Demo: Types of Functions

Duration: 7 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:
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  18. GATE 2026 Counselling: Counselling and Guidance Sessions
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This lecture provides a comprehensive visual analysis of asymptotic function comparison, establishing a hierarchy of growth rates as input size n approaches infinity. The instructor systematically introduces five distinct categories: decrement, constant, logarithmic, polynomial, and exponential functions. Using a coordinate system where the x-axis represents n approaching infinity and the y-axis represents time, the instructor draws curves to demonstrate relative growth speeds. The lesson culminates in a definitive inequality chain ranking these functions from slowest to fastest, providing students with a clear mental model for algorithmic complexity analysis.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The session begins with the instructor introducing the topic of asymptotic comparison by listing five specific function categories on screen: decrement, constant, logarithmic, polynomial, and exponential functions. He then sets up a graphical framework by drawing the axes of a coordinate system, explicitly labeling the x-axis with 'n -> infinity' to indicate that the analysis focuses on behavior as input size grows indefinitely. This foundational setup prepares students for visualizing how different mathematical expressions scale over time.

  2. 2:00 – 5:00 02:00-05:00

    The instructor proceeds to illustrate the growth rates by drawing specific curves on the graph. He starts with exponential functions, writing examples such as '2^n', 'e^n', and 'n!' to show rapid growth. He then draws polynomial curves, labeling them with examples like 'n', 'n^2', and 'n^4' to demonstrate slower growth compared to exponentials. Logarithmic functions are added next with examples like 'log n' and 'log log n', followed by a horizontal line for constant functions. This visual layering clearly distinguishes the complexity classes based on their steepness and position relative to one another.

  3. 5:00 – 7:15 05:00-07:15

    In the final segment, the instructor completes the hierarchy by adding decrement function examples and writing a summary inequality chain on the board. The final text explicitly ranks the functions: 'Dec. functions < Constant functions < Log functions < Polynomial function < Exponential function'. This conclusion solidifies the visual evidence presented throughout the lecture, providing a concise reference for comparing algorithmic efficiency where decrement functions are fastest and exponential functions represent the slowest growth rate as n approaches infinity.

The lecture effectively constructs a visual hierarchy of algorithmic complexity by mapping five function types onto a single graph. The progression from defining categories to drawing specific curves and finally ranking them creates a logical flow for understanding asymptotic behavior. Key evidence includes the explicit labeling of axes with 'n -> infinity' and 'Time', the specific mathematical examples provided for each class (e.g., '2^n' for exponential, 'n^2' for polynomial), and the final inequality chain that summarizes the growth rates. This visual approach allows students to intuitively grasp why exponential algorithms are generally less efficient than polynomial ones for large inputs.

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