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Discrete Mathematics
Chapter 1.
The Foundations
Cheng-Chia Chen
Transparency No. 1-0
The Foundations
1. The foundations
Logic:
 The basis of all mathematical reasoning
 has practical applications to the design of
computing machine
 give the precise meaning of math. statements.
Sets:
 The basis of all mathematical structures.
 definitions, operations
Functions :
 definitions, classification, operations
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1.1 Logic
Proposition:
 A statement that is either true or false (,but not
both).
 Example:
1. Taipei is the capital of ROC. 2. Tokyo is the capital of Korea.
3. 1+1 = 2. 4. 2+2 = 3.
5. There is no integer x,y,z and n > 2 s.t.
xn + yn = zn ---- Fermat’s last theorem
6. “Every even number is the sum of two prime numbers.” ---Goldaach’s conjecture (1742)
==>
1. Statement (1)~(5) are propositions,
2. (1),(3) are true; (2),(4) are false.
3. Nobody knew (5) is true or false until 1994, but it must be
true or false, hence it is a proposition. (6) open problem.
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More Examples
Examples:
1. What time is it? (interrogative)
2. Read this carefully! (command)
3. x+1 = 2.
4. x+y = z.
==>
1. (1) and (2) are not statements
2. (3) and (4) are not propositions:
neither true nor false!
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Propositional variables and propositional constants
 In algebra, we use variables to denote an item with
unspecified value.
 e.g. 10 chickens and rabbits in a cage with 16 feet. ==>
#rabbits = ? and #chickens=?
 ==> let x = #rabbits, y = #chickens
 ==> x + y = 10 /\ 2x + 4y = 16.
 Likewise, In logic, we can use propositional variable
to denote an arbitrary proposition with unspecified
truth value.
 e.g., If it is raining, the ground is wet. since the ground is
wet, it is raining now.
 ==> Let p =def “It is raining”, q =def “the ground is wet”
 ==> ((p ->q) /\ q) -> p.
 Propositional constants: only two values:
 T(true), F(false).
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Forming Compound propositions from simpler ones
 Primitive propositions:


propositional variables
propositional constants
 P, Q: two known propositions
 Then the followings are also propositions:
1. “P and Q” : ( P /\ Q) conjunction of P and Q
2. “It is not the case that p”: (~P) negation of P
3. “P or Q” : (P \/ Q) disjunction of P and Q
4. “P implies Q” : ( P  Q) implication
 P : antecedent, premise, hypotheses;
 Q: consequence, conclusion
 equivalent phrase:
– “P entails Q”, “Q if P”, “P only if Q”
5. “p iff q” (p <->q) equivalence of P and Q
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Truth conditions for compound propositions
1.
2.
3.
4.
5.
P /\ Q is true iff both P and Q are true
P \/ Q is true iff either P or Q is true
~P is true iff P is not true (i.e., false)
P<->Q is true iff both are true or both are false.
PQ is true iff whenever P is true, Q is true
( i.e., it is not the case that P is true but Q is false )
 In the propositions, /\, \/, <-> and  are called binary
connectives(or operations) and ~ is called unary
connective(or operations).
 Other binary connectives are possible.
 What about the truth condition for primitive
propositions?
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From natural language statements to formal logic expressions
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Natural language statement
Today is Friday
I have a test today
It is not the case that today is
Friday
Today is not Friday
Today is Friday and
I have a test today
If today is Friday, then I have
a test today.
Today is Friday or I have a
test today.

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symbolized logic expr.
p
q
~p
 ~p
 (P ∧ q)
 (pq)
 (P ∨ q)
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Truth conditions for propositions
 Problem: when will you say that the sentence:
It_is_raining
is true ?
=> The proposition:” It_is_raining” is true if the meaning
(or fact) that the proposition is intended to represent
occurs(happens, exists) in the situation that the
sentence referred to.
=>Example: Since it is not raining now(the current situation), the
statement It_is_raining is false (in the current situation). But if
it were raining now, then I would say that It_is_raining is true.
 Factors affecting the truth value of a proposition:
 the situation in which the proposition is used.
 the meaning of the proposition.
