1(.)

    P2

    P2

    U2(.)

    1 unit

    2

    2

    ........

    ........

    Pn

    Pn

    1 unit

    n

    n

    Un(.)

    Goods

    Traders


    Fisher equilibrium l.jpg

    P1

    w1

    1 unit

    1

    1

    U1(.)

    P2

    w2

    U2(.)

    1 unit

    2

    2

    ........

    ........

    Pn

    wn

    1 unit

    n

    n

    Un(.)

    Goods

    Buyers

    Fisher Equilibrium


    Utility functions l.jpg

    Utility Functions


    Equilibrium computation l.jpg

    Equilibrium Computation

    Eisenberg and Gale[1959] , Scarf [1973], Eaves [1976,1985]


    Equilibrium computation39 l.jpg

    Equilibrium Computation

    Nenakhov and Primak [1983], Jain [2004]


    Equilibrium computation40 l.jpg

    Equilibrium Computation

    [2004, 2005]


    Equilibrium computation41 l.jpg

    Equilibrium Computation

    Codenotti et al. [2005],

    Chen and Deng [2005, 2006],


    Linear conic programming l.jpg

    Linear Conic Programming

    Many excellent sessions in ISMP 2006 …


    Outline43 l.jpg

    Outline

    • LP in Auction Pricing

      • Parimutuel Call Auction

      • Core of Alliance

    • Proving Theorems using LP

      • Uncapacitated Facility Location

    • Applications of LP Algorithms

      • Walras-Arrow-Debreu equilibrium

      • Linear Conic Programming

    • Photo Album of George


    Childhood years l.jpg

    Childhood Years


    University student years l.jpg

    University Student Years


    1967 stanford or l.jpg

    1967 Stanford OR


    1975 national medal of science l.jpg

    1975 National Medal of Science


    1975 nobel laureate l.jpg

    1975 Nobel Laureate


    1987 student graduation l.jpg

    1987 Student Graduation


    2003 science fiction l.jpg

    2003 Science Fiction

    COMP

    IN OUR OWN IMAGE

    - a computer science odyssey -

    by

    George B. Dantzig

    Nach, pale and shaking,rushed in to tell Adam, Skylab’s Captain, that a biogerm plague is sweeping the Earth, killing millions like flies.

    COMP

    In Our Own Image

    Copyright © 2003 by

    George Bernard Dantzig

    All rights reserved


    2004 90 th birthday party l.jpg

    2004 90th Birthday Party

    Organized by MS&E, Stanford, November 12, 2004 (Lustig, Thapa, etc)


    2004 90 th birthday party52 l.jpg

    2004 90th Birthday Party


    2004 90 th birthday party53 l.jpg

    2004 90th Birthday Party


    Lp dantzig legacy continues l.jpg

    LP/Dantzig Legacy Continues …


    The dantzig lieberman operations research fellowship fund l.jpg

    THE DANTZIG-LIEBERMANOPERATIONS RESEARCH FELLOWSHIP FUND


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