The knapsack problem is one of the most studied problems in combinatorial optimization, with many real-life applications. For this reason, many special cases and generalizations have been examined. Common to all versions are a set of n items, with each item having an associated profit pj and weight wj. The binary decision variable xj is used to select the item. The objective is to pick some of the items, with maximal total profit, while obeying that the maximum total weight of the chosen items must not exceed W. Generally, these coefficients are scaled to become integers, and they are almost always assumed to be positive. The knapsack problem in its most basic form: (Wikipedia).
0/1 Knapsack problem | Dynamic Programming
Overview of the 0/1 Knapsack problem using dynamic programming Algorithms repository: https://github.com/williamfiset/algorithms My website: http://www.williamfiset.com
From playlist Dynamic Programming
0-1 Knapsack Problem (Dynamic Programming)
Dynamic Programming Tutorial with 0-1 Knapsack Problem
From playlist Dynamic Programming Tutorial Series
Math for Liberal Studies - Lecture 1.9 The Knapsack Problem
This video covers material from Math for Liberal Studies Section 1.9: The Knapsack Problem. In this video, I explain what the knapsack problem is, and we work through an example using a recursive algorithm to solve the problem.
From playlist Math for Liberal Studies Lectures
Knapsack Problem Using Dynamic Programming | 0/1 Knapsack Problem | Data Structures | Simplilearn
This video on knapsack Problem Using Dynamic Programming will acquaint you with a clear understanding of the fractional or 0-1 knapsack problem statement and solution implementation. In this Data Structure Tutorial, you will understand why the difference between 0-1 knapsack and fractional
From playlist Data Structures & Algorithms
Dynamic Programming 1 [Programming Competition Problems]
Source code: http://problemvault.com/index.php#problem127 Problem source / Online judge: https://open.kattis.com/problems/knapsack This video explores a classic dynamic programming problem known as the "0/1 Knapsack Problem". We walk through how the algorithm works, then we go ahead and i
From playlist Programming Competition Problems with Micah Stairs
Problem #3 - Swinging Pendulum
Problem #3 - Swinging Pendulum
From playlist Bi-weekly Physics Problems
When you FINALLY get the courage to perform a Magic Trick!
*Awkward silence
From playlist Magician Problems.
Turing Machines and The Halting Problem (Part 2)
The Halting Problem has fascinated thousands of computer scientists from around the world. A major part of Computing Logic, the proof of the halting problem proves that computers can't do everything. Check out the video to learn more about why computers work the way they do! For Turing Ma
From playlist Math
GeoGebra Link: https://www.geogebra.org/m/f5zgupmz
From playlist Geometry: Challenge Problems
Knapsack, Bandwidth Min. Intro: Greedy Algorithms - Lecture 14
All rights reserved for http://www.aduni.org/ Published under the Creative Commons Attribution-ShareAlike license http://creativecommons.org/licenses/by-sa/2.0/ Tutorials by Instructor: Shai Simonson. http://www.stonehill.edu/compsci/shai.htm Visit the forum at: http://www.coderisland.c
From playlist ArsDigita Algorithms by Shai Simonson
Lec 14 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008
Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming Instructors: Prof. Eric Grimson, Prof. John Guttag View the complete course at: http://ocw.mit.edu/6-00F08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at
From playlist MIT 6.00 Intro to Computer Science & Programming, Fall 2008
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag provides an overview of the course and discusses how we use computational models to understand the world in which we li
From playlist MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
Lec 18 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011
Lecture 18: Optimization Problems and Algorithms Instructor: John Guttag View the complete course: http://ocw.mit.edu/6-00SCS11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.00SC Introduction to Computer Science and Programming
Lecture 26 - The NP-Completeness Challenge
This is Lecture 26 of the CSE373 (Analysis of Algorithms) course taught by Professor Steven Skiena [http://www3.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: https://www.cs.stonybrook.edu/~skiena/373/newlectures/lecture22.pdf More inf
From playlist CSE373 - Analysis of Algorithms 2016 SBU
The corner cube problem is interesting because it initially looks difficult. When the problem was first posed to me, for example, it didn't know how to solve it. Still, my intuition bells were ringing, telling me there was a nice solution. In this video, I cover two of these solutions, in
From playlist Fun
Lec 12 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008
Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming Instructors: Prof. Eric Grimson, Prof. John Guttag View the complete course at: http://ocw.mit.edu/6-00F08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms Mor
From playlist MIT 6.00 Intro to Computer Science & Programming, Fall 2008