
- Automata Theory - Applications
- Automata Terminology
- Basics of String in Automata
- Set Theory for Automata
- Finite Sets and Infinite Sets
- Algebraic Operations on Sets
- Relations Sets in Automata Theory
- Graph and Tree in Automata Theory
- Transition Table in Automata
- What is Queue Automata?
- Compound Finite Automata
- Complementation Process in DFA
- Closure Properties in Automata
- Concatenation Process in DFA
- Language and Grammars
- Language and Grammar
- Grammars in Theory of Computation
- Language Generated by a Grammar
- Chomsky Classification of Grammars
- Context-Sensitive Languages
- Finite Automata
- What is Finite Automata?
- Finite Automata Types
- Applications of Finite Automata
- Limitations of Finite Automata
- Two-way Deterministic Finite Automata
- Deterministic Finite Automaton (DFA)
- Non-deterministic Finite Automaton (NFA)
- NDFA to DFA Conversion
- Equivalence of NFA and DFA
- Dead State in Finite Automata
- Minimization of DFA
- Automata Moore Machine
- Automata Mealy Machine
- Moore vs Mealy Machines
- Moore to Mealy Machine
- Mealy to Moore Machine
- Myhill–Nerode Theorem
- Mealy Machine for 1’s Complement
- Finite Automata Exercises
- Complement of DFA
- Regular Expressions
- Regular Expression in Automata
- Regular Expression Identities
- Applications of Regular Expression
- Regular Expressions vs Regular Grammar
- Kleene Closure in Automata
- Arden’s Theorem in Automata
- Convert Regular Expression to Finite Automata
- Conversion of Regular Expression to DFA
- Equivalence of Two Finite Automata
- Equivalence of Two Regular Expressions
- Convert Regular Expression to Regular Grammar
- Convert Regular Grammar to Finite Automata
- Pumping Lemma in Theory of Computation
- Pumping Lemma for Regular Grammar
- Pumping Lemma for Regular Expression
- Pumping Lemma for Regular Languages
- Applications of Pumping Lemma
- Closure Properties of Regular Set
- Closure Properties of Regular Language
- Decision Problems for Regular Languages
- Decision Problems for Automata and Grammars
- Conversion of Epsilon-NFA to DFA
- Regular Sets in Theory of Computation
- Context-Free Grammars
- Context-Free Grammars (CFG)
- Derivation Tree
- Parse Tree
- Ambiguity in Context-Free Grammar
- CFG vs Regular Grammar
- Applications of Context-Free Grammar
- Left Recursion and Left Factoring
- Closure Properties of Context Free Languages
- Simplifying Context Free Grammars
- Removal of Useless Symbols in CFG
- Removal Unit Production in CFG
- Removal of Null Productions in CFG
- Linear Grammar
- Chomsky Normal Form (CNF)
- Greibach Normal Form (GNF)
- Pumping Lemma for Context-Free Grammars
- Decision Problems of CFG
- Pushdown Automata
- Pushdown Automata (PDA)
- Pushdown Automata Acceptance
- Deterministic Pushdown Automata
- Non-deterministic Pushdown Automata
- Construction of PDA from CFG
- CFG Equivalent to PDA Conversion
- Pushdown Automata Graphical Notation
- Pushdown Automata and Parsing
- Two-stack Pushdown Automata
- Turing Machines
- Basics of Turing Machine (TM)
- Representation of Turing Machine
- Examples of Turing Machine
- Turing Machine Accepted Languages
- Variations of Turing Machine
- Multi-tape Turing Machine
- Multi-head Turing Machine
- Multitrack Turing Machine
- Non-Deterministic Turing Machine
- Semi-Infinite Tape Turing Machine
- K-dimensional Turing Machine
- Enumerator Turing Machine
- Universal Turing Machine
- Restricted Turing Machine
- Convert Regular Expression to Turing Machine
- Two-stack PDA and Turing Machine
- Turing Machine as Integer Function
- Post–Turing Machine
- Turing Machine for Addition
- Turing Machine for Copying Data
- Turing Machine as Comparator
- Turing Machine for Multiplication
- Turing Machine for Subtraction
- Modifications to Standard Turing Machine
- Linear-Bounded Automata (LBA)
- Church's Thesis for Turing Machine
- Recursively Enumerable Language
- Computability & Undecidability
- Turing Language Decidability
- Undecidable Languages
- Turing Machine and Grammar
- Kuroda Normal Form
- Converting Grammar to Kuroda Normal Form
- Decidability
- Undecidability
- Reducibility
- Halting Problem
- Turing Machine Halting Problem
- Rice's Theorem in Theory of Computation
- Post’s Correspondence Problem (PCP)
- Types of Functions
- Recursive Functions
- Injective Functions
- Surjective Function
- Bijective Function
- Partial Recursive Function
- Total Recursive Function
- Primitive Recursive Function
- μ Recursive Function
- Ackermann’s Function
- Russell’s Paradox
- Gödel Numbering
- Recursive Enumerations
- Kleene's Theorem
- Kleene's Recursion Theorem
- Advanced Concepts
- Matrix Grammars
- Probabilistic Finite Automata
- Cellular Automata
- Reduction of CFG
- Reduction Theorem
- Regular expression to ∈-NFA
- Quotient Operation
- Parikh’s Theorem
- Ladner’s Theorem
Relations and Sets in Automata Theory
In automata theory, we frequently use the set theory and its relations to map the models mathematically. In this chapter, we will understand them in detail. We will start from the basic relations of the domain and co-domain and learn different types of relations with notations.
