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Discr

e

te S

tructur

es

Pr

opo

sitional Logic

1

Dr

. Muham

mad

Huma

y

oun

Assis

tan

t

Pr

of

ess

or

C

OMS

A

T

S

Ins

ti

tut

e

of C

omput

er

Sci

e

nce

,

Lahor

e.

mhuma

youn@ci

itl

ahor

e.e

du.pk

h

ttps://

si

te

s.

g

oo

gl

e.c

om

/a/

ci

it

lah

or

e.ed

u.pk

/d

struc

t/

1

Ins

truct

or

MS in Comput

er

Sci

ence

and En

gineering

Chal

mer

s

Uni

ver

si

ty of

T

echnol

ogy

,

S

w

eden.

PhD

in Comput

er

Sci

ence

Uni

ver

si

ty of

Gr

enobl

e,

Fr

ance.

P

os

t-doc

R

esear

ch

Fell

ow

P

ohang U

ni

ver

si

ty of

Sci

ence

and

Technol

ogy

,

South

K

or

ea.

Speci

ali

za

tion:

Human Langu

ag

e

Pr

ocessing

,

Logic

,

Pr

oof Theory

,

Da

ta

mining

2

Logi

stics

T

w

o lectur

es

per w

eek

E

ac

h l

ectur

e

requi

res

readi

n

g

cour

se

book

(a

nd a

n

op

ti

on

al

r

eadi

n

g o

f r

e

fer

ence book

s)

3

Qui

zz

es

(15%

mark

s)

3

As

si

gnmen

ts

(10

% mark

s)

Thr

ee e

xams

Sessi

onal

Ex

am 1

(10%

mark

s)

Sessi

onal

Ex

am

2

(15%

mark

s)

Termi

nal

Ex

am

(50%

mark

s)

Co

ver

s al

l

cour

se

3

Logi

stics

Con

t.

Cour

se

ma

teri

al

wi

ll

be

pos

ted

on

the c

our

se

w

eb

si

te:

h

ttp

s://

si

tes.

g

oogl

e.c

om/

a/

ci

itl

a

hor

e.edu

.pk/d

struct

/

Cour

se r

epr

esen

ta

ti

ve

(CR)

can al

so

col

lect

c

our

se

ma

teri

al

fr

om me a

ft

er ev

ery l

ectu

re

Other s

tuden

ts

ma

y g

e

t i

t fr

om hi

m

DON’

T ind

ividual

ly

appr

oac

h

me f

or the ma

terial

CR:

Ge

t c

our

se

handbook

and

thi

s

lectu

re fr

om

me

a

ft

er

this class

4

Logi

stics

Con

t.

Wha

t

is on

the w

eb

sit

e:

Cour

se Handbook

Lectur

es

As

si

gnmen

ts

P

as

t qui

zz

es

and

the

ir sol

uti

ons

Ex

ams pa

tt

ern

Ne

w

s

Visit the

w

eb

sit

e

fr

eque

n

tly

5

Plagiari

sm

Cop

ying

someone

el

se’

s

w

ork

(parti

al

or

compl

e

te) and

sub

mi

tti

ng

it

as

if

i

t w

er

e

one’

s

own

Z

er

o

toler

ance

for

plagia

rism

R

ead

cour

se

hand

book

t

o

kno

w mor

e

about

pl

agi

ari

sm

6

A

tt

endance

P

olicy

80% a

tt

end

anc

e

is mand

a

tor

y

The

stud

en

ts

fal

li

ng

shor

t

wi

ll

not b

e allo

w

ed

to

appear i

n the T

ermi

n

al

Ex

am

To

g

e

t

g

ood

gr

ade y

ou mus

t

a

tt

end

al

l

the

lectu

res and

r

ead

sug

g

es

ted

ma

teri

al

7

Cour

se Objectiv

es

Deep u

nder

st

andi

n

g

of

discr

e

te

struct

ur

es

used

in

Comp

ut

er S

cience

De

vel

opi

n

g

pr

oblem

so

lving

and

analyt

ic

al

skil

ls

De

vel

opi

n

g

alg

ori

thmic

and

compu

ta

tiona

l

skil

ls

Abi

li

ty

to under

st

and

ma

thema

tical

ar

gumen

ts

and

thei

r

desi

gn

Under

st

andin

g

of log

ic

Pr

oo

fing

techniques

8

Cour

se Objectiv

es

Think

Ma

thema

tic

ally

The v

er

y

founda

tio

n

of

Comput

er

Science

(2)

