Practice Question 2

Duration: 4 min

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Module outline

  1. Discrete Mathematics: Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic
  2. DataBase Management System/DBMS: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control
  3. Digital Electronics: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  4. Computer Architecture: Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design
  5. Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management
  6. C Language: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  7. Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  8. Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos
  9. Computer Networks: Introduction to CN, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware Basics
  10. Theory Of Computation/Automata Theory: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory
  11. Compiler Design: Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  12. Engineering Mathematics: Permutation and Combination, Linear Algebra, Calculus, Probability, Statistics
  13. General Aptitude: Ratio and Proportion (Ratios), Divisibility Rules, Data Interpretation, Logarithm, Number System, HCF LCM, Sequence and Series (Series), Speed Time and Distance, Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Data Sufficiency, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Percentage, Mensuration and Geometry, Mental Ability, Arithmetic, Profit and Loss, Powers and Exponents (Surds and Indices), Average, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Syllogisms, Venn Diagram, Seating Arrangements, Blood Relations, Directions (Direction Test), Analogy, Algebra, Time and Work, Analytical Reasoning (Counting Figures Reasoning), Puzzle Solving (Puzzles), Cubes & Dices, Ranking, Order and Sequence, Mixture and Alligation, Age Problems, Clock, Selection Decision Table (Decision Making), Data Arrangement
  14. English (Verbal Aptitude): Vocabulary, Noun, Subject Verb Agreement (Verb Noun Agreement), Adjectives, Tenses, Pronoun, Preposition, Direct and Indirect Speech, Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Completion (Fill in the blanks), Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Sentence Correction (Error Correction), Verbal Analogy (Word Based Analogy), Conjunction, Interjection, Verb, Articles, Adverb, Modals, Sentence Construction
  15. Live Classes Recordings(Earlier Batch): GATE 2026 Live Class
  16. Full Mock Test:
  17. Previous Year Papers:
  18. GATE 2026 Counselling: Counselling and Guidance Sessions
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This lecture segment focuses on asymptotic analysis, specifically Big O notation and the principle of ignoring constants in algorithm complexity. The instructor introduces a practice problem comparing two linear functions, f(n) = 4n and g(n) = n. The core objective is to determine the correct asymptotic relationship between these functions among four multiple-choice options. The lesson emphasizes that constant multipliers do not affect the growth rate of an algorithm, meaning 4n and n are asymptotically equivalent. Through visual demonstrations on a digital whiteboard, the instructor calculates specific values for n=1 and n=2 to verify that f(n) remains proportional to g(n). The analysis concludes by identifying Option A as the correct answer, confirming that f(n) = O(g(n)) and g(n) = O(f(n)).

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor introduces a problem involving two linear functions, f(n) = 4n and g(n) = n. He begins analyzing the asymptotic relationship between these functions by writing them out on the digital whiteboard and identifying a constant factor c=4. The visual focus is on setting up the definitions for Big O notation to determine which option correctly describes their relationship. On-screen text displays the question: 'Q. Consider the functions: f(n) = 4n and g(n) = n' followed by four options regarding their Big O relationships. The instructor identifies the constant multiplier in a linear function and sets up a comparison to analyze asymptotic equality.

  2. 2:00 – 4:21 02:00-04:21

    The instructor transitions from explaining how to ignore constant multipliers in Big O notation to solving a specific practice problem. He analyzes two functions, f(n) = 4n and g(n) = n, to determine their asymptotic relationship. By removing the constant 4 from f(n), he demonstrates that both functions simplify to n, indicating they are asymptotically equal. He creates a table comparing values for n=1 and n=2, showing f(n)=4*1=4 and c*g(n)=4*1=4. The instructor writes f(n) = O(g(n)) and g(n) <= f(n), concluding that Option A is correct because both functions grow at the same rate despite the constant difference.

The lecture effectively demonstrates that Big O notation focuses on growth rates rather than exact values. By comparing f(n) = 4n and g(n) = n, the instructor illustrates that constant factors like 4 are irrelevant to asymptotic complexity. The use of a comparison table with n=1 and n=2 provides concrete evidence that the functions scale identically. The final conclusion reinforces that f(n) = O(g(n)) and g(n) = O(f(n)), making them asymptotically equal. This approach simplifies complex algorithm analysis by allowing students to ignore constant multipliers and fixed values, focusing solely on the variable component that dictates growth.

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