Knap Sack Problem

Duration: 13 min

This video lesson is available to enrolled students.

Return to /learn/DSSSB-TGT-COMPUTER-SCIENCE-COMPLETE-COURSE/design-analysis-of-algorithms/greedy-algorithms/fractional-knapsack/asset-knap-sack-problem after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. Section A: General Awareness: Polity, वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research, Biology, Chemistry, Static General Knowledge, History, Geography, Indian Economy, समसामयिकी / Current Affairs, Evolution of Indian Banking (Banking & Financial Awareness), Environment
  3. Section A: General Intelligence & Reasoning ability: श्रेणी / Series, कूट लेखन एवं कूट वाचन / Coding-Decoding, Data Sufficiency, अशाब्दिक तर्क / Non-Verbal Reasoning, Analytical Reasoning (Counting Figures Reasoning), रक्त संबंध / Blood Relations, Syllogisms, कथन आधारित तार्किक विवेचन / Statement-Based Reasoning, बैठक व्यवस्था एवं पहेलियाँ / Seating Arrangement and Puzzles, दिशा एवं दूरी / Direction and Distance, गणितीय संक्रियाएँ एवं लुप्त संख्या / Mathematical Operations and Missing Numbers, कैलेंडर एवं घड़ी / Calendar and Clock, Clock, घन, पासा एवं आकृतियों की गिनती / Cubes, Dice and Figure Counting, आँकड़ों की पर्याप्तता एवं निर्णय क्षमता / Data Sufficiency and Decision Making, वर्णमाला एवं संख्या परीक्षण / Alphabet and Number Test, Crypt Arithmetic, वेन आरेख, न्याय वाक्य एवं असमानता / Venn Diagrams, Syllogism and Inequalities, Data Arrangement, Inequality, सादृश्यता एवं वर्गीकरण / Analogy and Classification, क्रम एवं रैंकिंग / Order and Ranking, TCS NQT Advanced Reasoning Ability
  4. Section A: Arithmetical & Numerical Ability: अनुपात और समानुपात / Ratio and Proportion, Divisibility Rules, आँकड़ा निर्वचन / Data Interpretation, मिश्रण और एलिगेशन / Mixture and Alligation, Permutation and Combination, Logarithm, संख्या पद्धति / Number System, महत्तम समापवर्तक और लघुत्तम समापवर्त्य / HCF and LCM, Sequence and Series (Series), समय, चाल और दूरी / Time, Speed and Distance, प्रतिशत / Percentage, औसत / Average, लाभ, हानि और बट्टा / Profit, Loss and Discount, साधारण और चक्रवृद्धि ब्याज / Simple and Compound Interest, समय और कार्य / Time and Work, Pipe and Cistern, घन क्षेत्रमिति / Solid Mensuration, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, Algebra, Probability, Mental Ability, Age Problems, Partnership, Statistics, Arithmetic, Speed Mathematics and Basic Mathematics, Miscellaneous
  5. Section A: English Language & Comprehension: Vocabulary, Subject Verb Agreement (Verb Noun Agreement), Adjectives, Verbs and Tenses, Reading Comprehension, Parts of Speech, Sentence Construction, Prepositions and Conjunctions, Direct and Indirect Speech, Sentence Completion (Fill in the blanks), Sentence Structure and Common Errors, Modals, Transformation of Sentences, Sentence Correction (Error Correction), Adverb, Conjunction, Cloze Test (Cloze Passage)
  6. Section A: Hindi Language & Comprehension: भाषा, लिपि और वर्ण, वर्तनी, वर्ण-विन्यास, अनुस्वार, अनुनासिक, चंद्रबिंदु और ‘र’ के रूप, शब्द, पद और शब्द-भेद, संज्ञा और सर्वनाम, पद-भेद और रूप-विकार, लिंग, वचन, पुरुष और कारक, काल, वाच्य, वाक्य और वाक्य-भेद, संधि, समास, विलोम शब्द, पर्यायवाची शब्द, सामान्य अशुद्धियाँ, अनेक शब्दों के लिए एक शब्द, शब्द-भंडार, अपठित बोध, Multi-Topic Content (Cross-Concept Material), वर्णमाला
  7. Section B: Programming, Data Structures, OOP & Java: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Java, OOPS with C++, Language Design, Intro to Compilers, Visual Basics, Syntax Analysis: Bottom-Up, Object Oriented Technology
  8. Section B: Computer Networks, TCP/IP & Network Security: Introduction to CN, Data Communication, DLL: Flow Control, DLL: Access Control, DLL: Error Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Access Technologies, Network Security, Society, Law & Ethics, Mobile Computing, Net Layer:Routing Protocol, Cyber Safety and Security
  9. Section B: Teaching Methodology: वृद्धि और विकास / Growth and Development, Concept of Pedagogy, विकास के सिद्धांत / Theories of Development, समावेशी शिक्षा / Inclusive Education, Experiential Pedagogy, आकलन और मूल्यांकन / Assessment and Evaluation, समाजीकरण और जेंडर / Socialisation and Gender, National Education Policy (NEP) 2020, संज्ञानात्मक प्रक्रियाएँ / Cognitive Processes, शिक्षण योजना और शिक्षण कौशल / Teaching Planning and Teaching Skills, Personality Development, शिक्षण विधियाँ / Teaching Methods, अधिगम और व्यवहारवादी सिद्धांत / Learning and Behavioural Theories, Learners & Learning Process, Factors Affecting Teaching, शिक्षण विधियाँ एवं उपागम / Teaching Methods and Approaches, शिक्षण-अधिगम प्रक्रिया एवं कक्षा प्रबंधन / Teaching-Learning Process and Classroom Management, शिक्षण सहायक सामग्री एवं शैक्षिक तकनीकी / Teaching Aids and Educational Technology, सूक्ष्म शिक्षण एवं कक्षा संप्रेषण / Microteaching and Classroom Communication, आकलन एवं मूल्यांकन / Assessment and Evaluation, पाठ्यचर्या, कला एवं सह-शैक्षिक क्षेत्र / Curriculum, Arts and Co-curricular Areas, दिव्यांग बच्चों की शिक्षा / Education of Children with Disabilities, शिक्षक: गुण, व्यावसायिक नैतिकता एवं विकास / Teacher: Qualities, Professional Ethics and Development
  10. Section B: Digital Electronics / Boolean Logic: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Analog-Digital Conversion