Discr

e

te

Str

uctur

es/Ma

thema

ti

cs

Disc re te ma thema tics deal s wi th objects tha t come in di scr e te bun dl e s, e. g ., 1 or 2 babi es . • Con tinuous ma thema tics deal s wi th objects tha t var y con ti nuousl y, e. g., 3.4 2 inches fr om a w al l. • Thi n k of digit al wat ches vers us analo g wat ches (ones wher e the sec ond h and loop s ar ound con ti nuousl y wi th out st oppi ng). 1 0

Dis

cr

e

te

vs

. Con

tinu

ous

Con tinu o u s Discre te 1 1

Wh

y Study Dis

cr

e

te

Str

uctur

es

It is the ma thema tics und erlyi ng al mos t all of compu ter sc ience : • Pr ogr am veri fi ca ti on – Anal yzi n g al g ori thms for co rr ectness and e ff iciency • Fi ndi n g e fficien t alg ori thm s – (f or sorti n g , sear chi n g , e tc. ) • Formal izing securi ty requi remen ts • Desi gni ng cr yp togr aph ic pr ot oc ols f or enhanced securi ty • Gr aph The or y (Ne tw ork s – both ph ysi cal & s oci al ) 1 2

Cour

se

Topics

• Founda ti ons : Logi c • Me thod s of Pr oof • Set Theory • Indu cti on and R ecur si on • Coun ti ng • R el a ti ons • Gr aphs • Tr ees • In tr oducti on of A lg ori th ms 1 3

Cour

se r

equir

es

origi

nal

thinki

ng

• Man y s tuden ts fi nd thi s cour se to be signifi ca n tly mo re c ha ll enging than other c our ses • Bec ause (amo ng other t hi ngs), it teaches ma thema tic al r eason in g an d pr ob lem sol vin g – R equi res origin al a n d de ep th in kin g • Book e xer ci ses – A w a y t o l e t you succes sfu ll y appl y concep ts usi ng your o wn cr ea ti vi ty One of the prim ary g oa ls of this co ur se: • To l earn how to a tt ack pr obl e ms tha t ma y be som e wha t di ff er en t fr om an y y ou ma y ha ve pr e vi ousl y seen 1 4

Lectur

e

Schedule

W eek s T o pic of Lectur e Rea ding Ass ig nm ent W eek 1 Fo u n d atio n s: Log ic Ch ap ter 1 ( sectio n 1 .1 an d 1.2 ), Ro sen . W eek 2 Predicate Alg eb ra Ch ap ter 1 (s ect io n 3 , 4 an d 5 ), Ro sen . W eek 3 Metho d s o f Proo f: Ch ap ter 1 (s ect io n 6 an d 7 ), Ro sen . W eek 4 Set Theo ry Ch ap ter 2 , Ro sen . W eek 5 SESS IONAL I Ex a m · Pap er w il l b e co n d u cted i n th e first lectu re o f th e w ee k · Ma rked p ap er s wil l b e sh o wn t o stu d en ts an d th e so lu tio n o f p ap er w il l b e d iscu ss ed i n th e seco n d lectu re o f th e w ee k 1 5

Lectur

e

Schedule

W ee k 6 Induc ti on and Rec ursion Chapt er 4 , Rosen . W ee k 7 Counti ng Chapt er 5 and 7, Rosen W ee k 8 Rel at ions Chapt er 8 , Rosen . W ee k 9 Revi sion wea k W ee k 1 0 S ES S ION AL I I Exam · Paper will be conduc te d i n the first le ct ure · Marke d pape rs will be show n t o student s in th e sec ond le ct ure 1 6

