📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON (MCA547) UNIT -1 Object Oriented Programming & Python Basics (university of allahabad)

 

🔴 UNIT 1 Object Oriented Programming & Python Basics

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1️⃣ Introduction to Object-Oriented Programming (OOP)

✅ What is OOP?

Object-Oriented Programming is a programming approach that organizes software using objects and classes instead of functions and logic.

✅ Objective of OOP

• Code reusability

• Data security

• Easy maintenance

• Real-world modeling

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2️⃣ Basic Concepts of OOP

🔹 1. Object

An object is a real-world entity that has:

• Properties (data)

• Behavior (methods)

📌 Example:

car = Car()

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🔹 2. Class

A class is a blueprint for creating objects.

📌 Example:

class Car:
    def start(self):
        print("Car started")

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🔹 3. Encapsulation

Wrapping data and methods together.

✔ Data hiding
✔ Security

Example:

class Student:
    def __init__(self):
        self.__marks = 90

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🔹 4. Abstraction

Showing only essential details and hiding internal implementation.

✔ Achieved using abstract classes

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🔹 5. Inheritance

One class acquires properties of another.

class Child(Parent):
    pass

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🔹 6. Polymorphism

Same function behaves differently.

Example:

print(len("Hello"))
print(len([1,2,3]))

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3️⃣ Benefits of OOP

✔ Code reusability
✔ Modularity
✔ Easy debugging
✔ Security
✔ Real-world modeling

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4️⃣ Applications of OOP

• Software development

• Web applications

• Game development

• AI & ML

• Mobile apps

• GUI applications

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5️⃣ Algorithms and Programming

✅ Algorithm

A finite set of steps to solve a problem.

Characteristics:

• Finite

• Unambiguous

• Effective

• Input & Output defined

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6️⃣ Introduction to Python

✅ Features of Python

• Easy syntax

• Interpreted language

• Platform independent

• Object-oriented

• Large standard library

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7️⃣ Structure of Python Program

# Comment
print("Hello World")

Components:

1. Comments

2. Statements

3. Indentation

4. Functions

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8️⃣ Variables in Python

Definition:

A variable is a container to store data.

x = 10
name = "Python"

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9️⃣ Data Types in Python

Built-in Data Types

Type	Example
int	10
float	10.5
str	"Hello"
bool	True
list	[1,2,3]
tuple	(1,2)
set	{1,2}
dict	{"a":1}

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🔟 Operators in Python

Types of Operators:

1. Arithmetic (+, -, *, /)

2. Relational (>, <, ==)

3. Logical (and, or, not)

4. Assignment (=, +=)

5. Bitwise (&, |)

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1️⃣1️⃣ Control Flow in Python

🔹 Conditional Statements

if a > b:
    print("A is greater")
else:
    print("B is greater")

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🔹 Looping Statements

For Loop

for i in range(5):
    print(i)

While Loop

i = 0
while i < 5:
    print(i)
    i += 1

📘 PAPER 1: THEORY OF COMPUTATION (MCA546) UNIT 5: TURING MACHINES & UNDECIDABILITY (university of allahabad )

 

🔴 UNIT 5: TURING MACHINES & UNDECIDABILITY

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1️⃣ Turing Machine (TM)

✅ Definition

A Turing Machine is the most powerful abstract machine used to model computation.

It can solve any problem that is algorithmically computable.

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✅ Components of Turing Machine

A Turing Machine is a 7-tuple:

M = (Q, Σ, Γ, δ, q₀, B, F)

Where:

• Q → Finite set of states

• Σ → Input alphabet

• Γ → Tape alphabet

• δ → Transition function

• q₀ → Initial state

• B → Blank symbol

• F → Final (accepting) states

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✅ Structure of TM

A Turing Machine has:

• Infinite tape

• Read/Write head

• Finite control

• Move Left / Right / Stay

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2️⃣ Instantaneous Description (ID)

✅ Definition

An Instantaneous Description (ID) represents the current state of the Turing Machine.

Format:

$$α q β$$

Where:

• α → Tape content to left of head

• q → Current state

• β → Tape content under and right of head

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✅ Example

101q011

Means:

• Head is reading 0

• Current state = q

• Tape contains 101011

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3️⃣ Language Accepted by Turing Machine

A language L is said to be accepted by a TM if:

• TM halts in accepting state for every string in L

• TM may loop for strings not in L

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4️⃣ Turing Computability of Functions

A function is Turing computable if:

• There exists a TM that computes it

• TM halts for all valid inputs

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Important Fact

All recursive functions are Turing computable

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5️⃣ Equivalence of Turing Machine and Recursive Functions

Theorem:

A function is Turing computable if and only if it is partial recursive.

