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Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

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The trend effect that Kate, Duchess of Cambridge has on others, from cosmetic surgery for brides, to sales of coral-colored jeans.” “Kate Middleton effect Kate Middleton effect 3

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Page 1: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Lecture 2-1

Submodular Maximization

Weili Wu Ding-Zhu DuUniversity of Texas at Dallas

Page 2: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Outline• Kate Middleton effect• Max Submodular Function

2

Page 3: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

The trend effect that Kate, Duchess of Cambridge has on others, from cosmetic surgery for brides, to sales of coral-colored jeans.”

“Kate Middleton effect

Kate Middleton effect

3

Page 4: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

According to Newsweek, "The Kate Effect may be worth £1 billion to the UK fashion industry."

Tony DiMasso, L. K. Bennett’s US president, stated in 2012, "...when she does wear something, it always seems to go on a waiting list."

Hike in Sales of Special Products

4

Page 5: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

• Influential persons often have many friends.

• Kate is one of the persons that have many friends in this social network.

For more Kates, it’s not as easy as you might think!

How to Find Kate?

5

Page 6: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

•Given a digraph and k>0,

•Find k seeds (Kates) to maximize the number of influenced persons (in one step).

Domination Maximization

6

Page 7: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

7

. toequal isunion whosesubsets theof find,0integer and},,...,{set ground a of

,..., subsets of collection aGiven :Cover-SetMax Domination Cover-Set

hard.- isMax Domination

1

1

UkkuuU

SS

NP

n

m

pm

Theorem

Proof

1S 2S mS

1u nu2u

ji Su

nodes influence seeds solution hasCover -Set knk

Page 8: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Outline• Kate Middleton effect• Submodular Maximization

8

Page 9: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Max Coverage

Given a collection C of subsets of a set E, find a subcollection C’ of C, with |C’|<k, to maximize the number of elements covered by C’ .

Influence Maximization is a special case of Max Coverage.

Page 10: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Max Coverage

Given a collection C of subsets of a set E, find a subcollection C’ of C, with |C’|<k, to maximize the number of elements covered by C’ .

.||)( define , of ion subcollectany For

SAfCA

As

Page 11: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Submadular Function Max

11

kSVS

Sf

Rf V

|| subject to )( max

Considerfunction. submodular increasing

monotone nonegative a be 2:Let

Page 12: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Greedy Algorithm

12

.output };{

set and )( maximize to choose do to1for

;

1

1

0

k

ii

iv

SvSS

SfVvki

S

Page 13: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Performance Ratio

13

solutionm. optimalan is * where*)()1()( 1

SSfeSf k

Theorem (Nemhauser et al. 1978)

Proof

Page 14: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Proof

14

*).()1()( i.e.,*),( Hence,

1 )11(

).(Then ).(*)( Denote))()(()(

)()()()(

}),...,{(

}){()()( *)(*)(

}.,...,,{* optimal Suppose

110

1

/11

1

1

11

1

21

21

21

SfeSfSfeaea

exaeak

a

aakaSfSfaSfSfkSf

SfSfSfSf

uuSf

uSfSfSfSSfSf

uuuS

kk

xi

kii

iiiii

iii

iuiuiui

kiu

iuiui

i

k

k

k

Monotone increasing

Why?Submodular!

Page 15: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Theorem

15

optimal. of)1(least at aluefunction v objective

ith solution wion approximatan produces which problem,on maximizati influencefor

algorithm time-polynomial a exists There

1 e

Page 16: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Exercise

16

ratio. eperformancion approximat theestimate Please

end );(\

;,..., subsets produce A to algorithm use begin do while

follows. asCover Set Min for ion approximatan design to

CoverageMax for ion approximat-)1( usemay We.1

1

1

1

k

k

SSXXSSk

X

e

Page 17: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Section 2.1-2.2

17

Page 18: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Independent System

• Consider a set E and a collection C of subsets of E. (E,C) is called an independent system if

CACBBA ,

Page 19: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Maximization

• c: E→R

max c(A) s.t. AεC • c(A) = ΣxεA c(x)

+

Page 20: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Greedy Approximation MAX

.output };{hen t

}{ if do to1for

;).()()(

ordering into in elements allSort 21

AxAA

CxAni

Axcxcxc

E

i

i

n

Page 21: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Theorem

.in

set t independen maximal of size minimum theis)( andin set t independen of size maximum theis )( where

)()(max

)(*)(

Then solution. optimalan be *Let Algorithm.Greedy by the obtained beLet

FFv

FFuFvFu

AcAc

AA

EFG

G

Page 22: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

Proof

)(|*|))()((|*|

*)( Similarly,

)(||))()((||

|)||)(|(||)(

)( }. ..., ,{ Denote

1

1

1

1

1

1

2

111

1

nn

n

i

iii

nGn

n

i

iiGi

n

i

GiGiiG

G

ii

xcAExcxcAE

Ac

xcAExcxcAE

AEAExcAExc

AcxxE

Page 23: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

. ofset t independen maximal a is is, that ),(||

show only to need We).(|*| that Note

.|||*|max

)(*)(

Therefore,

1

i

GiiGi

ii

Gi

i

niG

EAEEvAE

EuAEAEAE

AcAc

Page 24: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

. choosingnot of rule theingcontradict t,independen is }{)( So, t.independen

is }{)(such that \ a is Then therein maximalnot is set t independen Suppose

1

.

j

jGj

jGiGij

iGi

xxAE

xAEAExEAE

Page 25: Lecture 2-1 Submodular Maximization Weili Wu Ding-Zhu Du University of Texas at Dallas

THANK YOU!