Lectur

e

Schedule

W ee k 1 1 Graphs Chapt er 9 , Rosen W ee k 12 Graphs Algorit hm s · Eule r and Ham il ton pat hs · Shortest pat hs proble m s · Plana r gra phs · Graph col ouring Chapt er 9 , Rosen W ee k 13 T re es · Introduc ti on · Applic at ions Chapt er 10 , Rosen W ee k 14 T re e Algorit hm s · T ra ver sal · Spanning tre es · Minim um Spanning tre es Chapt er 10 , Rosen W ee k 1 5 Introducti on o f Algorithm s · Algorit hm s · Grow th func ti on · Com ple xit y of al gorit hm s Chapte r 3 , Ros en W ee k 16 Revi sion W ee k 1 7 F ina l exa m 1 7

R

ec

omm

ended

Boo

k

s

Cour

se

Book

Disc re te Ma thema tics an d Its Ap pli ca tio ns, 6

th E

d. b y K en ne th H. R osen

R

e

fer

ence

Boo

k

s:

Discr e te Ma thema tics , 6th E d. Ri char d Johnsonb augh • Ap pli ed Disc re te Struct ur es for Comp ut er Science . P ear son E duc a ti on, Inc. Al an Doerr and K en ne th Le vasseur . • Discr e te Ma thema tics Usi

ng a Com

put er . John O ’Donne ll , Cor de li a Hal

l and R

e x P ag e. 2 n

d E

(3)

LE

C

TURE 1

Founda

tions:

Logic

Chap

ter 1 Sections 1.1 and 1.2

1

9

In

tr

od

ucti

on

Logi

c

is the

study

of the

pr

inci

p

les

an

d

me

thod

s

tha

t

di

sti

ngui

sh

es

be

tw

een

a

v

alid

an

d

an

in

v

alid

ar

gumen

t

Logi

c

dea

ls

wi

th

g

ener

al

r

easoni

n

g

la

w

s,

whi

ch y

ou

can

trus

t

2

0

Appl

ic

a

ti

ons

App

li

ed

in pr

ovi

ng

pr

ogr

am

corr

ectness

and

v

erific

a

tion

Da

tabases

(R

el

a

ti

onal

Al

g

ebr

a and

cal

cul

us)

Art

if

ici

al

In

tel

li

g

ence

2

1

Pr

opos

itio

nal

Logic

2

2

Pr

oposi

tio

n

A s

ta

teme

n

t

is

a

de

cl

ar

a

ti

ve

sen

ten

ce

It i

s

Sunda

y

toda

y (OK)

The

sun

ri

ses

fr

om

eas

t

(OK

)

Open

the

door (

an

or

der; not a s

ta

temen

t)

Ar

e

you hungr

y?

(In

terr

og

a

ti

ve;

not a s

ta

tem

en

t)

A

pr

oposi

ti

on

is a s

ta

teme

n

t

whi

ch

is ei

ther

true

or

fal

se

but

not both

ʹ

ʹ

Ͷ

It

i

s

Sunda

y

toda

y

The

sun

ri

ses

fr

om

eas

t

2

3

Truth V

alues

If a pr

oposi

ti

on

is

true, w

e

sa

y th

a

t

it

has

a

truth

v

alue

of

true

If a pr

oposi

ti

on

is

f

al

se, i

ts

tru

th

val

ue i

s

fals

e

The truth

v

al

ues

“true”

and

“f

als

e”

ar

e,

respect

iv

el

y,

denot

ed by the l

e

tt

er

s

T

and

F

2

4

Ex

amples

Pr

opo

sitio

ns

Gr

ass

is

gr

een

Ͷ

ʹ

͸

Ͷ

ʹ

͹

Ther

e

ar

e f

our

fi

n

g

er

s

in

a

hand

No

t

Pr

opo

sitio

ns

Wh

a

t

ti

me i

s

it

?

R

ead thi

s

car

e

ful

ly

Not

decl

arati

ve

sen

tenc

es

ݔ

ͳ

ʹ

ݔ

ݕ

ݖ

He

is

v

ery

ri

ch

Nei

ther

true

nor

false

2

5

Con

te

xt

If the sen

tence i

s pr

eceded

b

y other sen

tences

tha

t

mak

e the p

ronoun

or v

ari

ab

le r

e

fer

e

nce

cl

ear

,

then

the sen

tence

is

a

st

a

temen

t /

pr

oposi

ti

on

Ex

ample

:

ݔ

ͳ

ݔ

ʹ

No

w

ݔ

ʹ

i

s a

pr

opo

si

ti

o

n

wi

th

truth

-v

al

ue

FALSE

Bi

ll

Ga

tes

is an

Ame

ri

can.