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Truth table
•The meaning of logical connectives can be
represented by a truth table.
A
B
~A
A/\B
A\/B
A->B
A<->B
0
0
1
0
0
1
1
0
1
1
0
1
1
0
1
0
0
0
1
0
0
1
1
0
1
1
1
1
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Other usual connectives
 Besides /\,\/, and <->, there are also some other
connectives as you have known in digital system.
xor , nand, nor.
Rules: let I(x) denote the truth value of x.
 I(A  B)= I(~(A<->B))=1 iff not I(A)=I(B)
 I(A nand B) = I(~(A/\B)).
 I(A nor B) = I(~( A \/B)).
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1.2 Propositional Equivalence
Some definitions: Let A be any proposition,
then
1. A is a tautology(contradiction) iff A is true
(false) no matter what the truth values of the
prop. variables inside A are given.
2. If A is neither a tautology nor a
contradiction, then A is called a contingency.
Contingencies
Tautologies
(valid)
Contradictions
(unsatisfiable)
The geography of propositions
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Logical Equivalence
 A and B are logically equivalent (,A  B) if A <-> B is a
tautology
 Theorem1: Logical equivalence relation is reflexive,
symmetric and transitive.
I.e., for all propositions A,B and C,
1. A  A
2. A  B implies B A and
3. A  B and B  C imply A  C.
 Theorem2[substitution theorem]: If A  B and C[X] is
a proposition containing X as a subproposition, then
C[A] and C[B] are logically equivalent, where C[A] is
the result of C with X in C replaced by A.
 ex: (p∨q)  (q∨p), C[X] =def ~(p ∧ X)
 => ~(p∧ (p∨q) )~(p∧ (q∨p))
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The Foundations
Determine tautologies, contradictions and contingencies
 Problems: Given a proposition A, how can you determine
whether it is a tautology, a contradiction or a contingency?
=>Exhausted evaluation! (by using truth table)
e.g., A = (p->q) <-> (~p \/q) a tautology ?
1. there are two primitive props: p, q.
2. So there are 4 possible assignments of truth values to p
and q.
p=0,q=0: p=0,q=1:
p=1,q=0:
p=1,q=1:
3. Under each assignment, we can evaluate the truth value of
A (in bottom up way) by the truth condition rule of
connecting connectives.
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Determine tautologies
(pq)<->~p\/q :1
p->q:1
~p:1
p:0
~p \/q:1
q:0
The evaluation tree for (p->q)<-> (~p\/q)
p
q
~p p->q
~p\/q
p->q <-> ~p\/q
0
0
1
1
1
0
1
1
1
T
1
0
0
1
1
0
1
1
1
0
1
0
1
1
1
The truth table for pq <-> ~p\/q
A tautology
since all are 1’s
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Determine other properties
1. logical equivalence: A <=> B iff A<->B is a tautology.
So we can apply the procedure for determining
tautology to determine if A <=>B.
2. contradiction: A is a contradiction iff ~A is a
tautology
3. Satisfiability: A is satisfiable iff ~A is not a tautology.
4. contingence: A is a contingence iff neither A nor ~A
is a tautology.
5. logical consequence: B is a logical consequence of
A [denoted A |= B] iff AB is a tautology.
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
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Some important logical equivalences
P /\ T  P
P/\P  P
• Identity law
•Idempotent law
P \/ F  P
P\/P P
P \/ T 
• Domination law
P /\ F 
~(~P) 
• Double negation
P \/ Q 
•Commutative
P /\ Q 
P \/ (Q \/ R) 
•Associative
P /\ (Q /\ R) 
==> P /\ (Q /\ R) = (P/\Q/\R) 
P /\(Q \/ R) 
•Distribution law
P \/(Q /\ R) 
~(P /\Q) 
•DeMorgan’s law
~(P\/Q) 
==> ~(P1 /\ P1/\.../\Pn) 
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One Example:
Show that
~(p \/ (~p /\ q)  ~p /\ ~q
Proof:
1. By truth table (omitted)
2. ~(p \/ (~p /\ q)  deM
~p /\ ~(~p /\ q)  deM
~p /\ (~(~p) \/ ~q)  DbNeg
~p /\ (p \/ ~q)  Distr
(~p \/ ~q) /\ (~p \/ p)  ExMd
(~p \/ ~q) \/ T  identity
(~p \/ ~q)
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1.3 Predicates (述詞) and quantifiers(量詞)
 Problems of Propositional logic:
 Cannot analyze the content of primitive
propositions.
 cannot group similar propositions
 cannot analyze quantifiers.