This proves:

• Turing Machine

• Recursive Functions

• Lambda Calculus

➡ All are equivalent models of computation

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6️⃣ Recursively Enumerable (RE) Languages

✅ Definition

A language L is recursively enumerable if:

• There exists a Turing Machine that accepts L

• TM may not halt for strings not in L

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✅ Types of Languages

Language Type	Description
Recursive	TM halts for all inputs
Recursively Enumerable	TM may loop
Non-RE	Not accepted by any TM

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7️⃣ Recursively Decidable Languages

✅ Definition

A language is recursive (decidable) if:

• TM halts for all inputs

• Gives YES or NO answer

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Relationship

Recursive ⊂ Recursively Enumerable

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8️⃣ Undecidability

✅ Definition

A problem is undecidable if:

• No algorithm exists to solve it for all inputs

• TM cannot decide it

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9️⃣ Undecidability of Type-0 Grammar

Type-0 grammars generate:

• Recursively enumerable languages

Problem:

Whether a given string belongs to a Type-0 grammar?

Result:

❌ Undecidable

Because:

• Equivalent to Halting Problem

• No general algorithm exists

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🔟 Church–Turing Thesis

✅ Statement

Any function that can be computed by an algorithm can be computed by a Turing Machine.

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⚠ Important Points

• Not a theorem (cannot be proved)

• Based on strong evidence

• Foundation of computer science

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1️⃣1️⃣ Halting Problem

✅ Problem Statement

Given a program P and input I, determine:

Will P halt or run forever?

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✅ Result

🚫 Halting Problem is Undecidable

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✅ Proof Idea (Simple)

• Assume halting problem is decidable

• Construct a contradictory machine

• Leads to logical contradiction

• Hence undecidable

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1️⃣2️⃣ Importance of Halting Problem

• Shows limits of computation

• Basis of undecidability theory

• Used in compiler design & verification

📘 PAPER 1: THEORY OF COMPUTATION (MCA546) UNIT 4: PUSH DOWN AUTOMATA (PDA) (university of allahabad)

 

🔴 UNIT 4: PUSH DOWN AUTOMATA (PDA)

This unit is very important because it connects CFG and Automata, and questions often come for 5–10 marks.

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1️⃣ Pushdown Automata (PDA)

✅ Definition

A Pushdown Automaton (PDA) is a computational model that:

• Has finite control

• Uses a stack (LIFO)

• Recognizes Context-Free Languages (CFLs)

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✅ Formal Definition

A PDA is a 7-tuple:

M = (Q, Σ, Γ, δ, q₀, Z₀, F)

Where:

• Q → Finite set of states

• Σ → Input alphabet

• Γ → Stack alphabet

• δ → Transition function

• q₀ → Initial state

• Z₀ → Initial stack symbol

• F → Final states

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✅ Transition Function

$$\delta : Q × (Σ ∪ \{ε\}) × Γ → P(Q × Γ^*)$$

Meaning:

• Read input symbol or ε

• Pop top of stack

• Push new symbol(s)

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2️⃣ Instantaneous Description (ID)

✅ Definition

An Instantaneous Description (ID) describes the current status of a PDA at any moment.

Format:

$$(q, w, γ)$$

Where:

• q → Current state

• w → Remaining input

• γ → Stack content

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✅ Example

(q₀, 1011, Z₀)

Means:

• Current state = q₀

• Input remaining = 1011

• Stack contains = Z₀

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3️⃣ Acceptance by PDA

A PDA can accept a language in two ways:

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🔹 1. Acceptance by Final State

A string is accepted if:

• Input is completely read

• PDA reaches a final state

✔ Stack content does not matter

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🔹 2. Acceptance by Empty Stack

A string is accepted if:

• Input is fully consumed

• Stack becomes empty

✔ Final state not necessary

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⚠ Important Theorem

👉 Both methods are equivalent in power

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4️⃣ Deterministic Pushdown Automata (DPDA)

✅ Definition

A PDA is deterministic if:

• For a given state, input symbol, and stack symbol

• Only one transition is possible

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✅ Key Points

• DPDA ⊂ PDA

• DPDA recognizes deterministic CFL

• Not all CFLs are accepted by DPDA

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✅ Example

Language:
L = { aⁿbⁿ | n ≥ 1 }

✔ Can be accepted by DPDA

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❌ Example

L = { wwʳ | w ∈ {a,b}* }

✖ Cannot be accepted by DPDA

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5️⃣ Relationship Between PDA and CFG