He

i

s

ver

y

ri

ch.

“H

e

is v

er

y

ri

ch”

is

no

w a pr

opo

si

ti

o

n

wi

th

truth

-v

al

ue

TRU

E

‘”

ݔ

ʹ

’”

‘˜‹†‡†

–Š

ƒ–

ݔ

ͳ

2

6

Quiz

Ar

e

thes

e pr

oposi

ti

ons?

Ar

e y

ou

hu

ngry

?

ݔ

ݕ

͵

I am happ

y

It i

s r

ai

ni

ng

2

(4)

Qui

z

Ar

e

thes

e pr

oposi

ti

ons?

Ar

e y

ou

hu

ngry

?

N

O

ݔ

ݕ

͵

I am happ

y

YE

S

It i

s r

ai

ni

ng

YE

S

2

8

The ar

ea

of l

ogi

c

tha

t deal

s wi

th

pr

oposi

ti

ons

is

c

al

led the

pr

oposit

ion

a

l

calculus

or

pr

oposi

tiona

l

log

ic

It

w

as f

ir

st

de

vel

oped s

ys

tema

ti

cal

ly

b

y the

Gr

eek

phi

losopher

Ari

st

otl

e

mor

e

than

2

3

0

0

year

s

ag

o

2

9

Compound

Pr

oposi

tio

ns

Compound

pr

oposi

tio

n

s

,

ar

e

formed

fr

om

e

xi

sti

ng

pr

oposi

ti

ons

usi

ng

logi

cal

oper

a

tor

s

(al

so c

al

led

as

conn

ectiv

es

)

The me

thod

s t

o pr

oduce

ne

w pr

oposi

ti

ons

(fr

om

those

tha

t

w

e

al

ready

ha

ve) w

er

e

di

scuss

ed

b

y the Engl

ish

ma

thema

ti

ci

an

Geor

g

e

Bool

e

in

18

54

i

n

hi

s book

The Laws of

Though

t

3

0

S

ymbols

f

or Connectiv

es

S

ymbol

Mean

ing

Neg

a

ti

on

ש

Or

, di

sjun

cti

on

ר

And

,

conjuncti

on

֜

Impl

ic

a

ti

on

֞

B

i-imp

li

ca

ti

on

3

1

Neg

a

tio

n

De

finitio

n

1

Le

t

p

be a pr

oposi

ti

on.

The

negati

on

of p

, denot

ed

b

y

p

(al

so denot

ed by

݌

), i

s the

st

a

temen

t

It i

s not

the c

ase t

ha

t

p

.”

The

pr

oposi

ti

on

p

is

r

ead “not

p

.”

The

tru

th

val

ue of th

e neg

a

ti

on

of

p

,

p

, i

s the

opposi

te

of

the

trut

h

val

ue of

p

.

3

2

Ex

amples

“M

y

PC runs Li

n

ux

“It

is not the

c

ase

tha

t

m

y

PC ru

ns Li

nu

x

My

P

C

does not run

Li

nu

x

ʹ

ʹ

Ͷ

It

is not the

case

tha

t

ʹ

ʹ

Ͷ

ʹ

ʹ

Ͷ

ݔ

͵

͵

?

?

?

3

3

Truth

Table

for t

he Ne

g

a

tio

n

Ot

her

Not

a

tio

n

for neg

a

tio

n:

̱

p

Wha

t i

s the neg

a

ti

on o

f “It

is

not

the c

ase

tha

t

ʹ

ʹ

Ͷ

3

4

The

Conjuncti

on

De

finitio

n

2

Le

t

p

and

q

be p

roposi

ti

ons.

The

conjun

cti

on

of

p

and

q

, denot

ed

b

y

p

ר

q

, i

s the

pr

oposi

ti

on

p

and

q

.”

The

conjuncti

on

p

ר

q

is

true when both

p

and

q

ar

e

tru

e and

is

f

al

se

otherwi

se.