At the end, we will see a quick reference table which will be useful to remember the most important relationship types.
Relations and Sets in Automata Theory
A relation in mathematics is a mapping between two sets of information, established by connecting elements of two or more non-empty sets.

A set of ordered pairs is a relation from set A into set B, where the ordered pairs are (1,1), (2,4), (3,9), and (4,16). The notation {1, 2, 3, 4} represents the domain and {1, 9, 16, 4} represents the range.
Types of Relations in Sets
Set us see some of the important relation types:
Empty Relation
An empty relation is a set where there is no relationship between any elements. For instance, R = {x, y} where |x – y| = 8 is an example of an empty relation. R = φ ⊂ A × A
Universal Relation
A universal relation is a set-wide relation where every element is related to each other. For example, R = {x, y} where |x – y| ≥ 0, where R = A × A. This type of relation is essential for understanding sets.
Identity Relation
An identity relation is a set where every element is related to itself, such as in a set A = {a, b, c}. It is represented by I = {(a, a), a ∈ A}.
Inverse Relation
Inverse relation refers to a set having elements that are inverse pairs of another set, such as R-1 = {(b, a): (a, b) ∈ R}, where R-1 = {(b, a): (a, b) ∈ R}.
Reflexive Relation
A reflexive relation is a set where every element maps to itself, as seen in the example of R = {(1, 1), (2, 2), (1, 2), (2, 1)}, where (a, a) ∈ R.
Symmetric Relation
A symmetric relation is a set where "a = b" is true, implying that "b = a" is also true. It is symmetric if (b, a) ∈ R is true when (a,b) ∈ R. An example is R = {(1, 2), (2, 1)} for a set A = {1, 2}. aRb ⇒ bRa, ∀ a, b ∈ A
Transitive Relation
The transitive relation (x, y, z) ∈ R implies (x, z) ∈ R, while aRb and bRc ⇒ aRc for all a, b, c ∈ A.
Equivalence Relation
An equivalence relation is a relation that is reflexive, symmetric, and transitive simultaneously.
For a quick reference, follow the table given below −
Type of Relation | Definition | Example |
---|---|---|
Relation | A relation R between sets S and T is a subset of S × T, and if an ordered pair (s, t) ∈ R, then an element in S is related to an element in T. | The relation R between elements s in S and t in T is defined as s is related to t if it is divided by s, and R = {(2, 12), (2, 16), (2, 18), (4, 12), (6, 12), (6, 18)}. |
Reflexive | A relation R is said to be reflexive on a non-empty set R if every element of A is related to itself by that relation R. | Let A = {B, C, D}, where B, C, and D are brothers. The relation brotherhood is reflexive on the set A as {B, C}, {B, D}, and {C, D} are all sets of brothers. |
Symmetric | A relation R is said to be symmetric, if for two elements 'a' and 'b' in X, if 'a' is related to 'b' then b is related to a. | Let A be a set of all students in a class. Let R be a relation called classmate. If a and b are two students belonging to the set, then a is a classmate of b and b is a classmate of a. |
Transitive | Let R be a relation defined on a set A; then the relation R is said to be transitive if for a, b, c ∈ A and if aRb, bRc holds good then aRc also holds good. | Let A = {8, 6, 4}, where R is a relation called greater than. If 8 > 6 and 6 > 4 hold good, then 8 > 4 also holds good. |
Equivalence relation | A relation R is called as an equivalence relation on 'A' if R is reflexive, symmetric, and transitive. | The set A = {a, b} is not an equivalence relation, as the (b, b) tuple is not present, but R = {(a, a), (a, b), (b, a), (b, b)} is. |
Closure | A set is closed (under an operation) if and only if the operation on two elements of the set produces another element of the set. If an element outside the set is produced, then the operation is not closed. | The set S = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10….} is a collection of integer numbers, and all elements of this set are included in it, indicating that integer numbers are closed under multiplication. |
Conclusion
In this chapter, we explained relations in set theory and their types. Most important types are symmetric, reflexive, and transitive. The equivalence sets are combinations of them. We covered all of them with notations and explained the concepts examples for a better understanding.