Ex: Consider the following situations:
 1. Family = {a,b,c1,c2,c3 }
 2. We are interested in who is whose
brother.
 3. Suppose In Family, ci is cj’s brother if i 
j for i,j =1..3.
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Inadequancy of propositional logic
Primitive propositions needed:
 x_is_y’s_brother, x,y  Family
 So we need 25 propositions.
New approach: Parameterized
proposition(Predicate).
 Use a (predicate) symbol B to symbolize the
statement:
 B(x,y)  x is y ‘s brother.
 e.g.,
 B(a,c1) : a is c1’s brother,
 B(c2,c2) : c2 is c2’s brother,
 ...
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Benefits of Predicates
Advantage of using predicates:
==>
1. need only 6 (1 predicate and 5 names)
symbols.
2. can group related propositions together.
(B(x,y) and others, for instance,
q(x)  x_is_old. )
3.The most important : allow representation of
quantified statement like: c1_has_a_brother (or,
there is an object x which is c1’s brother, $ x B(x, c1))
to be inferred from
c2_is_c1’s brother ( B(c2,c1) )
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Predicates
In the expression:
B(x,y )
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
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B : predicate symbol (or proposition function)
x (y) : first (2nd) argument
B(x,y) : [atomic] proposition(or formula)
#arguments in B(X,Y): the arity of the predicate
symbol B.
Notes:
1. Each arity has a fixed arity.
2. B(x,y,c1) and B(x) are meaningless.
because used #arguments different from B’s arity.
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The Foundations
Predicates (cont’d)
3. The truth value of a proposition p(x1,...,xn)
depends on both p and x1,...,xn.
Ex1: Let p(x)  “x >3 “. then
 P(4)  “4 > 3” has truth value T,
 P(2)  “2 > 3” has truth value F.
Ex2: Let Q(x,y)  “x = y +3” then
 Q(1,2) = “1 = 2 + 3” is false.
 Q(3,0) = “3 = 0 + 3” is true.
 Mathematically, we can define (the meaning of) P as a
function [P]
 [P]: Un (n is P’s arity) --> {T,F}with the intention that
 “P(x1,...,xn)” holds (is true) iff [P]([x1],...,[xn]) = T.
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Set representations of truth-functions
 Ex:
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[B]: Family2 -->{T,F} s.t. [b](x,y) = T iff x =[ci], y=[cj], i  j.
p(x)  “x >3 “ , So [P]: N --> {T,F } s.t. [P](x) = T iff x > 3.
Q(x,y)  “x = y +3” , so
[Q] : N2 --> {T,F} with
[Q](x,y) = T iff x = y +3.
Note: P partition N into two sets, one with members
So, instead of represent
mapped to T and one mapped to F.
P(x) = T.
P as a truth function, we
equivalently represent P
{1,2,3}
{4,5,...}
as the set {x | P(x) = T }
= {4,5,6,...}
P(x) = F
Similarly, we can represent Q as {(x,y) | x = y + 3 }
N
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constant, function, variables and terms
 In the statement:
“y > min(x, 3)” --- (s1)
 x,y are called variables,
 3 is a constant,
 min is a function symbol with arity 2
 “min(3,2)” behaves more like x, 3 than “x >y”.
 So if let P(x,y)  “x > y”, then
 s1 can be represented as
P(y, min(x,3))
 we call any expression that can be put on the
argument position of an atomic proposition a term
 Obviously, constants and variables are terms;
moreover,if f is a function of arity n, and t1,...tn are n terms,
then so is f(t1,...,tn).