3

5

Ex

amples

p

:

It

is r

ai

ni

ng

q

:

It

is wi

nd

y

݌

ר

ݍ

?

I am thi

rs

ty

I am hungry Conjuncti

on?

“R

ebec

ca’

s

PC

has

mor

e

than

16

GB

fr

ee

har

d

di

sk sp

ace,

and

the

pr

ocesso

r

in R

ebecc

a’

s

PC ru

ns

fas

ter

than

1 GHz.

It

is c

ol

d but

sunn

y.

3

(5)

Truth T

able

Can y

ou do

it

for thr

ee

pr

oposi

ti

ons?

How man

y poss

ibl

e

ans

w

er

s?

3

7

The Di

sjunctio

n

De

finitio

n

3

Le

t

p

and

q

be p

roposi

ti

ons.

The

di

sju

nct

ion

of

p

and

q

, denot

ed

b

y

p

ש

q

, i

s the pr

oposi

ti

on

p

or

q

.”

The

di

sjuncti

on

p

ש

q

is

fals

e

when both

p

and

q

ar

e

fals

e

and

is

true o

therwi

se.

3

8

Truth T

able

3

9

Inclusiv

e

vs. Ex

clusiv

e

“S

tuden

ts

who

ha

v

e

tak

en

c

al

cul

u

s

or

comput

er

sci

e

nce

c

an

tak

e

thi

s

cl

ass

.”

(Inclusiv

e or)

“St

ude

n

ts

who ha

v

e

tak

en

c

al

cul

us

or

comput

er

sci

ence

,

but

no

t bo

th,

c

an

en

rol

l

in

thi

s

cl

ass.

Stude

n

ts

who ha

ve

tak

en

either

c

al

cul

us

o

r

c

omput

er

sci

ence

,

can

en

rol

l

in thi

s

cl

ass.

(e

xclusiv

e or)

4

0

Ex

clusiv

e Di

sjunct

ion

De

finitio

n

4:

Le

t

p

and

q

be p

roposi

ti

ons.

The

e

xcl

u

si

ve

or

of

p

and

q

, denot

ed

b

y

p

ْ

q

,

is

the pr

oposi

ti

on

tha

t

is

true

when e

xactl

y

one

of

p

and

q

is

true

and

is

fals

e

otherwi

se

.

Ei

the

r

p

or

q

.

p

or

q

but

not both

.

4

1

Truth T

able

Ei

ther

p

or

q

.

p

or

q

but

not both.

4

2

“Inclusiv

e

or

or “Ex

clusiv

e

or

Toni

gh

t

I wi

ll

s

ta

y ho

me o

r

g

o

out t

o

a mo

vi

e

.”

?

?

?

Hu

man

languag

es

can

be

amb

iguous

So be

c

ar

e

ful

4

3

Conditio

nal

St

a

temen

ts/

Implic

a

tion

p:

Pr

emi

se,

Hypothes

is

,

an

teceden

t

q:

Concl

us

ion,

Consequ

ence

The s

ta

temen

t

p

q

is

true

when

both

p

a

n

d

q

ar

e

true

p

is f

alse

(no

ma

tt

er

wha

t

trut

h

val

ue

q

has

)

4

4

Cond

itio

nal

St

a

temen

ts

De

finitio

n

5:

Le

t

p

and

q

be p

roposi

ti

ons.

The

condi

ti

onal

st

at

emen

t

p

q

is

the pr

oposi

ti

on

“i

f

p

, then

q

.”

The c

ondi

ti

onal

s

ta

temen

t

p

q

is

fals

e

when

p

is

true

and

q

is

fals

e

,

and

true

otherwi

se.

In

the

c

ondi

ti

onal

s

ta

temen

t

p

q

,

p

is

c

al

led the

h

ypot

hesi

s

(or

an

tec

eden

t

or

premi

se

)

and

q

is

c

al

led

th

e

concl

u

si

on

(or

consequenc

e

). 4

(6)

The

st

a

teme

n

t

p

q

is c

al

led

a

condi

ti

onal

st

a

teme

n

t

be

cause

p

q

asserts

tha

t

q

is

true

on

the

condi

ti

on tha

t

p

hol

ds.

