Practice Question

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/HPSC-PGT-COMPUTER-SCIENCE/data-structure-algorithms/time-complexity-analysis/master-theorem/asset-practice-question-4691 after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. HPSC Interview Preparation: Interview Preparation
  2. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  3. Data Structure & Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Sorting Algorithms, Minimum Spanning Trees, Time Complexity Analysis, Graph Theory, Shortest Path Algos
  4. Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  5. Computer System & Organisation: Basics of COA, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Programming the Basic Computer
  6. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, SQL, File Organization & Indexing, Transaction Management, Concurrency Control
  7. Operating Systems: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling
  8. Digital Logic & Circuits: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  9. Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Pushdown Automata & CFG, Turing Machines, Moore & Mealy Machines
  10. 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
  11. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Data Warehousing & Mining
  12. Introduction to Emerging Trends: Emerging Tech Trends, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, व्यापार, विपणन एवं उपभोक्ता संरक्षण / Trade, Marketing and Consumer Protection, कंप्यूटर नेटवर्क / Computer Networks
  13. Digital Society & Cyber Law: Society, Law & Ethics, साइबर सुरक्षा / Cyber Safety and Security
  14. HPSC PGT Previous Year Paper:
  15. HPSC Assistant Professor Previous Year Paper:
  16. Live Sessions: HPSC Live Sessions, EMRS TGT/PGT Live Class
  17. Full Mock Test:
  18. HPSC PGT - MOCK Test Series:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a detailed solution for the recurrence relation T(n) = 9T(n/3) + n using the Master Theorem. The instructor clearly writes the general form T(n) = a * T(n/b) + f(n) and maps the specific values a=9, b=3, and f(n)=n. He calculates the term n^(log_b a), simplifying n^(log_3 9) to n^2. Comparing f(n)=n with n^2, he establishes that f(n) is polynomially smaller, satisfying f(n) = O(n^(2-epsilon)) for epsilon > 0. This confirms Case 1 applies. He concludes the complexity is determined by the recursive part, writing T(n) = Theta(n^(log_b a)), and simplifies the result to Theta(n^2).

Chapters

  1. 0:00 – 1:39 00:00-01:39

    The lecture starts with the recurrence T(n) = 9T(n/3) + n. The instructor writes the standard form T(n) = a * T(n/b) + f(n) on the whiteboard. He identifies the parameters a=9 and b=3. He calculates the critical exponent log_b a as log_3 9, which equals 2, resulting in the term n^2. He compares f(n)=n against n^2, noting that f(n) is smaller by a polynomial factor, satisfying f(n) = O(n^(2-epsilon)). This identifies the scenario as Case 1 of the Master Theorem. He writes the general solution T(n) = Theta(n^(log_b a)) and substitutes the values to get Theta(n^(log_3 9)). Finally, he underlines the simplified answer Theta(n^2).

This lesson demonstrates the Master Theorem for divide-and-conquer algorithms. By identifying parameters a, b, f(n) and comparing growth rates, the instructor shows how to bypass complex expansions. The visual derivation reinforces calculating the critical exponent log_b a to determine the applicable case, leading to a very concise asymptotic bound for the specific recurrence.

Loading lesson…