From the marriage problem to sailor assignment and Google AdWords problems G. Hernandez UofM / UNAL Colombia MATCHING PROBLEMS OLD AND NEW: Based on: “The

From the marriage problem to sailor assignment and Google AdWords problems

G. HernandezUofM / UNAL Colombia

MATCHING PROBLEMS OLD AND NEW:

Based on:• “The Mathematics Of 1950’s Dating: Who wins The Battle of The Sexes?”` from Great Theoretical Ideas in Computer Science http://www.cs.cmu.edu/~15251/index.html •“New Market Models and Algorithms'‘ talk form V. Vazirani http://www.cc.gatech.edu/~vazirani/

Outline1. Matching , Assignment and Correspondence

Problems

2. The Stable Marriage Problem

Gale-Shapley Algorithm

3. Linear Assignment Problem

Kuhn-Munkres Algorithm

4. The Sailor Assignment Problem

KM extension

EMOAs, HMOAs

5. The Google AdWords Problem

1. Matching's , assignments and correspondence

problems

Question: How do we pair, match, assign, make them correspond off in

an optimal way ?

There are n (agents, boys, sailors, bets, …) and n (tasks, girls, jobs, adwords, …)

We may have restrictions on the agents can perform the particular tasks

We may have preference lists or a cost of assigning an agent to a task, or both

The Traditional Marriage Problem

2. The Stable Marriage Problem

Taken from http://www.cs.cmu.edu/~15251/index.html

Question: How do we pair them off?

Dating Scenario

There are n boys and n girls

Each girl has her own ranked preference list of all the boys

Each boy has his own ranked preference list of the girlsThe lists have no ties

3,5,2,1,4

1

5,2,1,4,3

2

4,3,5,1,2

3

1,2,3,4,5

4

2,3,4,1,5

5

1

3,2,5,1,4

2

1,2,5,3,4

3

4,3,2,1,5

4

1,3,4,2,5

5

1,2,4,5,3

More Than One Notion of What Constitutes A “Good” Pairing

Maximizing total satisfaction

Hong Kong and to an extent the USA

Maximizing the minimum satisfactionWestern Europe

Minimizing maximum difference in mate ranksSweden

Maximizing people who get their first choiceBarbie and Ken Land

Rogue Couples - InstabilitySuppose we pair off all the boys and girls

Now suppose that some boy and some girl prefer each other to the people to whom they are paired

They will be called a rogue couple

Why be with them when we can be with each other?

What use is fairness, if it is not stable?

•Any list of criteria for a good pairing for “marriage” must include stability.

•If the pairing can be challenged in an open market it is doomed if it contains a rogue couple.

A pairing of boys and girls is called stable if it contains no rogue couples

Stable Pairings

A pairing of boys and girls is called stable if it contains no rogue couples

Stable Pairings

3,2,1

1

2,1,3

2

3,1,2

3

1

3,2,1

2

1,2,3

3

3,2,1

The Traditional Marriage Algorithm

Worshipping MalesFemale

String


For each day that some boy gets a “No” do:

Morning• Each girl stands on her balcony

• Each boy proposes to the best girl whom he has not yet crossed off

Afternoon (for girls with at least one suitor)• To today’s best: “Maybe, return tomorrow”• To any others: “No, I will never marry you”

Evening• Any rejected boy crosses the girl off his list

If no boys get a “No”, each girl marries boy to whom she just said “maybe”

Gale-Shapley


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1

X


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1

X


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1

XX


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1

XX


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1

XX


3,2,1

1

2,1,3

2

3,1,2

3

1

3,1,2

2

1,2,3

3

3,2,1

The TMA always terminates in at most n2 days.

TMA always produces a male-optimal, female-pessimal pairing.

A boy’s optimal girl is the highest ranked girl for whom there is some stable pairing in which the boy gets her

A “master list” of all n of the boys lists starts with a total of n x n = n2 girls on itEach day that at least one boy gets a “No”, so at least one girl gets crossed off the master listTherefore, the number of days is bounded by the original size of the master list

TMA Related problems!

Weighted matching Stable roommates hospitals/residents problem hospitals/residents problem with couples

Current real applications of TMA!

National Resident Matching Program

The National Resident Matching Program (NRMP) is a private, not-for-profit corporation established in 1952 to provide a uniform date of appointment to positions in graduate medical education (GME) in the United States.

http://www.nrmp.org

3. The Linear Assignment Problem

Agents Tasks

Complete Bipartite Graph

1

2

1

3

2

3

n n

r0,0

rn,n

n! assignments

.

...

r1,1 r1,2 r1,3 … r1,n

r2,1 r2,2 r2,3 … r2,n

:

rn,1 rn,2 rn,3 … rn,n

.

.

.

.

.

.

.

LAP Kuhn-Munkres Algorithm The Kuhn- Munkers (Hungarian) algorithm is used to find an optimum (maximum or minimum weight) matching in a complete bipartite graph in time O(n3 )

Step 1: For each row of the matrix, find the smallest element and subtract it from every element in its row.