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Universal and existential quantifications
 A: any statement (or called formula)
 e.g., A = p(x,y) /\ q(x,f(3)),...
x: any variable,
Then
1. ("x A) is a new formula called the universal
quantification of A,
2. ("x A) means:
“A(x) is true for all values of x in the universe of
discourse.”
3. ($x A) is a new formula called the existential
quantification of A,
4. ($x A) means:
“A(x) is true for some value of x in the universe of
discourse.”
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Quantifiers and universe of discourse
Consider the sentence: p(x) = “x > 0”.
 1. as stated before, p(x) is true or false depending on
the value of x.
 2. p(1), p(2),... : true;
p(0) , p(-1),...: false
Universe of Discourse (U,論域):
 The set of objects (domain) from which all variables
are allowed to have values.
 Ex: consider the sentence:
s2: ”all numbers are greater than 0”.
(for-all x “x > 0” ) or (" x p(x) )
 U = N+={1,2,3,...} => s2 is true. U = Z={...,-1,0,1,...}=>
s2 is false.
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Translation of NL statement into logical one
 Translate the following sentences into logical
expressions:
1. Every body likes Chang
2. Everybody loves somebody.
3. There is somebody loved by every one.
4. Nobody likes everybody.
5.There is somebody Chang don’t like.
6. there is somebody no one likes.
7.there is exactly one person everybody likes.
8 Chang likes exactly two persons
9. Everyone likes himself.
10. There is someone who likes no one who does not
like himself.
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Terminology about formulas
Occurrences of variables in formulas:
 A = p(f(x), c) \/ q(x,y)
 A contains 2 variables, one of which occurs
twice.
Free and bound variables:
A: a formula
 fv(A) = the set of all free variables in A is defined as
follows:
 If A is atomic => fv(A) is the set of all variables
in A
 fv(~A) = fv(A)
 fv(A/\B)=fv(A\/B)=fv(A->B)=fv(A<->B)= fv(A) U
fv(B).
 fv( for-all x A) = fv($xA) = fv(A)\ {x}.
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 bd(A): the set of bound variables in A is defined as
follows;
 1. If A is atomic => bd(A) = {}
 2. bd(~A) = bd(A)
 3. bd(A/\B) = d(A\/B) = bd(AB)
=bd(A<->B) = bd(A) U bd(B).
 4. bd( ∀x A) =
bd(A) if x ∉ fv(A).
bd(A) U {x} if x ∈ fv(A).
 Example A = ($ x p(x,y)) \/ q(x)
=> fv(A) = {x,y}; bd(A) = {x}
x occurs twice in A, one occurs free and the other
occurs bound.
 Def: A is a sentence iff fv(A) = {}
 Note: A is a proposition if A is a sentence
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More logical notions [omitted!!]
 A  B [logical equivalence (in predicate logic)]
iff A <-> B is a tautology(or valid) for all possible values
of free variables in A and in B.
 Common equivalences:
1. all propositional equivalences
 A->B  ~A \/ B,
~~A  A, ~(A/\B)  ~A\/~B,...
2. ~ "x A <  $x ~A.
3. ~$xA  "x ~A.
4. "x (A /\ B)  "x A /\ "xB.
5. $x(A \/B)  $xA \/ $XB
If x is not free in B, and Q is " or $, then
6 Qx (A /\ B)  QxA /\ QxB
7. Qx(A\/B)  QxA \/ QxB
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1.4 Sets
 Basic structure upon which all other (discrete and
continuous ) structures are built.
 a set is a collection of objects.
 an object is anything of interest, maybe itself a set.
 Definition 1.
 A set is a collection of objects.
 The objects is a set are called the elements or
members of the set.
 If x is a member of a set S, we say S contains x.
 notation: x S vs x S
 Ex: In 1,2,3,4,5, the collection of 1,3 5 is a set.
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Set description
 How to describe a set:?
1. List all its member.
 the set of all positive odd integer >10 = ?
 The set all decimal digits = ?
 the set of all upper case English letters = ?
 The set of all nonnegative integers
=?
2. Set builder notation:
 P(x) : a property (or a statement or a proposition)
about objects. e.g., P(x) = “ x > 0 and x is odd”
 then {x | P(x) } is the set of objects satisfying
property P.
 P(3) is true => 3  {x | P(x)}
 P(2) is false => 2  {x | P(x)}
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Conventions
 N =def {x | x is a natural numbers } = { 0,1,2,3,...}
 N+ =def {1,2,3,...}
 Z =def {...,-3,-2,-1,0,1,2,3,...}
 R =def the set of real numbers.
Problem:
The same set may have many different descriptions.
 {x | 0 < x <10 /\ x is odd }
 {1,3,5,7,9}, {5,3,1,9,7}
 {9,7,1,3,5}.
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Set predicates
Definition 2.
 Two sets S1, S2 are equal iff they have the
same elements
 S1 = S2 iff "x (x S1 <-> x  S2)
 Ex: {1,3,5} = {1,5,3} = {1,1,3,3, 5}
 Graphical representation of sets:
 Venn Diagrams:
 rectangle: Universal set: U
 circles: sets
 points: particular elements
 point x inside circle S => x  S
 point x outside S => x S.
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Set predicates (cont’d)
 Null set ={} =  =def the collection of no objects.
Def 3’: [empty set] for-all x x .
Def 3. [subset]
 A  B iff all elements of A are elements of B.
 A  B  for-all x (x  A ⇒ x  B)).
Def 3’’: A  B =def A  B /\ A  B.
 Exercise : Show that:
 1. For all set A (
 2. (A  B /\ B  A)  (A = B)
 3. A 
 Diagram representation of the set inclusion
relationship.
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Size or cardinality of a set
Def. 4: | A | = the size(cardinality) of A = # of distinct
elements of A.
 Ex:
 |{1,3,3,5}| = ?
|{}| = ?
 | the set of binary digits } | = ?
 |N| = ? ; |Z| = ? ; | {2i | i ∈ N} = ?
 |R| = ?
Def. 5.
 A set A is finite iff |A| is a natural number ; o/w it is
infinite.
 Two sets are of the same size (cardinality) iff there
is a 1-1 & onto mapping between them.
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countability of sets
Exercise: Show that
 1. |N| = |Z | = | Q | = |{4,5,6,...}|
 2. |R| = | (-1, 1) | = |(0,1)|
 3. |(0,1)| is uncountable
Def.
 A set A is said to be denumerable iff |A| = |N|.
 A set is countable iff either |A| = n for some n
in N or |A| = |N|.
By exercise 1,2,3,
 R is not countable.
 Q and Z is countable.
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The power set
Def 6.
 If A is a set, then the collection of all subsets
of A is also a set, called the poser set of A and
is denoted as P(A) or 2A.
Ex:
 P({0,1,2}) = ?
 P({}) = ?
 |P({1,2,..., n})| = ?
Order of elements in a set are
indistinguishable. But some sometimes we
need to distinguish between (1,3,4) and
(3,4,1) --> ordered n-tuples
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The Foundations
Show that |A| ≠ |2A|
Pf: (1) The case that A is finite is trivial since |2A| =
2|A| > |A|
and there is no bijection b/t two finite sets with
different sizes.
(2) assume |A| = |2A|, i.e., there is a bijection f: A -> 2A.
Let D = {x ∈ A | x  f(x) }. ==>
1. D is a subset of A; Hence
2. $y ∈ A s.t. f(y) = D.
Problem: Is y  D ?
if yes (i.e., y  D) ==> y  f(y) = D, a contradiction
if no (i.e., y  D) ==> y  f(y) =D, a contradiction too.
So the assumption is false, i.e., there is no bijection
b/t A and 2A.
Transparency No. 1-40
The Foundations
The Halting Problem
 L : any of your favorite programming languages (C,
C++, Java, BASIC, etc. )
 Problem: write an L-program HALT(P,X), which takes
another L-program P(-) and string X as input, and
HALT(P,X) returns true if P(X) halts and
returns false if P(X) does not halt.
P
Pr(x:String) {
…..