🔹 Important Theorem

A language is context-free if and only if it is accepted by a PDA

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🔹 CFG → PDA

For every CFG, there exists a PDA that:

• Simulates leftmost derivation

• Uses stack to store variables

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🔹 PDA → CFG

For every PDA, a CFG can be constructed that:

• Generates same language

• Uses transitions to define productions

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🔁 Relationship Diagram

CFG  ⇄  PDA

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6️⃣ PDA for Language aⁿbⁿ

Language:

L = { aⁿbⁿ | n ≥ 1 }

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Working:

1. Push a onto stack for each input a

2. Pop one a for each b

3. Accept when stack is empty

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Stack Behavior:

Input	Stack
a	push a
a	push a
b	pop a
b	pop a

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7️⃣ Difference: FA vs PDA

Finite Automata	Pushdown Automata
No stack	Has stack
Recognizes regular languages	Recognizes CFL
Limited memory	Infinite memory (stack)
DFA/NFA	PDA

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8️⃣ Applications of PDA

• Syntax checking

• Compiler design

• Expression evaluation

• Parsing in programming languages

📘 PAPER 1: THEORY OF COMPUTATION (MCA546) UNIT -3 Context Free Grammars (CFG) (university of allahabad)

🔴 UNIT 3: Context Free Grammars (CFG)

(EXTREMELY IMPORTANT – Long answers + conversions often asked)

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1️⃣ Context Free Grammar (CFG)

Definition

A Context Free Grammar is a grammar in which each production rule has a single non-terminal on the left-hand side.

Formal Definition

A CFG is a 4-tuple:

G = (V, Σ, P, S)

Where:

• V → Set of non-terminals

• Σ → Set of terminals

• P → Production rules

• S → Start symbol

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Form of Productions

A → α
Where:

• A ∈ V

• α ∈ (V ∪ Σ)*

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Example

Grammar for L = { aⁿbⁿ | n ≥ 1 }

Productions:

• S → aSb

• S → ab

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2️⃣ Derivation in CFG

Derivation

Process of generating strings from the start symbol using production rules.

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Types of Derivation

Leftmost Derivation

• Always expand the leftmost non-terminal

Rightmost Derivation

• Always expand the rightmost non-terminal

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Example

S → aSb → aab b

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3️⃣ Derivation Trees (Parse Trees)

Definition

A derivation tree (or parse tree) is a tree representation of how a string is generated in a CFG.

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Properties

• Root → Start symbol

• Internal nodes → Non-terminals

• Leaves → Terminals

• Reading leaves left to right gives the string

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Importance

• Shows structure of language

• Used in compilers

• Common exam question

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4️⃣ Ambiguity in CFG

Ambiguous Grammar

A grammar is ambiguous if a string has more than one parse tree.

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Example

Grammar:
E → E + E | E * E | id

Expression: id + id * id

➡ Two different parse trees possible

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Key Point

• Some languages are inherently ambiguous

• Ambiguity is undesirable in compilers

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5️⃣ Simplification of Context Free Grammars

Simplification means removing unnecessary productions without changing the language.

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Steps of Simplification

(a) Removal of ε-productions

Productions of the form:
A → ε

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(b) Removal of Unit Productions

Productions of the form:
A → B

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(c) Removal of Useless Symbols

• Non-generating symbols

• Unreachable symbols

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Result

Simplified grammar that generates the same language

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6️⃣ Chomsky Normal Form (CNF)

Definition

A CFG is in Chomsky Normal Form if all productions are of the form:

• A → BC

• A → a

Where:

• A, B, C ∈ V

• a ∈ Σ

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Special Rule

S → ε allowed if ε ∈ L

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Steps to Convert CFG to CNF

1. Remove ε-productions

2. Remove unit productions

3. Remove useless symbols

4. Convert long RHS into binary form

5. Replace terminals in long productions

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Importance

• Used in parsing algorithms

• Asked as long conversion question

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7️⃣ Greibach Normal Form (GNF)

Definition

A CFG is in Greibach Normal Form if every production is of the form:

A → aα

Where:

• a ∈ Σ

• α ∈ V*

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Characteristics

• Right-hand side starts with a terminal

• No left recursion

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Importance

• Useful in top-down parsing

• Ensures derivations consume input symbols

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8️⃣ Decision Algorithms for CFG

