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Chapter 7 Linear Programming I. Simplex Method Ding-Zhu Du

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Chapter 7 Linear Programming. Ding-Zhu Du. I. Simplex Method. LP examples. - PowerPoint PPT Presentation

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Page 1: Chapter 7 Linear Programming

Chapter 7Linear Programming

I. Simplex Method

Ding-Zhu Du

Page 2: Chapter 7 Linear Programming
Page 3: Chapter 7 Linear Programming
Page 4: Chapter 7 Linear Programming
Page 5: Chapter 7 Linear Programming
Page 6: Chapter 7 Linear Programming
Page 7: Chapter 7 Linear Programming

LP examples

• A post office requires different numbers of full-time employees on different days of the week. The number of full-time employees required on each day is given in the table. Union rules state that each full-time employee must work five consecutive days and then receive two days off. The post office wants to meet its daily requirements using only full-time employees. Formulate an LP that the post office can use to minimize the number of full-time employees that must be hired.

Page 8: Chapter 7 Linear Programming
Page 9: Chapter 7 Linear Programming
Page 10: Chapter 7 Linear Programming
Page 11: Chapter 7 Linear Programming
Page 12: Chapter 7 Linear Programming

6032 yx

yxz 54

Feasible domain

Optimal occurs at a vertex!!!

Page 13: Chapter 7 Linear Programming

.0,9,0

6032 s.t.

54 min

wyx

wyx

yxzax

Slack Form

.)(rank

.0

s.t.

min

mA

x

bAx

cxzx

Page 14: Chapter 7 Linear Programming

What’s a vertex?

. ,),(2

1

if

vertexa called is polyhadren ain point A

zyxzyzyx

x

Page 15: Chapter 7 Linear Programming

. of sin vertice found becan it then

solution, optimalan has over min If

.}0|{Let

xcx

Ax = b, xx =

Fundamental Theorem

Page 16: Chapter 7 Linear Programming

.constraint oneleast at violates' is, that ,in not

'point a havemust line theThus, line.any contain not

does However, solutions. optimal are *)( line

on points feasible all that followsIt solutions. optimal

also are and that means This .

havemust we2,)( and ,

,* Since distinct. are ,*, and 2/)(*

such that ,exist thereis, that not, is * suppose

on,contraditiBy . of vertex a is * that show willWe

solutions. optimal all among components zero of

number maximum with *solution optimalan Consider

x

x

y-xx*+

zy czcx* = cy =

/cy+czcx* = czcx*

cycxzyxzyx

zyx

x

x

Proof.

Page 17: Chapter 7 Linear Programming

ion.contradict a

,*than component -zero more one has which ,* and

'between solution optimalan findeasily can weNow,

.0 with somefor 0 constraint a violate

must ' Hence, 0. constraint latecannot vio '

that means This .any for 0 toequal is *)( of

component th theTherefore, .0* havemust

we,0 and 2/)( since ,0for

Moreover, . constraint latecannot vio ' Thus,

.*))(( ,any for that Note

xx

x

*>xjx

xxx

y-xx*+

i==x=yz

,zy+zy*=x* =x

Ax = bx

= by-xx*+A

jj

i

iii

iiiiii

Proof (cont’s).

Page 18: Chapter 7 Linear Programming

t.independenlinearly

are 0 with 1for all if

only and if vertex a is point feasible a

Then . ofcolumn th thedenote Let

jj

j

xnja

x

Aja

Characterization of Vertex

Page 19: Chapter 7 Linear Programming

Proof

.2

and ,

0, smallly sufficientfor and 0Then

.0 if 0

0 if

settingby )( Define zero. are

allnot and 0such that 0 with for

exists e then thert,independenlinearly not are 0for If

}.0|{set index by determineduniquely are

then t,independenlinearly are 0for If

0

zyxdxzdxy

d

x

xd

dd

axj

xa

xjx

xa

j

jjj

j

x jjjjj

jj

jj

jj

j

Page 20: Chapter 7 Linear Programming

Basic Feasible Solution

0. and

|| = m = )(rank ifonly and if basis feasible

a is subset index an Then .)( Denote

0}.|{such that solution

feasible basic a exists thereif feasible is basisA

basis. a called torscolumn vec of

subsett independen maximum a ofset index The

solution. feasible basic a called also isA vertex

1

bA

IA

II, ja=A

xjIx

I

I

jI

j

Page 21: Chapter 7 Linear Programming

Optimality Condition

condition.acy nondegenerunder

0

ifonly and if optimal is Moreover,

}.|{ where

0 and with solution feasible

basic a with associated is basis feasibleEach

1

1

IIII

III

AAcc

x

IjjI

xbAxx

I

Page 22: Chapter 7 Linear Programming

Sufficiency

.0at minimum the

reaches ,0 and 0 Since

)(1

11

11

I

IIIII

IIIIIIIIIII

IIIII

IIII

x

cxxAAcc

xAAccbAcxcxccx

xAAbAx

bxAxA

bAx

Page 23: Chapter 7 Linear Programming

Nondegeneracy Assumption

.0 , basis feasibleevery For 1 bAI I

Remark

solution. basic feasible oneexactly with associated is

basis feasibleevery condition,acy nondegenerUnder

Page 24: Chapter 7 Linear Programming

Necessary

.,...,2,1 allfor 0 1. Case

.' and )( Denote

optimal.not is 0

thatshow We.0' ,* somefor Assume

.' Denote

*

11

1

*

1

mia

bAbAAa

bA

x

xx

cIj

AAccc

ij

IIij

I

I

I

j

IIIII

Page 25: Chapter 7 Linear Programming

solution. optimal

an givenot do and 0 So,

. as togoes aluefunction v

object theHence, solution. feasible a is

1 and if '