}
“It’s a test …”
X
Program you
are asked to
design
HALT(P,X)
P(X) Halt ?
yes
true
no
false
Transparency No. 1-41
The Foundations
Halt(P,X) does not exist
 Motivations of the proof:
Problem1 : What about the truth value of the sentence:
L: L is false
Problem 2 : Let S = {X | X X}. Does S belong to S or
not ?
The analysis: S  S => S  S; S  S => S  S.
Conclusion:
 1. S is not a set!!
 2. If a language is too powerful, it may produce expressions
that is meaningless or can not be realized.
 Question: If HALT(P,X) can be programmed, will it
incur any absurd result like the case of S?
Ans: yes!!
Transparency No. 1-42
H(P) : Another program using HALT(-,-) as a
subroutine
The Foundations
H(P)
HALT(P,X)
P
yes
P
Pr(x:String) {
…..
}
Loop
P(P) Halt ?
no
true
false
END
P
Note: H(P) Halts iff HALT(P,P) returns false iff P(P) does not halt.
Transparency No. 1-43
The Foundations
H(P)
Problem:
Will H(H(P)) Halt?
HALT(P,X)
yes
P(X) Halt ?
Loop
true
H(H(P))
no false END
HALT(P,X)
yes
P(X) Halt ?
H(P)
no
Loop
true
END
false
The anomaly :
H(H(P) halts iff
H(H(P)) does not halt.
Transparency No. 1-44
The Foundations
Cartesian Products
Def. 7 [n-tuple]
 If a1,a2,...,an (n > 0) are n objects, then “(a1,a2,...,an)”
is a new object, called an (ordered) n-tuple [ with
ai as its ith element. ]
 Any ordered 2-tuple is called a pair.
 (a1,a2,...,am) = (b1,b2,...,bn) iff
(1) m = n and (2) for i = 1,..,n ai = bi.
Transparency No. 1-45
The Foundations
Cartesian product
Def. 8: [Cartesian product]
A x B =def {(a,b) | a ∈ A and b ∈ B }
A1 x A2 x ...x An =def {(a1,...,an) | ai ∈ Ai }.
Ex: A = {1,2}, B = {a,b,c} , C = {0,1}
1. A x B = ? ; 2. B x A = ?
3. A x {} = ? ;4. A x B x C = ?
problem: 1. when will A x B = B x A ?
2. |A x B | = ?
Transparency No. 1-46
The Foundations
1.5 Set operations
union, intersection,difference , complement,
Definition.
1. A B = {x | x ∈ A or x ∈ B }
2. A B = {x | x ∈ A and x ∈ B }
3. If A  B = {} => call A and B disjoint.
4. A - B = {x | x ∈ A but x ∉ B }
5. ~ A = U - A
Venn diagram representations
Ex: U = {1,..,10}, A = { 1,2,3,5,8}
B = {2,4,6,8,10}
=> A B , A B , A - B , ~ A = ?
Transparency No. 1-47
The Foundations
Set identities
Identity laws:
A??=A
Domination law: U ? ? = A; {} ?? = {}
Idempotent law: A ? A = A ;
complementation: ~~A = A
commutative :
Associative:
Distributive:
DeMoregan laws:
A?B=B?A
A ? (B ? C) = (A ? B ) ? C
A ? (B ? C) = ?
~(AUB) = ?; ~(A ⋂ B)=?
Transparency No. 1-48
The Foundations
Prove set equality
1. Show that ~(A  B) = ~A ~B by show that
 1. ~(A  B) ~A ~B
 2. ~A ~B ~(A  B)
pf: (By definition) Let x be any element in ~(A 
B) ...
2. show (1) by using set builder and logical
equivalence.
3. Show distributive law by using membership
table.
4. show ~(A (B C)) = (~C ~B) ~A by set
identities.
Note the similarity between logical equivalence and
set identities.
Transparency No. 1-49
The Foundations
Generalized set operations
Def. 6
 A1,A2,...An: n sets
 B = {A1,A2,...An }
.n {i=1,..n} Ai =def ?
.n {i=1,..n} Ai=def ?
quiz: if B = {} =>  = ?; 
 Venn diagram representation of  and 
.
