
- 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
DFA Minimization
DFA Minimization using Myphill-Nerode Theorem
Algorithm
Input − DFA
Output − Minimized DFA
Step 1 − Draw a table for all pairs of states (Qi, Qj) not necessarily connected directly [All are unmarked initially]
Step 2 − Consider every state pair (Qi, Qj) in the DFA where Qi ∈ F and Qj ∉ F or vice versa and mark them. [Here F is the set of final states]
Step 3 − Repeat this step until we cannot mark anymore states −
If there is an unmarked pair (Qi, Qj), mark it if the pair {δ (Qi, A), δ (Qi, A)} is marked for some input alphabet.
Step 4 − Combine all the unmarked pair (Qi, Qj) and make them a single state in the reduced DFA.
Example
Let us use Algorithm 2 to minimize the DFA shown below.

Step 1 − We draw a table for all pair of states.
a | b | c | d | e | f | |
a | ||||||
b | ||||||
c | ||||||
d | ||||||
e | ||||||
f |
Step 2 − We mark the state pairs.
a | b | c | d | e | f | |
a | ||||||
b | ||||||
c | ✔ | ✔ | ||||
d | ✔ | ✔ | ||||
e | ✔ | ✔ | ||||
f | ✔ | ✔ | ✔ |
Step 3 − We will try to mark the state pairs, with green colored check mark, transitively. If we input 1 to state a and f, it will go to state c and f respectively. (c, f) is already marked, hence we will mark pair (a, f). Now, we input 1 to state b and f; it will go to state d and f respectively. (d, f) is already marked, hence we will mark pair (b, f).
a | b | c | d | e | f | |
a | ||||||
b | ||||||
c | ✔ | ✔ | ||||
d | ✔ | ✔ | ||||
e | ✔ | ✔ | ||||
f | ✔ | ✔ | ✔ | ✔ | ✔ |
After step 3, we have got state combinations {a, b} {c, d} {c, e} {d, e} that are unmarked.
We can recombine {c, d} {c, e} {d, e} into {c, d, e}
Hence we got two combined states as − {a, b} and {c, d, e}
So the final minimized DFA will contain three states {f}, {a, b} and {c, d, e}

DFA Minimization using Equivalence Theorem
If X and Y are two states in a DFA, we can combine these two states into {X, Y} if they are not distinguishable. Two states are distinguishable, if there is at least one string S, such that one of δ (X, S) and δ (Y, S) is accepting and another is not accepting. Hence, a DFA is minimal if and only if all the states are distinguishable.
Algorithm 3
Step 1 − All the states Q are divided in two partitions − final states and non-final states and are denoted by P0. All the states in a partition are 0th equivalent. Take a counter k and initialize it with 0.
Step 2 − Increment k by 1. For each partition in Pk, divide the states in Pk into two partitions if they are k-distinguishable. Two states within this partition X and Y are k-distinguishable if there is an input S such that δ(X, S) and δ(Y, S) are (k-1)-distinguishable.
Step 3 − If Pk ≠ Pk-1, repeat Step 2, otherwise go to Step 4.
Step 4 − Combine kth equivalent sets and make them the new states of the reduced DFA.
Example
Let us consider the following DFA −

q | δ(q,0) | δ(q,1) |
---|---|---|
a | b | c |
b | a | d |
c | e | f |
d | e | f |
e | e | f |
f | f | f |
Let us apply the above algorithm to the above DFA −
- P0 = {(c,d,e), (a,b,f)}
- P1 = {(c,d,e), (a,b),(f)}
- P2 = {(c,d,e), (a,b),(f)}
Hence, P1 = P2.
There are three states in the reduced DFA. The reduced DFA is as follows −

Q | δ(q,0) | δ(q,1) |
---|---|---|
(a, b) | (a, b) | (c,d,e) |
(c,d,e) | (c,d,e) | (f) |
(f) | (f) | (f) |