## What is the best Greedy approach for the fractional knapsack problem?

The basic idea of the greedy approach is to calculate the ratio value/weight for each item and sort the item on basis of this ratio. Then take the item with the highest ratio and add them until we can’t add the next item as a whole and at the end add the next item as much as we can.

## Is fractional knapsack is an example of greedy method?

In Fractional Knapsack Problem, As the name suggests, items are divisible here. We can even put the fraction of any item into the knapsack if taking the complete item is not possible. It is solved using Greedy Method.

**How do you prove the greedy choice property?**

Greedy choice property: We show greedy choice property holds to show that the greedy choice we make in our algorithm makes sense. We prove this property by showing that there is an optimal solution such that it contains the best item according to our greedy criterion.

### What is a greedy choice property?

Greedy-choice property: a globally optimal solution can be arrived at by making a locally optimal (greedy) choice. Optimal substructure: A problem exhibits optimal substructure if an optimal solution to the problem contains within its optimal solutions to subproblems.

### What is the optimal value in case of fractional knapsack problem?

Now, the capacity of the Knapsack is equal to the selected items. Hence, no more item can be selected. This is the optimal solution. We cannot gain more profit selecting any different combination of items.

**What is the difference between 0 1 knapsack and fractional knapsack problems?**

What is the difference between knapsack and 0 1 knapsack problem? In the 0–1 Knapsack problem, we are not allowed to break items. We either take the whole item or don’t take it. In Fractional Knapsack, we can break items for maximizing the total value of knapsack.

#### Which is optimal value in the case of fractional knapsack problem?

#### What are the characteristics of greedy algorithm?

Characteristics of Greedy approach

- There is an ordered list of resources(profit, cost, value, etc.)
- Maximum of all the resources(max profit, max value, etc.) are taken.
- For example, in fractional knapsack problem, the maximum value/weight is taken first according to available capacity.

**How do you prove optimal solution?**

If there is a solution y to the system AT y = cB such that AT y ≤ c, then x is optimal. By = cB and AT y ≤ c. m i=1 aijyi = ci. are obeyed, then x and y must be optimal.

## What are the characteristics of a greedy strategy for any a problem?

## How do you solve a fractional knapsack problem?

Steps to solve the Fractional Problem:

- Compute the value per pound for each item.
- Obeying a Greedy Strategy, we take as possible of the item with the highest value per pound.
- If the supply of that element is exhausted and we can still carry more, we take as much as possible of the element with the next value per pound.

**How to prove that the fractional knapsack problem exhibits the greedy choice property?**

Prove that the fractional knapsack problem has the greedy-choice property. An optimal solution to a problem can be obtained by making local best choices at each step of the algorithm.

### When do you put an I in a knapsack?

When the knapsack is completely full of a i or all available a i has been placed in the knapsack, we have an optimal solution, achieved by being as greedy as possible with the choice a i. Thanks for contributing an answer to Mathematics Stack Exchange!

### Are there greedy algorithms for the unbounded knapsack problem?

Some references on greedy algorithms for the Unbounded Knapsack Problem are [38,42,49,53,54]. Economic issues are central to the control of disease because of the limited funds available for public health everywhere in the world, even in the wealthiest nations.

**What is the 0 / 1 multidimensional knapsack problem?**

The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields.