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Logic and Set Theory: The Building Blocks of Modern Mathematics

Logic and Set Theory

Introduction

Logic and Set Theory are fundamental branches of mathematics that provide the foundation for many areas in computer science, mathematics, and philosophy. Logic deals with the principles of valid reasoning, while Set Theory provides a framework for organizing and manipulating collections of objects.

Why Study Logic and Set Theory?
  • Foundation for mathematical reasoning and proofs
  • Essential for computer science (programming, algorithms, databases)
  • Improves critical thinking and analytical skills
  • Provides tools for solving complex problems in various fields

1. Propositional Logic

1.1 Propositions

A proposition is a declarative statement that is either true or false, but not both.

Examples of Propositions:
  • "Paris is the capital of France." (True)
  • "7 + 5 = 10" (False)
  • "All prime numbers are odd." (False, because 2 is prime but even)
Non-Examples (Not Propositions):
  • "How are you?" (Question, not a statement)
  • "Go to the store." (Command, not a statement)
  • "This statement is false." (Paradoxical, cannot be assigned a truth value)

We typically use letters like p, q, r to denote propositions.

1.2 Logical Connectives

Logical connectives allow us to combine simple propositions into compound propositions.

Connective Symbol Name Meaning
NOT ¬ Negation "It is not the case that"
AND Conjunction "Both... and..."
OR Disjunction "Either... or... (or both)"
IF...THEN Implication "If... then..."
IF AND ONLY IF Biconditional "... if and only if..."
Example:

Let p: "It is raining" and q: "The ground is wet"

  • ¬p: "It is not raining"
  • p ∧ q: "It is raining and the ground is wet"
  • p ∨ q: "It is raining or the ground is wet (or both)"
  • p → q: "If it is raining, then the ground is wet"
  • p ↔ q: "It is raining if and only if the ground is wet"

1.3 Truth Tables

Truth tables define the meaning of logical connectives by showing how the truth value of a compound proposition depends on the truth values of its components.

Negation (NOT): ¬p

p ¬p
T F
F T

The negation of a true statement is false, and the negation of a false statement is true.

Conjunction (AND): p ∧ q

p q p ∧ q
T T T
T F F
F T F
F F F

A conjunction is true only when both of its components are true.

Disjunction (OR): p ∨ q

p q p ∨ q
T T T
T F T
F T T
F F F

A disjunction is true when at least one of its components is true.

Implication (IF-THEN): p → q

p q p → q
T T T
T F F
F T T
F F T

An implication is false only when the antecedent (p) is true and the consequent (q) is false.

The implication p → q can be read as "if p, then q" or "p implies q". It is only false when p is true and q is false.

Note that "if p, then q" is equivalent to "not p or q" (¬p ∨ q).

Biconditional (IF AND ONLY IF): p ↔ q

p q p ↔ q
T T T
T F F
F T F
F F T

A biconditional is true when both components have the same truth value.

p ↔ q is equivalent to (p → q) ∧ (q → p)

Creating Truth Tables for Complex Expressions

Let's create a truth table for the expression (p → q) ∧ ¬p

p q p → q ¬p (p → q) ∧ ¬p
T T T F F
T F F F F
F T T T T
F F T T T

Method:

  1. List all possible combinations of truth values for the variables.
  2. Build the truth table column by column, working from the simplest components.
  3. Use the previously computed columns to determine the values for more complex expressions.

1.4 Logical Equivalence

Two logical expressions are equivalent if they have the same truth values for all possible combinations of their variables.

Example: p → q is logically equivalent to ¬p ∨ q
p q p → q ¬p ¬p ∨ q
T T T F T
T F F F F
F T T T T
F F T T T

Since the columns for p → q and ¬p ∨ q are identical, the expressions are logically equivalent.

Important Logical Equivalences

Law Equivalence
Double Negation ¬(¬p) ≡ p
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)
De Morgan's Laws ¬(p ∧ q) ≡ ¬p ∨ ¬q
¬(p ∨ q) ≡ ¬p ∧ ¬q
Implication Law p → q ≡ ¬p ∨ q
Contrapositive p → q ≡ ¬q → ¬p
Biconditional Law p ↔ q ≡ (p → q) ∧ (q → p)

Example: Simplify ¬(p ∧ (q ∨ ¬r)) using logical equivalences.

Solution:

  1. ¬(p ∧ (q ∨ ¬r))
  2. Apply De Morgan's Law: ¬p ∨ ¬(q ∨ ¬r)
  3. Apply De Morgan's Law again: ¬p ∨ (¬q ∧ ¬(¬r))
  4. Apply Double Negation: ¬p ∨ (¬q ∧ r)
  5. The simplified expression is: ¬p ∨ (¬q ∧ r)

1.5 Arguments and Validity

An argument consists of a set of premises and a conclusion. An argument is valid if the conclusion logically follows from the premises.

Example of Valid Argument:

Premises:

  • If it rains, then the ground gets wet. (p → q)
  • It is raining. (p)

Conclusion: The ground is wet. (q)

This is a valid argument form known as Modus Ponens.

Common Valid Argument Forms

Name Form
Modus Ponens p → q, p ⊢ q
Modus Tollens p → q, ¬q ⊢ ¬p
Hypothetical Syllogism p → q, q → r ⊢ p → r
Disjunctive Syllogism p ∨ q, ¬p ⊢ q
Addition p ⊢ p ∨ q
Simplification p ∧ q ⊢ p
Conjunction p, q ⊢ p ∧ q
Resolution p ∨ q, ¬p ∨ r ⊢ q ∨ r

The symbol ⊢ means "therefore" or "it can be derived that."

Example: Determine if the following argument is valid:

Premises:

  • If I study hard, I will pass the exam. (p → q)
  • I did not pass the exam. (¬q)

Conclusion: I did not study hard. (¬p)

Analysis:

Let's check if the conclusion logically follows from the premises:

  1. p → q (Premise 1)
  2. ¬q (Premise 2)
  3. We want to determine if ¬p follows logically

This is an example of Modus Tollens (p → q, ¬q ⊢ ¬p), which is a valid argument form.

Therefore, the argument is valid.

2. Set Theory

2.1 Set Basics

A set is a well-defined collection of distinct objects, called elements or members of the set.

Set Notation

  • Roster Notation: Listing all elements inside curly braces: A = {1, 2, 3, 4, 5}
  • Set-Builder Notation: Specifying a property that defines the set: B = {x | x is an even integer and 0 ≤ x ≤ 10}
  • Element Notation: a ∈ A means "a is an element of set A"
  • Not an Element: a ∉ A means "a is not an element of set A"

Special Sets

  • Empty Set (∅ or {}): The set containing no elements
  • Universal Set (U): The set containing all elements under consideration
  • Natural Numbers (ℕ): {1, 2, 3, ...}
  • Integers (ℤ): {..., -2, -1, 0, 1, 2, ...}
  • Rational Numbers (ℚ): Numbers expressible as fractions
  • Real Numbers (ℝ): All numbers on the number line

Set Properties

  • Cardinality: The number of elements in a set, denoted |A|
  • Subset: A ⊆ B means every element of A is also in B
  • Proper Subset: A ⊂ B means A ⊆ B and A ≠ B
  • Equal Sets: A = B means A ⊆ B and B ⊆ A
  • Power Set: P(A) is the set of all subsets of A
Example:

Let A = {1, 2, 3}

  • |A| = 3 (A has 3 elements)
  • {1, 2} ⊂ A (proper subset)
  • A ⊆ A (subset, but not proper)
  • P(A) = {∅, {1}, {2}, {3}, {1, 2}, {1, 3}, {2, 3}, {1, 2, 3}}
  • |P(A)| = 2³ = 8

2.2 Set Operations

Set operations allow us to combine sets to create new sets.

Operation Symbol Definition
Union A ∪ B {x | x ∈ A or x ∈ B}
Intersection A ∩ B {x | x ∈ A and x ∈ B}
Difference A - B {x | x ∈ A and x ∉ B}
Complement A' {x ∈ U | x ∉ A}
Symmetric Difference A △ B (A - B) ∪ (B - A)
Cartesian Product A × B {(a, b) | a ∈ A and b ∈ B}
Example:

Let A = {1, 2, 3, 4}, B = {3, 4, 5, 6}, and U = {1, 2, 3, 4, 5, 6, 7, 8, 9}

  • A ∪ B = {1, 2, 3, 4, 5, 6}
  • A ∩ B = {3, 4}
  • A - B = {1, 2}
  • B - A = {5, 6}
  • A' = {5, 6, 7, 8, 9}
  • A △ B = {1, 2, 5, 6}

For A = {a, b} and B = {1, 2, 3}:

A × B = {(a, 1), (a, 2), (a, 3), (b, 1), (b, 2), (b, 3)}

B × A = {(1, a), (1, b), (2, a), (2, b), (3, a), (3, b)}

Note that A × B ≠ B × A unless A = B.

|A × B| = |A| × |B| = 2 × 3 = 6

2.3 Set Identities

Set identities are statements that assert the equality of two sets or set expressions.

Law Identity
Identity Laws A ∪ ∅ = A
A ∩ U = A
Domination Laws A ∪ U = U
A ∩ ∅ = ∅
Idempotent Laws A ∪ A = A
A ∩ A = A
Complementation Laws A ∪ A' = U
A ∩ A' = ∅
(A')' = A
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)
De Morgan's Laws (A ∪ B)' = A' ∩ B'
(A ∩ B)' = A' ∪ B'
Absorption Laws A ∪ (A ∩ B) = A
A ∩ (A ∪ B) = A

Example: Prove that A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C)

We need to show that every element in the left-hand side is also in the right-hand side, and vice versa.

Part 1: Show x ∈ A ∪ (B ∩ C) → x ∈ (A ∪ B) ∩ (A ∪ C)

If x ∈ A ∪ (B ∩ C), then either x ∈ A or x ∈ B ∩ C.

  • If x ∈ A, then x ∈ A ∪ B and x ∈ A ∪ C, so x ∈ (A ∪ B) ∩ (A ∪ C).
  • If x ∈ B ∩ C, then x ∈ B and x ∈ C, which means x ∈ A ∪ B and x ∈ A ∪ C, so x ∈ (A ∪ B) ∩ (A ∪ C).

Part 2: Show x ∈ (A ∪ B) ∩ (A ∪ C) → x ∈ A ∪ (B ∩ C)

If x ∈ (A ∪ B) ∩ (A ∪ C), then x ∈ A ∪ B and x ∈ A ∪ C.

There are three cases to consider:

  • If x ∈ A, then x ∈ A ∪ (B ∩ C).
  • If x ∉ A but x ∈ B and x ∈ C, then x ∈ B ∩ C, so x ∈ A ∪ (B ∩ C).
  • If x ∉ A and either x ∉ B or x ∉ C, then x cannot be in both A ∪ B and A ∪ C, which contradicts our assumption.

Therefore, A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C).

2.4 Venn Diagrams

Venn diagrams visually represent sets and set operations using overlapping circles within a rectangle (representing the universal set).

A
B

A basic Venn diagram with two sets A and B. The universal set U is represented by the rectangle surrounding the circles.

A
B

The union A ∪ B consists of all elements in either A or B (or both). It's represented by the entire shaded area.

A
B

The intersection A ∩ B consists of all elements that are in both A and B. It's represented by the overlapping region (purple).

A
B

The difference A - B consists of elements in A that are not in B. It's represented by the blue region (A) minus the overlapping region.

A

The complement A' consists of all elements in the universal set U that are not in A. It's represented by the green area outside the circle A.

Three-Set Venn Diagrams

For three sets, the Venn diagram becomes more complex with 8 distinct regions:

Three-set Venn diagram
  1. Elements in A only
  2. Elements in B only
  3. Elements in C only
  4. Elements in A and B, but not C
  5. Elements in A and C, but not B
  6. Elements in B and C, but not A
  7. Elements in A, B, and C
  8. Elements in none of A, B, or C (but in U)

This allows us to visualize complex set operations like (A ∩ B) ∪ (B ∩ C) ∪ (A ∩ C).

2.5 Cartesian Product

The Cartesian product of two sets A and B, denoted A × B, is the set of all ordered pairs (a, b) where a ∈ A and b ∈ B.

Example:

Let A = {1, 2} and B = {a, b, c}

A × B = {(1, a), (1, b), (1, c), (2, a), (2, b), (2, c)}

B × A = {(a, 1), (a, 2), (b, 1), (b, 2), (c, 1), (c, 2)}

Properties of Cartesian Product

  • |A × B| = |A| · |B|
  • A × B ≠ B × A (unless A = B or either A or B is empty)
  • A × (B ∪ C) = (A × B) ∪ (A × C)
  • A × (B ∩ C) = (A × B) ∩ (A × C)
  • If A = ∅ or B = ∅, then A × B = ∅

3. Relations and Functions

3.1 Relations

A relation R from set A to set B is a subset of the Cartesian product A × B. It consists of ordered pairs (a, b) where a ∈ A and b ∈ B.

Example:

Let A = {1, 2, 3} and B = {a, b, c}

A possible relation R from A to B could be:

R = {(1, a), (1, c), (2, b), (3, c)}

This relation associates 1 with both a and c, 2 with b, and 3 with c.

Relations on a Set

A relation on a set A is a relation from A to itself, i.e., a subset of A × A.

Properties of Relations

A relation R on a set A can have the following properties:

Property Definition
Reflexive (a, a) ∈ R for all a ∈ A
Symmetric If (a, b) ∈ R, then (b, a) ∈ R
Antisymmetric If (a, b) ∈ R and (b, a) ∈ R, then a = b
Transitive If (a, b) ∈ R and (b, c) ∈ R, then (a, c) ∈ R

Example: Let's examine the "less than or equal to" relation (≤) on the set of integers:

  • Reflexive? Yes, because a ≤ a for all integers a.
  • Symmetric? No, because 3 ≤ 5 but 5 ≰ 3.
  • Antisymmetric? Yes, because if a ≤ b and b ≤ a, then a = b.
  • Transitive? Yes, because if a ≤ b and b ≤ c, then a ≤ c.

Equivalence Relations

A relation that is reflexive, symmetric, and transitive is called an equivalence relation.

Equivalence relations partition a set into disjoint subsets called equivalence classes.

Example of Equivalence Relation:

The "congruence modulo n" relation on integers: a ≡ b (mod n) if a and b have the same remainder when divided by n.

For example, with mod 3, the equivalence classes are:

  • [0] = {..., -3, 0, 3, 6, ...}
  • [1] = {..., -2, 1, 4, 7, ...}
  • [2] = {..., -1, 2, 5, 8, ...}

Partial Order Relations

A relation that is reflexive, antisymmetric, and transitive is called a partial order relation.

Examples of Partial Order Relations:
  • ≤ on real numbers
  • ⊆ on sets (subset relation)
  • | on integers (divisibility relation: a | b if a divides b)

3.2 Functions

A function f from set A to set B, denoted f: A → B, is a relation from A to B where each element in A is related to exactly one element in B.

Function Terminology

  • Domain: The set A of input values
  • Codomain: The set B of possible output values
  • Range: The set of actual output values {f(a) | a ∈ A}
  • Image: For a ∈ A, f(a) is the image of a under f
  • Preimage: For b ∈ B, the set {a ∈ A | f(a) = b} is the preimage of b

Types of Functions

Type Definition
Injective (One-to-One) ∀a₁, a₂ ∈ A, if a₁ ≠ a₂, then f(a₁) ≠ f(a₂)
Surjective (Onto) ∀b ∈ B, ∃a ∈ A such that f(a) = b
Bijective Both injective and surjective
Examples:

Let A = {1, 2, 3} and B = {a, b, c, d}

  • f₁ = {(1, a), (2, b), (3, c)} is injective but not surjective (d is not in the range)
  • f₂ = {(1, a), (2, a), (3, b)} is not injective (1 and 2 map to the same element) and not surjective
  • If B = {a, b, c}, then f₃ = {(1, a), (2, b), (3, c)} is bijective

Function Composition

Given functions f: A → B and g: B → C, the composition of g and f, denoted g ∘ f: A → C, is defined as:

(g ∘ f)(a) = g(f(a)) for all a ∈ A

Example:

Let f(x) = x² and g(x) = x + 3

(g ∘ f)(x) = g(f(x)) = g(x²) = x² + 3

(f ∘ g)(x) = f(g(x)) = f(x + 3) = (x + 3)²

Note that function composition is not commutative: g ∘ f ≠ f ∘ g in general.

Inverse Functions

If f: A → B is a bijective function, then there exists an inverse function f⁻¹: B → A such that:

  • f⁻¹(f(a)) = a for all a ∈ A
  • f(f⁻¹(b)) = b for all b ∈ B

Example:

Let f(x) = 2x + 3

To find f⁻¹, solve for x:

y = 2x + 3

x = (y - 3)/2

Therefore, f⁻¹(y) = (y - 3)/2

We can verify that:

f⁻¹(f(x)) = f⁻¹(2x + 3) = ((2x + 3) - 3)/2 = x

f(f⁻¹(y)) = f((y - 3)/2) = 2((y - 3)/2) + 3 = y - 3 + 3 = y

4. Quizzes and Practice Problems

Propositional Logic Quiz

1. What is the negation of the statement "If it rains, then the ground gets wet"?

The given statement is p → q, where p is "it rains" and q is "the ground gets wet".