A

c

ondi

ti

onal

s

ta

teme

n

t

is

al

so

cal

led

an

im

pl

ic

a

tio

n

.

Other

Not

a

tio

ns

݌

֜

ݍ

݌

ـ

ݍ

4

6

Other f

orms

Condi

ti

onal

s

ta

temen

ts

pl

a

y

an

essen

tial

r

ole

in ma

the

ma

ti

cal

r

easoni

ng

Man

y w

a

ys t

o

e

xpr

ess

an i

mpl

ic

a

ti

on

(p

-> q)

: 47

Ex

ample

p:

you

g

e

t 1

00% on the

fi

nal

q:

you wi

ll

g

e

t

an

A

p imp

lies

tha

t

q.

you

g

e

t 1

00% on the

fi

nal

imp

li

es

tha

t

you wi

ll

g

e

t

an A

.

If p,

then

q.

If

y

ou

g

e

t

10

0%

on t

he

fi

nal

,

the

n

tha

t

you wi

ll

g

e

t

an

A

.

4

8

Ex

ample

Con

t.

If

p,

q

.

If

you g

e

t

10

0%

on t

he fi

n

al

,

tha

t

you wi

ll

g

e

t

an

A

.

p is

sufficien

t

for q.

Ge

t

10

0% on the

fi

nal

is

suf

fi

ci

en

t

for

g

e

tti

ng an

A

.

q only

if p

.

you wi

ll

g

e

t an

A

onl

y i

f

y

ou g

e

t

100

%

on th

e fi

nal

.

q u

nless

p

.

you wi

ll

g

e

t an

A

unl

es

s

y

ou

don’t

g

e

t 1

00% on fi

nal

.

4

9

Ex

ampl

es

If I f

al

l

in a

lak

e, then

I’l

l

g

e

t

w

e

t.

If gr

a

vi

ty

does not e

xi

st t

hen

I c

an fl

y.

If sun

ri

ses f

rom the w

es

t

then

it

’l

l

be

the

end

of our pl

ane

t.

If the moon i

s made

of chees

e,

then the earth

is

r

ect

angul

ar

.

5

0

Ex

ample

Con

t.

If

you manag

e t

o

g

e

t

a 100% on

the fi

n

al

,

then

you w

oul

d e

xpect t

o

recei

ve

an A

.

ܶ

ݍ

ݍ

(Fi

rs

t

2

cases i

n

the tru

th

tabl

e)

If

you do

not

g

e

t

100

%

you ma

y o

r m

a

y no

t

recei

ve

an A depen

di

ng

on o

ther

fact

or

s.

ܨ

՜

ݍ

ܶ

ሺƒ•–

ʹ

…ƒ•‡

•ሻ

Ho

w

e

ver

,

if

y

ou do

g

e

t 1

00

%,

but

the pr

of

essor

does not

g

iv

e y

ou an A

,

you wi

ll

f

eel

chea

ted.

If

you g

e

t

100

%

on th

e fi

nal

,

then

you wi

ll

g

e

t an

A

.

5

1

Ex

er

cise

Tr

ansl

a

te the pr

oposi

ti

ons

in

to

respect

iv

e

formu

lae

It i

s r

ai

ni

ng

and

wi

nd

y.

It i

s sunn

y

but

fr

ee

zi

n

g.

Gi

ve me t

ea or c

of

fee.

If th

er

e

ar

e D

DP

s

tud

en

ts

and

enr

ol

led

in

BS,

then

I

wi

ll

t

eac

h DS.

5

2

Truth t

ables

p

q

ר

ש

0

0

0

0

1

0

1

0

1

1

1

0

0

1

0

1

1

1

1

0

5

3

Ex

er

cise

Can

you c

ompl

e

te th

e f

ol

lo

wi

ng

tru

th

tabl

e

wi

thout

ask

ing me an

y

ques

ti

on

in cl

as

s?

5

4

p

q

r

ሺ࢖

ר

ࢗሻ

ש

p,

q

and r

ar

e pa

rame

ter

s

in thi

s

e

xer

ci

(7)

Do

e

xer

cises

fr

om the

cour

se

book

5

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