LAP Kuhn-Munkres AlgorithmStep 2: Find a zero (Z) in the resulting matrix. If there is no starred zero in its row or column, star Z. Repeat for each element in the matrix.

Step 3: Cover each column containing a starred zero. If n columns are covered, the starred zeros describe a complete set of unique assignments. In this case STOP.

LAP Kuhn-Munkres AlgorithmStep 2: Find a zero (Z) in the resulting matrix. If there is no starred zero in its row or column, star Z. Repeat for each element in the matrix.

Step 3: Cover each column containing a starred zero. If n columns are covered, the starred zeros describe a complete set of unique assignments. In this case STOP.

LAP Kuhn-Munkres AlgorithmStep 4: Find a uncovered zero and prime it. If there is no starred zero in the row containing this primed zero, Go to Step 5. Otherwise, cover this row and uncover the column containing the starred zero. Continue in this manner until there are no uncovered zeros left. Save the smallest uncovered value and Go to Step 6.

Step 5: Construct a series of alternating primed and starred zeros as follows. Let Z0 represent the uncovered primed zero found in Step 4. Let Z1 denote the starred zero in the column of Z0 (if any). Let Z2 denote the primed zero in the row of Z1 (there will always be one). Continue until the series terminates at a primed zero that has no starred zero in its column. Unstar each starred zero of the series, star each primed zero of the series, erase all primes and uncover every line in the matrix. Return to Step 3.

Step 6: Add the value found in Step 4 to every element of each covered row, and subtract it from every element of each uncovered column. Return to Step 4 without altering any stars, primes, or covered lines.

LAP Kuhn-Munkres Algorithm

Step: 4 Step: 6 Step: 4

Step: 5 Step: 3 Final Solution

XML DB

Jobs database

Sailors120,000/year1,000-10,000 to be assigned every two weeks

Commanders

Detailers200

4. The Sailor Assignment Problem

Four objectivesTS

PCSSRCR

MK algorithm applied to SAP

1. SAP is multi-objective whereas LAP is single-objective.

• Solution: use of weight vectors

2. In case of SAP, we do not have a complete bipartite graph (many infinite values for big instances with).

• Solution: sparse matrix represent.

3. Sometimes we do not have a complete matching (conflicts), in those case MK does not work.

• Solution: The use of dummy jobs.

ProblemsSailors Jobs

2

3

4

5

1

2

3

4

5

6

7

1

dummy

Weight vectors

1

2

3

.

.

.

n

1 r1,1 2 r1,2 k r1,k

…

….

….

….

FTS1,1

FPCS1,1

FSR1,1

FCR1,1

Job ID Sailor-Job Information

Reduced rating

Sailor Array (List)

Sparse Matrix representation

r0,0 r0,1 …

r1,0 r1,1 … :

rn,0 rn, 1 …

r0,0 r0,1 … rd0 …

r1,0 r1,1 … rd1 …

: :rn,0 rn, 1 … … rdn

After adding dummy jobs in the matrixDummy jobs

EMOAs = NSGAII , SPEA2

RESULTSKM(287)

NSGAII

NGSA(300)-KM (5)

4. The AdWords Problem

Paid for each “impression”

WebQUERY

Pais when someone clicks on

GoogleYahoo!MSN

New algorithmic and game-theoretic questions

Monika Henzinger, 2004: Find on-line algorithm to maximize Google’s revenue.

Queries are coming on-line. Instantaneously decide which bidder gets it.

APP

The Adwords Problem

The Adwords Problem:N advertisers;

Daily Budgets B1, B2, …, BN Each advertiser provides bids for keywords he is interested

in.

Search Engine



in.

Search Enginequeries (online)



in.

Search EngineSelect one Ad

Advertiser pays his bid

queries (online)



in.



queries (online)

Maximize total revenue

Online competitive analysis - compare with best offline allocation



in.



queries (online)

Maximize total revenue

Example – Assign to highest bidder: only ½ the offline revenue

Example:

$1 $0.99

$1 $0

Book

CD

Bidder1 Bidder 2

B1 = B2 = $100

Queries: 100 Books then 100 CDs

Bidder 1 Bidder 2

Algorithm Greedy

LOST

Revenue100$

Example:

$1 $0.99

$1 $0

Book

CD

Bidder1 Bidder 2

B1 = B2 = $100

Queries: 100 Books then 100 CDs

Bidder 1 Bidder 2

Optimal Allocation

Revenue199$

Generalizes online bipartite matching

Each daily budget is $1, and

each bid is $0/1.

Online bipartite matching

advertisers

(boys)

queries

(girls)


advertisers

(boys)

queries

(girls)


advertisers

(boys)

queries

(girls)


advertisers

(boys)

queries

(girls)


advertisers

(boys)

queries

(girls)


advertisers

(boys)

queries

(girls)


advertisers

(boys)

queries

(girls)


Karp, Vazirani & Vazirani, 1990:Online optimal (1-1/e =0.63)n matching .

