Declarative sentences that are either true or false but not both
Primitive statements
There is really no way to break them down into anything simpler
New statements can be obtained from existing ones in two ways
Negation
We do not consider the negation of a primitive statement to be a primitive statement
The negation statement of p is ¬p
NOT
Compound statements, using the following logical connectives
Conjunction ∧
AND
Disjunction ∨
OR
p∨q means inclusive disjunction. True if one or the other of p,q is true or if both of the statements p,q are true
p⊻q maens exclusive disjunction. True if one or the other of p,q is true but not both of the statements p,q are true
Implication →
THEN
p→q
If p, then q
p is sufficient for q
p is a sufficient condition for q
q is necessary condition for p
q is a necessary condition for p
p only if q
The statement p is called the hypothesis of the implication; q is called the conclusion.
Biconditional ↔or⇔
IF AND ONLY IF
IFF
p↔q means p is necessary and sufficient for q.
The following is the graph of the truth table.
Tautology and Contradiction
Tautology
A compound statement is called a tautology if it is true for all truth value assignments for its component statement.
Contradiction
If a compound statement is false for all such assignments, then it is called a contradiction.
Logical Equivalence
Two statements s1,s2 are said to be logically equivalent, and we write s1⇔s2 , when the statement s1 is true (respectively, false) if and only if the statement s2 is true (respectively, false).
The Laws of Logic
For any primitive statements p,q,r, any tautology T0 , and any contradiction F0,
Law of Double Negation
¬¬p⇔p
DeMorgan’s Laws
¬(p∨q)⇔¬p∧¬q
¬(p∧q)⇔¬p∨¬q
Commutative Laws
p∨q⇔q∨p
p∧q⇔q∧p
Associative Laws
p∨(q∨r)⇔(p∨q)∨r
p∧(q∧r)⇔(p∧q)∧r
Distributive Laws
p∨(q∧r)⇔(p∨q)∧(p∨r)
p∧(q∨r)⇔(p∧q)∨(p∧r)
Idempotent Laws
p∨p⇔p
p∧p⇔p
Identity Laws
p∨F0⇔p
p∧T0⇔p
Inverse Laws
p∨¬p⇔T0
p∧¬p⇔F0
Domination Laws
p∨T0⇔T0
p∧F0⇔F0
Absorption Laws
p∨(p∧q)⇔p
p∧(p∨q)⇔p
Dual of Statement
Let’s say s is a statement. If s contains no logical connectives other than ∧ and ∨, then the dual of s, denoted sd, is the statement obtained from s by replacing each occorrence of ∧ with ∨, T0 with F0 and vice versa.
Here’s an example, if p is any primitive statement, then
pd≡p
(¬p)d≡¬p
p∨T0 and p∧F0 are duals of each other
Duality Principle
Let s and t be statements that contain no logical connectives other than ∧ and ∨. If s⇔t, then sd⇔td.
Substitution Rules
Suppose that the compound statement P is a tautology. If p is a primitive statement that appears in P and we replace each occurrence of p by the same statement q, then the resulting compound statement P1 is also a tautology.
Let P be a compound statement where p is an arbitrary statement that appears in P, and let q be a statement such that q⇔p. Suppose that in P we replace one or more occurence of p by q. Then this replacement yields the compound statement P1. Under these circumstances P1⇔P.
For example
PP1P2:¬(p∨q)⇔(¬p∧¬q)is a tautology:¬[(r∧s)∨q]⇔[¬(r∧s)∧¬q]is also a tautology:¬[(r∧s)∨(t→u)]↔[¬(r∧s)∧¬(t→u)]is also a tautology
Modus Ponens (Rule of Detachment)
Statement
If p is true, and p→q is true, then the conclusion q must also be true
Law of The Syllogism
Statement
If p then q
If q then r
2 of the aboves imply if p then r
Math Expression
[(p→q)∧(q→r)]→(p→r)
Modus Tollens
Statement
If p then q, then not q impies not p
Math Expression
[(p→q)∧¬q]→¬p
Rule of Conjunction
If p,q are true statement, then p∧q is also a true statement
Set Theory
Terms Definitions
Set
A well-defined collection of objects. Neither order nor repetition is relevant for a general set
Element
Objects inside the set is called elements. They are said to be members of the set
Universe
The set that contains all possible elements relevant to a particular discussion or problem
Subset
If every element of C is an element of D, then C is a subset of D, which will be denoted by C⊆D
Proper subset
If, in addition, D contains an element that is not in C, then C is called a proper subset of D. It will be denoted by C⊂D
Cardinality
Also called size, the number of elements in a set, denoted by ∣A∣
Equal
C & D are said to be equal when C⊆D and D⊆C
Subset Relations
Let A,B,C⊆U.
If A⊆B and B⊆C, then A⊆C.
If A⊂B and B⊆C, then A⊂C.
If A⊆B and B⊂C, then A⊂C.
If A⊂B and B⊂C, then A⊂C.
Null Set
The null set, or empty set, is the (unique) set containing no elements. It is denoted by ϕ or {}.
∣ϕ∣=0, but {0}=ϕ.
ϕ={ϕ}.
Theorem 3.2
For any universe U, let A⊆U. Then ϕ⊆A, and if A=ϕ, then ϕ⊂A.
Power Set
If A is a set from universe U, the power set of A, denoted P(A), is the collection (or set) of all subsets of A.
For any finite set A with ∣A∣=n≥0, we find that A has 2n subsets and that ∣P(A)∣=2n. For any 0≤k≤n, there are (kn) subsets of size k. Counting the subsets of A according to the number, k, of elements in a subset, we have the combinatorial identity (0n)+(1n)+(2n)+⋯+(nn)=∑k=0n(kn)=2n, for n≥0.