  11. Section B: Operating Systems & Linux: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, File Management, Disc Scheduling, Unix OS, Linux OS, Distributed Systems, Windows OS
  12. Section B: Data Base Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, Relational Algebra, SQL, File Organization & Indexing, Transaction Management, Concurrency Control, Database Security & Authorization, Database Recovery, Data Warehousing & Mining
  13. Section B: Internet & Web Technologies: Website, Browser & Services, Internet Fundamentals, Web Design & Development, Protocols & Services, Web Publishing & Hosting, Server Side & Security, HTML Basics, CSS, Emerging Technologies, JavaScript-Basics, .Net Programming, Electronic Data Interchange and E-Commerce, XML
  14. Section B: Computer Fundamentals, IT Basics & Architecture: Basics of COA, Cache Memory Organization, Input Output Organisation, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer, Terms & Abbreviations, MS Office Applications, Instr Formats & Modes, Floating Point Rep, कंप्यूटर के मूल सिद्धांत / Computer Fundamentals, साइबर सुरक्षा / Cyber Safety and Security, कंप्यूटर सॉफ़्टवेयर / Computer Software, ऑपरेटिंग सिस्टम / Operating System, वर्ड प्रोसेसिंग (MS Word) / Word Processing (MS Word), कंप्यूटर नेटवर्क / Computer Networks, प्रेज़ेंटेशन (MS PowerPoint) / Presentation Software (MS PowerPoint)
  15. Section B: Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  16. Section B: Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, extras, Fundamentals of Systems and Information Systems
  17. Section B: Design & Analysis of Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  18. Section B: Mathematics: Linear Algebra, Calculus, Trigonometry, Vectors, Set Theory, Functions, Relations, Propositional and Predicate Logic, Research Process Steps, Graph Theory, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, संख्या पद्धति और संख्या सिद्धांत / Number Systems and Number Theory, यूक्लिडीय ज्यामिति और क्षेत्रमिति / Euclidean Geometry and Mensuration, सांख्यिकी और प्रायिकता / Statistics and Probability, निर्देशांक ज्यामिति, सदिश और त्रिविमीय ज्यामिति / Coordinate Geometry, Vectors and 3D Geometry, बहुपद, समीकरण और सम्मिश्र संख्याएँ / Polynomials, Equations and Complex Numbers, समतल क्षेत्रमिति / Plane Mensuration, प्रतिचयन / Sampling
  19. Section B: Computer Graphics, Multimedia & Mobile Computing: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing, Multimedia
  20. Section B: Emerging Trends: Emerging Tech Trends, Knowledge Representation, Natural Language Processing, व्यापार, विपणन एवं उपभोक्ता संरक्षण / Trade, Marketing and Consumer Protection
  21. DSSSB Previous Year Paper:
  22. Business Studies and Management: Business Communication, प्रबंध के सिद्धांत एवं कार्य / Principles and Functions of Management, लेखांकन के आधार एवं लेन-देनों का अभिलेखन / Accounting Fundamentals and Recording of Transactions, Business Economics, ह्रास, अंतिम खाते व कंप्यूटरीकृत लेखांकन / Depreciation, Final Accounts and Computerised Accounting, Knowledge Management & New Economy
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video provides a comprehensive introduction to the Knapsack Problem, a classic problem in combinatorial optimization. It begins by defining the problem: given a set of items, each with a weight and a value, the goal is to determine the number of each item to include in a collection such that the total weight is less than or equal to a given limit, while maximizing the total value. The lecture uses visual aids, including images of a gold bar, diamond, ruby, and coin, to illustrate items with varying weights and values against a backpack with a maximum weight capacity. The instructor then delves into the historical context, referencing early works from 1897 and mathematician Tobias Dantzig. The core of the lecture transitions to a specific numerical example involving three objects (O1, O2, O3) with defined weights and profits. The instructor demonstrates how to solve this problem using different greedy strategies, specifically analyzing selection based on total profit, total weight, and profit-to-weight ratio. He performs calculations on the screen to show how items can be selected, including fractional inclusion, to achieve the maximum possible profit within the knapsack's capacity limit. This structured approach helps students understand both the theoretical underpinnings and the practical calculation methods required for this algorithmic challenge.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The lecture opens with a slide titled "Knap Sack Problem" and a bullet point defining it as a problem in combinatorial optimization. The text explains that given a set of items, each with a weight and a value, one must determine the number of each item to include so the total weight is within a limit and total value is maximized. Below the text, four items are displayed: a gold bar (Wt. = 5, Value = 10), a diamond (Wt. = 3, Value = 20), a ruby (Wt. = 8, Value = 25), and a coin (Wt. = 4, Value = 8). A graphic of a green and brown backpack is shown with an arrow pointing to "Maximum wt. = 13". The instructor uses these visuals to set the stage for understanding the constraints and objectives of the problem, explaining that the goal is to fill the backpack with the most valuable items possible without exceeding the weight limit.