Example:
{0,,4,6,8},{0,,,,4},C{0,,6,9}>
  = ?
 
Transparency No. 1-50
The Foundations
Set representation
(in computer)
 Unordered list or array
 union, intersection, ~: time consuming.
 Bit string:
 U = {a1,...,an} is the universal set.
 A: any subset of U
 A can be represented by the string:
s(A) = x1 x2 x3....xn where
xi = 1 if a1 ∈ A and 0 o/w.
 fast set operations
 suitable only if U is not large.
 constant size representation.
Transparency No. 1-51
The Foundations
1.6 Functions
Def. 1 [functions]
A, B: two sets
1. a function f from A to B is a set of pairs (x, y) ∈ AxB
s.t., for each x ∈ A there is at most one y ∈ B s.t. (x,y) ∈
f.
2. if (x,y) ∈ f, we write f(x) = y.
3. f :A B means f is a function from A to B.
Def. 2. If f:A  B then
1. A: the domain of f; 2. B: the codomain of f
if f(a)=b =>
3. b is the image of a 4. a is the preimage of b
5. range(f) = ?
6. preimage(f) = ?
7. f is total iff ?
Transparency No. 1-52
The Foundations
Types of functions
 Def 4.
f: A x B; S: a subset of A,
T: a subset of B
1. f(S) =def ?
2. f-1(T) =def ?
Def. [1-1, onto, injection, surjection, bijection]
f: A -> B.
 f is 1-1 (a injection) iff ?
 f is onto (surjective, a surjection) iff ?
 f is 1-1 & onto <=> f is bijective (a bijection, 1-1
correspondence)
Exercise:Show that if there are onto mappings from A
to B and from B to A, then |A|=|B|.
Transparency No. 1-53
The Foundations
Real valued functions
F:A B is a real valued function iff A and B
are subsets of R.
f1,f2: real valued functions
=> 1. f1+f2 (x) = ?
2. f1• f2 (x) = f1(x) x f2(x).
3. f is increasing iff ?
4. f is strictly increasing iff ?
Transparency No. 1-54
The Foundations
operations on functions
A, B, C: any sets ; f: A B; g: B  C, then
1. [identity function]
idA : A A s.t. idA(x) = x for all x in A.
2. [composition of g and f]
gf: A  C s.t. gf(x) = g(f(x)) for x in A.
3. [inverse] If f is a bijection, then
f-1: B  A s.t. f-1(y) = x iff f(x) = y for y in B.
Graphical representations
Transparency No. 1-55
The Foundations
Properties of operations on functions
f: A  B. g: B C; h: C D,
then
1. idA f = f = f idB
2. f(gh) = (fg) h --- assoc.
3. (fg)-1 = g-1 f-1
If f: A  A is a bijection then
4. f f-1 = f-1 f= idA .
Sequence:
finite A-sequence: a: [n] (or [1,n]) A
w A-sequence: a : N (or N+)  A.
B indexed A-sequence : f:BA.
Transparency No. 1-56
The Foundations
The growth of functions
 Summation rules
 S ai =def a1 + a2+...an+ (...)
 a + (a+d) + (a+2d)+... = ?
 a + ar +arr + ... = ?
The growth of functions:
If f is a positive valued function, then what do
the following terms mean?




O(f) means ? o(f) means ?
W(f) means ? w(f) means ?
Q(f) means ?
useful in the analysis of the efficiency of
computer algorithms.
Transparency No. 1-57
The Foundations
Problem, algorithm and Complexity
A problem is a general question:
 description of parameters [input]
 description of solution[output]
An algorithm is a step by step procedure to
solve a problem.
 a recipe
 a computer program
We want the most efficient algorithm
 fastest (mostly)
 most economical with memory (sometimes)
 expressed as a function of problem size
Transparency No. 1-58
The Foundations
Example: Traveling Salesman Problem
Parameters:
 Set of cities
 Inter-city distances
a
10
5
c
9
3
6
b
9
d
Transparency No. 1-59
The Foundations
Solution [output]
Solution:
 The shortest tour passing all cities
 Example: a,b,d,c,a has length 27
a
10
5
c
9
3
6
b
9
d
Transparency No. 1-60
The Foundations
Problem Size
What is appropriate measure of problem
size?
 m nodes?
 m(m+1)/2 distances?