Decidable Problems

1. Membership problem
   Given string w, determine whether w ∈ L(G)

2. Emptiness problem
   Determine whether L(G) = ∅

3. Finiteness problem
   Check if L(G) is finite

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Undecidable Problem

• Equivalence of two CFGs

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🔑 CFG vs Regular Grammar

CFG	Regular Grammar
More powerful	Less powerful
PDA required	FA sufficient
Supports recursion	No recursion

📘 PAPER 1: THEORY OF COMPUTATION (MCA546)🔴 UNIT 2: Finite Automata (university of allahabad)

🔴 UNIT 2: Finite Automata

(VERY HIGH EXAM WEIGHT – Definitions + Diagrams + Theorems)

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1️⃣ Finite Automata (FA)

A Finite Automaton is a mathematical model of computation used to recognize regular languages.

Formal Definition

A finite automaton is a 5-tuple:

M = (Q, Σ, δ, q₀, F)

Where:

• Q → Finite set of states

• Σ → Input alphabet

• δ → Transition function

• q₀ → Initial state

• F → Set of final (accepting) states

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2️⃣ Deterministic Finite Automata (DFA)

Definition

A DFA is a finite automaton in which:

• For each state and input symbol, there is exactly one transition

• No ε-moves allowed

Transition Function

δ : Q × Σ → Q

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Example

Language: Strings ending with 01

States:

• q₀ → start

• q₁ → last symbol was 0

• q₂ → accept (01 found)

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Key Properties

• Simple and predictable

• Easy to implement

• Always deterministic

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3️⃣ Non-Deterministic Finite Automata (NFA)

Definition

An NFA allows:

• Multiple transitions for the same input

• ε-transitions (move without input)

Transition Function

δ : Q × (Σ ∪ {ε}) → 2^Q

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Important Fact

➡ Every NFA has an equivalent DFA

(This is a very common exam question)

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Difference: DFA vs NFA

DFA	NFA
One transition per symbol	Multiple transitions
No ε-moves	ε-moves allowed
Faster execution	Easier to design
Harder to design	Simple construction

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4️⃣ ε-Moves (Moves on Empty String)

An ε-move allows the automaton to:

• Change state without reading any input

Why Needed

• Simplifies automaton design

• Useful in regular expression to NFA conversion

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5️⃣ Regular Expressions

Definition

A Regular Expression (RE) describes regular languages using symbols and operators.

Operators

• Union: |

• Concatenation: .

• Closure (Kleene star): *

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Examples

• (0|1)* → All binary strings

• 0*1* → Any number of 0s followed by 1s

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Equivalence

➡ Regular Expression ⇔ Finite Automaton

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6️⃣ Regular Sets (Regular Languages)

A language is called regular if:

• It can be represented by a DFA

• Or an NFA

• Or a Regular Expression

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Examples

• Set of strings with even number of 0s

• Strings ending with 1

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7️⃣ Relationship with Regular Grammars

Regular Grammar

A grammar is regular if its production rules are of the form:

• A → aB

• A → a

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Important Relationship

• Every regular grammar generates a regular language

• Every regular language has a regular grammar

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8️⃣ Pumping Lemma for Regular Sets

Purpose

Used to prove that a language is NOT regular

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Statement

If L is a regular language, then ∃ a constant p such that any string w ∈ L with |w| ≥ p can be split into:

w = xyz

Such that:

1. |y| > 0

2. |xy| ≤ p

3. xyⁱz ∈ L for all i ≥ 0

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Usage

• Assume language is regular

• Apply pumping lemma

• Reach contradiction

• Conclude: Language is not regular

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Common Example

L = {0ⁿ1ⁿ | n ≥ 1} → NOT regular

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9️⃣ Closure Properties of Regular Sets

Regular languages are closed under:

• Union

• Intersection

• Complement

• Difference

• Concatenation

• Kleene star

• Reversal

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Meaning

If L₁ and L₂ are regular, then:

• L₁ ∪ L₂ is regular

• L₁ ∩ L₂ is regular

➡ Frequently asked in short notes

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🔟 Decision Algorithms for Regular Sets

Problems That Are Decidable

• Emptiness problem

• Membership problem

• Equivalence of DFA

• Finiteness of language

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Meaning

There exists an algorithm that always terminates and gives correct answer.

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1️⃣1️⃣ Minimization of Finite Automata

Goal

Reduce the number of states without changing the language

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Steps

1. Remove unreachable states

2. Identify equivalent states

3. Merge equivalent states

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Why Important

• Reduces memory

• Improves efficiency

• Asked as long question