2n nodes bipartite graph Perfect matching (n pairs).

Any deterministic online algorithm has expected value <=

RANDOM (adversary) 1/2n

RANDOM Algorithm1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 . . .

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 . . .

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0


Online bipartite matchingadvertisers

(boys)

queries

(girls)

::

::

n/2

n/2


(boys)

queries

(girls)

::

::

n/2

n/2


(boys)

queries

(girls)

::

::

n/2

Any deterministic online algorithm has expected value on this instance <= (1-1/e)n


Karp, Vazirani & Vazirani, 1990: RANKING:

Rank by random permutation the boys vertices at the beging As each girl arrives, match her to the eligible boy, if any, of

highest rank.

Expected number of pairs n (1-1/e) + o(n)

Optimal.


Kalyanasundaram & Pruhs, 2000: On-line deterministic algorithm:

1-1/e factor algorithm for b-matching:

Daily budgets $b, bids $0/1, b>>1

Awards the query to the interested

advertiser who has the highest unspent budget.

Adwords Problem

Mehta, Saberi, Vazirani & Vazirani, 2005: 1-1/e algorithm, assuming budgets>>bids.

bids = 0, 1 or 2. Natural extension of KP00 that assigns the query to

the highest bidder, in case of ties: the largest remaining budget. strictly smaller and Bounded Away from 1-1/e.

Adwords Problem

Mehta, Saberi, Vazirani & Vazirani, 2007:

1-1/e algorithm, assuming budgets>>bids. A bidder pays only if the user clicks on his ad. Advertisers have different daily budgets. Instead of charging a bidder his actual bid, the search engine company

charges him the next highest bid. Multiple ads can appear with the results of a query. Advertisers enter at different times.

Optimal!

New Algorithmic Technique

Idea: Use both bid and

fraction of left-over budget


Idea: Use both bid and

fraction of left-over budget

Correct tradeoff given by

tradeoff-revealing family of LP’s


ψ(x) = 1 – e -x

The optimal tradeoff function is:

Algorithm

Allocate the next query to the bidder i maximizing the product of his bid and ψ(T(i)), where T(i) is the fraction of the bidder´s budget which has been spent so far, i.e., T(i) = mi / bi , where bi is the total budget of bidder i, mi is the amount of money spent by bidder i when the query arrives.

The algorithm assumes that the daily budget of advertisers is large compared to their bids.

Randomized Technique

The randomized algorithm CHOSES a random permutation of ADVERTISERS in advance, and rank the ads in order of

Where d is the bid and n is the total number of

advertisers and rank is the advertiser’s rank in the permutation

d*ψ(rank / n)

Algorithm

Google, then, will maximize its profits if it allocates queries to the advertiser with the highest value of d*ψ(f), where d is the bid and f is rank of the fraction of the budget that has not yet been spent.

Research directions

The current algorithm assumes that Google has no advance information about each day´s sequence of queries when, in fact, it does.

Theoretic Angle: A ranking algorithm should minimize manipulation of the market by encouraging advertisers to reveal their true valuations for a given keyword and their true daily budgets.

What is needed today?

An inherently algorithmic theory of

market equilibrium

New models that capture new markets

Beginnings of such a theory, within

Algorithmic Game Theory

Started with combinatorial algorithms

for traditional market models

New market models emerging

Open issues

Strongly poly algs for approximatingnonlinear convex programsequilibria

Insights into congestion control protocols?

Open issues

Borgs et al, gave some evidence that it is impossible to design a truthful mechanism in the presence of budget constraints. [BCI+05]

References D. Gale and L. S. Shapley: College Admissions and the Stability of

Marriage, American Mathematical Monthly 69, 9-14, 1962. R.M. Karp, U.V. Vazirani, and V.V. Vazirani. An optimal algorithm for

online bipartite matching. In proceedings of the 22nd Annual ACM Symposium on Theory of Computing, 1990.

Mehta, A. Saberi, A. Vazirani, U. Vazirani, V. AdWords and Generalized On-line Matching. Journal of the ACM 2007.

C. Borgs, J. Chayes, N. Inmmorlica, M. Mahdian, and A. Saberi. Multi-unit auctions with budget-constrained bidders. In ACM conference on electronic commerce. 2005.

B. Kalyanasundaram and K.R. Pruhs. An optimal deterministic algorithm for online b -matching. Theoretical Computer Science, 233(1{2):319{-25, 2000.

Computer Scientists Optimize Innovative Ad Auction, SIAM News, Volume 38, Number 3, April 2005.

Documents

From the marriage problem to sailor assignment and Google AdWords problems G. Hernandez UofM / UNAL Colombia MATCHING PROBLEMS OLD AND NEW: Based on: “The