  2. 2:00 – 5:00 02:00-05:00

    The slide changes to provide historical context. Text on the left states that the knapsack problem has been studied for more than a century, with early works dating as far back as 1897. It specifically mentions that the name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956). To the right, there is a black and white photograph of a bearded man in a suit, presumably Tobias Dantzig. The instructor discusses this history, emphasizing the longevity and significance of the problem in the field of mathematics and computer science, noting that it has been a subject of study for over a hundred years.

  3. 5:00 – 10:00 05:00-10:00

    A new slide appears with the question: "Consider the weights and values of items listed below. Note that there is only one unit of each item." A table lists three objects: O1 (Weight 18, Profit 25), O2 (Weight 15, Profit 24), and O3 (Weight 10, Profit 15). The instructor writes "M = 20" in a red box, indicating the knapsack capacity. He begins to explore different solution strategies. He writes "1 -> Profit" and attempts a calculation involving `1*25 + 2/3*24`. He then writes "2 -> Weight" and calculates `0*18 + 1/2*15 + 1*15`. Finally, he writes "3 -> %" and calculates ratios like `25/18`, `24/15`, and `15/10` to determine the efficiency of each item. This section sets up the comparison of different greedy approaches, showing how different criteria lead to different selection orders. He is demonstrating how to prioritize items based on different metrics to find the optimal solution.

  4. 10:00 – 13:12 10:00-13:12

    The instructor continues the calculation for the third strategy, the percentage or ratio method. He writes out the equation `0*18 + 1*24 + 5/10*15`. This calculation implies selecting object O2 fully (weight 15, profit 24) and half of object O3 (weight 5, profit 7.5) to fill the capacity of 20. He sums the profits to get a total value of `31.5`, which he circles in red. This demonstrates the solution for a fractional knapsack problem where items can be broken, showing how to achieve the maximum profit of 31.5 within the weight limit of 20. The instructor concludes this segment by highlighting the final calculated value, reinforcing the result of the greedy algorithm based on profit-to-weight ratio.

The video effectively bridges the gap between theoretical definition and practical application. It starts with a clear definition and visual examples to ground the concept, moves to historical context to establish credibility, and then applies the theory to a concrete numerical problem. By walking through multiple strategies (profit, weight, ratio) and performing the actual arithmetic, the instructor demonstrates the decision-making process required to solve optimization problems. The final calculation of 31.5 serves as a concrete answer derived from the fractional knapsack logic, providing a clear takeaway for students learning about this algorithm.

Loading lesson…