Solving the Fractional Knapsack Problem - Wave and Thinking

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In summary, the Fractional Knapsack Problem is a mathematical optimization problem where the goal is to maximize the total value of items in a knapsack while staying within the weight capacity. It can be solved using a greedy algorithm, and the Wave and Thinking method is a variation of this approach. The Wave and Thinking method has several advantages, including guaranteeing the optimal solution and considering both high-value and low-weight items. However, it may not always provide the most efficient solution and can be more complex and time-consuming.
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evinda
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Hello! (Wave)

I found the following algorithm for the fractional knapsack problem.

fractional_knapsack.PNG
Why at the case else, we do not change the variable w to w+(W-w)/S.weight? (Thinking)
 
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evinda said:
Why at the case else, we do not change the variable w to w+(W-w)/S.weight?
That would be adding a dimensionless number to pounds.
 

Related to Solving the Fractional Knapsack Problem - Wave and Thinking

What is the Fractional Knapsack Problem?

The Fractional Knapsack Problem is a mathematical optimization problem where a knapsack (a container with a limited capacity) needs to be filled with a combination of items that maximizes the total value of the items while staying within the weight capacity of the knapsack.

What is the difference between the Fractional Knapsack Problem and the 0-1 Knapsack Problem?

The main difference between the Fractional Knapsack Problem and the 0-1 Knapsack Problem is that in the Fractional Knapsack Problem, items can be divided into smaller parts and put into the knapsack, while in the 0-1 Knapsack Problem, items cannot be divided and must be either included or excluded from the knapsack.

How does the "Wave and Thinking" approach solve the Fractional Knapsack Problem?

The "Wave and Thinking" approach is a dynamic programming algorithm that solves the Fractional Knapsack Problem by breaking it down into smaller subproblems and finding the optimal solution for each subproblem. It then uses this information to build up to the optimal solution for the original problem.

What are the advantages of using the "Wave and Thinking" approach for solving the Fractional Knapsack Problem?

The "Wave and Thinking" approach has several advantages, including its efficiency in solving the problem in polynomial time, its ability to handle fractional items, and its ability to handle items with different values and weights. It also has a simple implementation and can handle large problem instances.

Are there any limitations to the "Wave and Thinking" approach for solving the Fractional Knapsack Problem?

While the "Wave and Thinking" approach is efficient and has many advantages, it may not always provide the optimal solution for the Fractional Knapsack Problem. In certain cases, other algorithms such as the Greedy algorithm may provide a better solution. Additionally, the "Wave and Thinking" approach may not be suitable for problems with a large number of items or a large weight capacity for the knapsack.

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