Use an encoding of the problem
 alphabet of symbols: a,b,c,d,0-9, |.
 strings: abcd||10|5|9|6|9|3.
Measures
 Problem Size: length of encoding.
 Time Complexity: how long an algorithm takes,
as function of problem size.
Transparency No. 1-61
The Foundations
Time Complexity
What is tractable?
 A function f(n) is O(g(n)) whenever
 ∃ c > 0 ∃ n0 > 0 s.t. |f(n)| ≤ c ∙ |g(n)| for all n > n0.
A polynomial time algorithm is one whose time
complexity is O(p(n)) for some polynomial p(n).
An exponential time algorithm is one whose
time complexity cannot be bounded by a
polynomial
(e.g., nlog n ).
Transparency No. 1-62
The Foundations
Tractability
 Basic distinction:
 polynomial time = tractable
 exponential time = intractable
execution time in microseconds(μs)
10
20
30
40
50
60
n
.00001s .00002s .00003s .00004s .00005s
.00006s
n2
.0001s
.0004s
.0009s
.0016s
.0025s
.0036s
n3
n5
2n
.001s
.1s
.001s
.008s
3.2s
1.0s
.027s
24.3s
17.9s
.064s
1.7 m
12.7d
.125s
5.2 m
35.7 y
.216s
13.0 m
366c
3n
.059 s
58 m
6.5 y
3855 c
2∙108c
1.3∙10 13 c
Transparency No. 1-63
The Foundations
Effect of Speed-ups for different order of functions
 Wait for faster hardware!
 Consider maximum problem size you can solve in an
hour.
present 100 times faster
1000 times faster
n
N1
100 N1
1000 N1
n2
N2
10N2
31.6 N2
n3
N3
4.64 N3
10 N3
n5
N4
2.5 N4
3.98 N4
2n
N5
N5+6.64
N5+9.97
3n
N6
N6+4.19
N6+2.29
Transparency No. 1-64
The Foundations
Asymptotic notations
Q((g(n)) = {f(n) | $c2,c1 and n0 > 0 s.t. c2g(n) 
f(n)  c1 g(n) for all n  n0 }
O((g(n)) = {f(n) | $ c and n0 > 0 s.t. f(n)  cg(n)
for all n  n0 }
W((g(n)) = {f(n) | $c and n0 > 0 s.t. c g(n)  f(n)
for all n  n0 }
o((g(n)) = {f(n) | "arbitrary small c >0 $ n0 > 0
s.t. f(n) < c g(n) for all n  n0 }
w((g(n)) = {f(n) | "arbitrary large c >0 $ n0 > 0
s.t. c g(n) < f(n) for all n  n0 }
Transparency No. 1-65
The Foundations
Using the asymptotic notations
We use f(n) = D(g(n)) to mean f(n) D(g(n)),
where D = Q,O,o,W,or w.
analog:
 O (asymptotic upper bound): 
f(n) = O(g(n)) means like f(n)  g(n)
 o (asymptotic upper bound but not tight): <
f(n) = o(g(n)) means like f(n) < g(n)
 W (asymptotic lower bound): 
f(n) = W(g(n)) means like f(n)  g(n)
 w (asymptotic lower bound but not tight): >
f(n) = w(g(n)) means like f(n) > g(n)
 Q (asymptotic tight bound : =
f(n) = O(g(n)) means like f(n) = g(n)
Transparency No. 1-66
The Foundations
Exercises
Let f and g be positive real function. Show
that
1. f = O(g) iff g = W(f)
2. f =o(g) iff g = w(f)
3. f =o(g) iff f =O(g) but not f = Q(g)
4 f = w (g) iff f =W(g) but not f = Q(g)
5 f = o(g)
iff
limit n f/g = 0 iff
limit n g/f =  iff
g = w(f).
Transparency No. 1-67