Gray Code
A systematic way to represent the subsets of a given nonempty set can be accomplished by using a coding scheme known as a Gray code.
Pascal’s Triangle
Set of Numbers
Z= the set of integers ={0,1,−1,2,−2,3,−3,…}
N= the set of nonnegative integers or natural numbers ={0,1,2,3,…}
Z+=the set of positive integers ={1,2,3,…}={x∈Z∣x>0}
Q= the set of rational numbers ={a/b∣a,b∈Z,b=0}
Q+=the set of positive rational numbers ={r∈Q∣r>0}
Q∗= the set of nonzero rational numbers
R= the set of real numbers
R+=the set of positive real numbers
R∗= the set of nonzero real numbers
C= the set of complex numbers ={x+yi∣x,y∈R,i2=−1}
C∗= the set of nonzero complex numbers
For each n∈Z+,Zn={0,1,2,…,n−1}
For real numbers a,b with a<b,[a,b]={x∈R∣a≤x≤b}, (a,b)={x∈R∣a<x<b},[a,b)={x∈R∣a≤x<b}, (a,b]={x∈R∣a<x≤b}. The first set is called a closed interval, the second set an open interval, and the other two sets half-open intervals.
Closed Binary Operations
The addition and multiplication of positive integers are said to be closed binary operations on Z+.
Two operands, hence the operation is called binary.
Since a+b∈Z+when a,b∈Z+, we say that the binary operation of addition (on Z+) is closed.
Binary Operations for Sets
For A,B⊆U, we define the following
Union
A∪B={x∣x∈A∨x∈B}
Intersection
A∩B={x∣x∈A∧x∈B}
Symmetric Difference (like XOR)
A△B={x∣(x∈A∨∈B)∧x∈/A∩B}={x∣x∈A∪B∧x∈/A∩B}
Disjoint
Let S,T⊆U. The sets S and T are called disjoint, or mutually disjoint, when S∩T=ϕ.
Theorem 3.3
If S,T⊆U, then S,T⊆U are disjoint if and only if S∪T=S△T.
Complement
For a set A⊆U, the complement of A, denoted U−A, or A, is given by {x∣x∈U∧x∈/A}.
For A,B⊆U, the (relative) complement of A in B, denoted B−A, is given by {x∣x∈B∧x∈/A}.
Subset, Union, Intersection, and Complement
Theorem 3.4
For any universe U and any sets A,B⊆U, the following statements are equivalent:
A⊆B
A∪B=B
A∩B=A
B⊆A
The Laws of Set Theory
For any sets A,B and C taken from a universe U.
Law of Double Complement
A=A
DeMorgan’s Laws
A∪B=A∩B
A∩B=A∪B
Commutative Laws
A∪B=B∪A
A∩B=B∩A
Associative Laws
A∪(B∪C)=(A∪B)∪C
A∩(B∩C)=(A∩B)∩C
Distributive Laws
A∪(B∩C)=(A∪B)∩(A∪C)
A∩(B∪C)=(A∩B)∪(A∩C)
Idempotent Laws
A∪A=A
A∩A=A
Identity Laws
A∪ϕ=A
A∩U=A
Inverse Laws
A∪A=U
A∩A=ϕ
Domination Laws
A∪U=U
A∩ϕ=ϕ
Absorption Laws
A∪(A∩B)=A
A∩(A∪B)=A
Dual
Let s be a (general) statement dealing with the equality of two set expressions. Each such expression may involve one or more occurrences of sets (such as A, A, B, B, etc.), one or more occurrences of ϕ and U, and only the set operation symbols ∩ and ∪. The dual of s, denoted sd , is obtained from s by replacing (1) each occurrence of ϕ and U (in s) by U and ϕ, respectively; and (2) each occurrence of ∩ and ∪ (in s) by ∪ and ∩, respectively.
Theorem 3.5
Let s denote a theorem dealing with the equality of two set expressions. Then sd , the dual of s, is also a theorem.
This result cannot be applied to particular situations but only to results (theorems) about sets in general
Index Set
Let I be a nonempty set and U a universe. For each i∈I let Ai⊆U. Then I is called an index set (or set of indices), and each i∈I is called an index. Under these conditions,
i∈I⋃Ai={x∣x∈Aifor at least one i∈I},
i∈I⋂Ai={x∣x∈Aifor every i∈I}.
For example, let I={3,4,5,6,7}, and for each i∈I let Ai={1,2,3,…,i}⊆U=Z+. Then ⋃i∈IAi=⋃i=37Ai={1,2,3,…,7}=A7, whereas ⋂i∈IAi={1,2,3}=A3.
Generalized DeMorgan’s Laws
Let I be an index set where for each i∈I, Ai⊆U. Then,
i∈I⋃Ai=i∈I⋂Ai
i∈I⋂Ai=i∈I⋃Ai
Definition for Probability
Under the assumption of equal likelihood, let I be the sample space for an experiment E. Any subset A of I, including the empty subset, is called an event. Each element of I determines an outcome, so if ∣I∣=n and a∈I,A⊆I, then Pr({a}) = The probability that {a} (or, a) occurs = I∣{a}∣=n1, and Pr(A) = The probability that A occurs = ∣I∣∣A∣=n∣A∣.
Cross Product
For sets A, B, the Cartesian product, or cross product, of A and B is denoted by A×B and equals {(a,b)∣a∈A,b∈B}.