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Q: Apply the dynamic algorithm of Knapsack problem for the following inputs. Knapsack of capacity W = 10 item weight value 1 1 $10 2 2 $40 3 1 $20 4 3 $20 5 5 $60

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter19: Probabilistic Dynamic Programming

Section19.4: Further Examples Of Probabilistic Dynamic Programming Formulations

Problem 10P

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Question

Q: Apply the dynamic algorithm of Knapsack problem for the following inputs.

Knapsack of capacity W = 10

item weight value

1 1 $10

2 2 $40

3 1 $20

4 3 $20

5 5 $60

Process or set of rules that allow for the solving of specific, well-defined computational problems through a specific series of commands. This topic is fundamental in computer science, especially with regard to artificial intelligence, databases, graphics, networking, operating systems, and security.

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Develop a dynamic programming algorithm for the knapsack problem: given n items of know weights w1, . . . , wn and values v1, . . . ,vn and a knapsack of capacity W, find the most valuable subset of the items that fit into the knapsack. We assume that all the weights and the knapsack’s capacity are positive integers, while the item values are positive real numbers. (This is the 0-1 knapsack problem). Analyze the structure of an optimal solution. Give the recursive solution. Give a solution to this problem by writing pseudo code procedures. Analyze the running time for your algorithms.
knapsack problem: given the first table: c beeing value and w beeing weight, W max weight. I got table 2 as a solution to: 2 Solve the Knapsack problem with dynamic programming. To do this, enter the numbers Opt[k,V ] for k = 1,...,5 and V = 1,...,9 in a table. Here Opt[k, V ] is the partial solution obtained for the first k items with maximum weight V. " Can somebody explain me the values of the table? How do they get calculated? Also how do i solve the followup-task: Using the values in the table, determine a solution OPTSOL(I)=(β1,β2,β3,β4), starting with β4. (Use backtracing to do this)
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use java programming language to explain if needed, please be clear, take your time, thanks
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