* if

* and if 0

0,any for Then

.,...,2,1 allfor 0 1. Case

1

*

)(

*

bAxx

aIjab

jj

jjIj

x

mia

III

ijiji

j

ij

Page 26: Chapter 7 Linear Programming

.assumptionacy nondegenerby 0' since

''

aluefunction vobject ofsolution

feasible basic with basis feasible new a is 'Then

*}.{})'{\('Set

.1 with 'Let

. 0 |min

such that * Choose

.,...,2,1 somefor 0 2. Case

*

**

**

'*

***

*

*

i

Iji

ijI

ji

ijij

i

ji

i

ij

b

bca

bcbc

I

jjII

aIj

aa

b

a

b

i

mia

(pivoting)

Page 27: Chapter 7 Linear Programming

Simplex Method

method.

simplex called g,propromminlinear thesolve

tomethod a givesy necessarit of proof The

Page 28: Chapter 7 Linear Programming

Simplex Table

1 0 0 2 1 4 36

0 1 0 5 2 2 24

0 0 1 3 1 1 30

0 0 0 2 1 3

0,,,,,

3624

24522

303 s.t.

23 min

654321

6321

5321

4321

321

z

xxxxxx

xxxx

xxxx

xxxx

xxxz

Page 29: Chapter 7 Linear Programming

1 2 1 4 36

1 5 2 2 24

1 3 1 1 30

2 1 3

}6,5,4{ 0

z

I

Page 30: Chapter 7 Linear Programming

1 2 1 4 36

1/2 5/2 1 1 12

1 3 1 1 30

2 1 3

}2{})5{\}6,5,4({ 1

z

I

Page 31: Chapter 7 Linear Programming

1 1/2 21/ 0 3 24

1/2 5/2 1 1 12

2/1 1 1/2 0 0 18

1/2 1/2 0 2 12

}2{})5{\}6,5,4({ 1

z

I

Page 32: Chapter 7 Linear Programming

1 1/2 21/ 0 3 24

1/2 5/2 1 1 12

2/1 1 1/2 0 0 18

1/2 1/2 0 2 12

}6,2,4{ 1

z

I

Page 33: Chapter 7 Linear Programming

1/3 1/6 61/ 0 1 8

1/2 5/2 1 1 12

2/1 1 1/2 0 0 18

1/2 1/2 0 2 12

}1{})6{\}6,2,4({ 2

z

I

Page 34: Chapter 7 Linear Programming

1/3 1/6 61/ 0 1 8

3/1 2/3 8/3 1 0 4

2/1 1 1/2 0 0 18

2/3 1/6 1/6 0 0 28

}1,2,4{ 2

z

I

Page 35: Chapter 7 Linear Programming

Puzzle 1

? basis feasible

1st or thesolution feasible basic1st thefind wedo How

Page 36: Chapter 7 Linear Programming

Artificial Feasible Basis

m

m

z

z

z

z

zx

bIzAx

zzz

2

1

21

where

0 ,0

s.t.

min

Page 37: Chapter 7 Linear Programming

Puzzle 2

LP? solve

tohow hold,not does assumptionacy nondegenerWhen

Page 38: Chapter 7 Linear Programming

lexicographical ordering

.0 if positivehocally lexicograp is

A vector .1 somefor ...

if , as written ,n larger tha

hicallylexicograp be tosaid is .)...(

and )...,( vectorswoConsider t

1111

21

21

L

iiii

L

n

n

x >x

n i y, x=y, x, =yx

yx >y

x,y,,yyy=

x,,xxx=

Page 39: Chapter 7 Linear Programming

Method

positive.hically lexicograp is row topthe

except blesimplex ta initial in the rowevery that makes This

columns. first at the placed is basis feasible initial the

such that columns of ordering therearrange Initially,

m

n

Page 40: Chapter 7 Linear Programming

Method(cont’)

pivoting.

under preserved be willrow top theother than rows all of

spositveneshically lexicograp that theguarantees choice This

.0'for )'

' ... ,

'

' ,

'

'( among one

smallesthocally lexicograp theis )'

' ... ,

'

' ,

'

'(such that

choose we, of choiceFor

'''

1

'

''

'

''

1'

''

'

ijij

in

ij

i

ij

i

ji

ni

ji

i

ji

i

aa

a

a

a

a

b

a

a

a

a

a

bi'

i'

Page 41: Chapter 7 Linear Programming

Method (cont’)

.0

such that basis feasibleith solution w basic feasible optimalan

findor solution optimal of cenonexisten findseither algorithm theTherefore,

ing.nonincreas is aluefunction v objective theand oncemost at basis feasible

each visit algorithm that theguarantee rules additional above theTherefore,

ordering. hicallexicograpin strictly decreasse top themake pivot will

each positive,hically lexicograp are row top theother than rows all Since

1 AAc - c

I

II

Page 42: Chapter 7 Linear Programming

Theorem

optimal. is with associatedsolution feasible

basic then the0, satisfies basis feasible a if Moreove,

0. such that basis

feasible with associatedsolution feasible basic optimalan hasit then

solution, optimalan has gprogramminlinear theIf

1

1

I

AAc - cI

AAc - cI

II

II

Page 43: Chapter 7 Linear Programming

Puzzle 3Method?Simplex of timerunning theisWhat

Page 44: Chapter 7 Linear Programming

Complexity of LP

time.-polynomialin computed becn

point extreme optimalan solution, optimalan Given

time.)(in runs MethodPoint Interior

time.)(in runs Method Ellepsoid

time.lexponentiain runs MethodSimplex

5.3

6

nO

nO

Page 45: Chapter 7 Linear Programming

Thanks, End