The negation of p → q is ¬(p → q).

Using the equivalence p → q ≡ ¬p ∨ q, we get:

¬(p → q) ≡ ¬(¬p ∨ q) ≡ ¬¬p ∧ ¬q ≡ p ∧ ¬q

Therefore, the negation is "It rains and the ground doesn't get wet."

2. Which of the following is logically equivalent to p → (q → r)?

Let's simplify p → (q → r):

  1. p → (q → r)
  2. p → (¬q ∨ r) [Using the equivalence q → r ≡ ¬q ∨ r]
  3. ¬p ∨ (¬q ∨ r) [Using the equivalence p → s ≡ ¬p ∨ s]
  4. (¬p ∨ ¬q) ∨ r [Associative law of disjunction]
  5. ¬(p ∧ q) ∨ r [Using De Morgan's law]
  6. (p ∧ q) → r [Using the equivalence ¬s ∨ t ≡ s → t]

Therefore, p → (q → r) is logically equivalent to (p ∧ q) → r.

3. Determine the validity of the following argument:
If I study, I will pass the exam.
If I pass the exam, I will graduate.
I did not graduate.
Therefore, I did not study.

Let's symbolize the statements:

  • p: I study
  • q: I pass the exam
  • r: I graduate

The premises are:

  1. p → q (If I study, I will pass the exam)
  2. q → r (If I pass the exam, I will graduate)
  3. ¬r (I did not graduate)

The conclusion is: ¬p (I did not study)

Let's check if the conclusion follows from the premises:

  1. From premises 1 and 2, we can derive p → r using the hypothetical syllogism.
  2. From p → r and ¬r, we can derive ¬p using modus tollens.

Therefore, the argument is valid.

Set Theory Quiz

1. If A = {1, 2, 3, 4}, B = {3, 4, 5, 6}, and C = {2, 4, 6, 8}, what is (A ∪ B) ∩ C?

Step 1: Find A ∪ B

A ∪ B = {1, 2, 3, 4} ∪ {3, 4, 5, 6} = {1, 2, 3, 4, 5, 6}

Step 2: Find (A ∪ B) ∩ C

(A ∪ B) ∩ C = {1, 2, 3, 4, 5, 6} ∩ {2, 4, 6, 8} = {2, 4, 6}

Therefore, (A ∪ B) ∩ C = {2, 4, 6}

Wait, I made a mistake. Let me verify again:

A = {1, 2, 3, 4}

B = {3, 4, 5, 6}

C = {2, 4, 6, 8}

A ∪ B = {1, 2, 3, 4, 5, 6}

(A ∪ B) ∩ C = {1, 2, 3, 4, 5, 6} ∩ {2, 4, 6, 8} = {2, 4, 6}

The correct answer is {2, 4, 6}.

2. Let U = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} be the universal set, and A = {1, 3, 5, 7, 9}, B = {2, 4, 6, 8, 10}. What is (A ∩ B')'?

Step 1: Find B'

B' = {1, 3, 5, 7, 9} (all elements in U that are not in B)

Step 2: Find A ∩ B'

A ∩ B' = {1, 3, 5, 7, 9} ∩ {1, 3, 5, 7, 9} = {1, 3, 5, 7, 9}

Step 3: Find (A ∩ B')'

(A ∩ B')' = {1, 3, 5, 7, 9}' = {2, 4, 6, 8, 10}

Therefore, (A ∩ B')' = {2, 4, 6, 8, 10}

Alternatively, we can use set identities:

(A ∩ B')' = A' ∪ (B')' = A' ∪ B [Using De Morgan's law and double negation]

A' = {2, 4, 6, 8, 10}

A' ∪ B = {2, 4, 6, 8, 10} ∪ {2, 4, 6, 8, 10} = {2, 4, 6, 8, 10}

3. If A = {a, b, c}, how many distinct subsets does A have?

For a set with n elements, the number of distinct subsets is 2^n.

Since A has 3 elements, it has 2^3 = 8 distinct subsets:

  1. ∅ (the empty set)
  2. {a}
  3. {b}
  4. {c}
  5. {a, b}
  6. {a, c}
  7. {b, c}
  8. {a, b, c}

Therefore, A has 8 distinct subsets.

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