The problem that is not preferred to be solved using dynamic programming is the Fractional Knapsack problem.

This is because dynamic programming is not suitable for problems where the solution involves fractions. Dynamic programming is typically used for optimization problems where the solution can be built from solutions to subproblems.

In the Fractional Knapsack problem, you can take fractions of items instead of being restricted to taking the whole item or leaving it, which is the case in the 0/1 Knapsack problem. This fractional property makes it more suitable to be solved using a greedy algorithm, not dynamic programming.

In a greedy algorithm, you make the choice that looks best at the moment to get the optimal solution. For the Fractional Knapsack problem, you would take the item with the highest value/weight ratio first, then the next highest, and so on, until you reach the weight capacity of the knapsack.

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The problem that is not preferred to be solved using dynamic programming is the Fractional Knapsack problem.

This is because dynamic programming is not suitable for problems where the solution involves fractions. Dynamic programming is typically used for optimization problems where the solution can be built from solutions to subproblems.

In the Fractional Knapsack problem, you can take fractions of items instead of being restricted to taking the whole item or leaving it, which is the case in the 0/1 Knapsack problem. This fractional property makes it more suitable to be solved using a greedy algorithm, not dynamic programming.

In a greedy algorithm, you make the choice that looks best at the moment to get the optimal solution. For the Fractional Knapsack problem, you would take the item with the highest value/weight ratio first, then the next highest, and so on, until you reach the weight capacity of the knapsack.

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The problem that is not preferred to be solved using dynamic programming is the Fractional Knapsack problem.

This is because dynamic programming is not suitable for problems where the solution involves fractions. Dynamic programming is typically used for optimization problems where the solution can be built from solutions to subproblems.

In the Fractional Knapsack problem, you can take fractions of items instead of being restricted to taking the whole item or leaving it, which is the case in the 0/1 Knapsack problem. This fractional property makes it more suitable to be solved using a greedy algorithm, not dynamic programming.

In a greedy algorithm, you make the choice that looks best at the moment to get the optimal solution. For the Fractional Knapsack problem, you would take the item with the highest value/weight ratio first, then the next highest, and so on, until you reach the weight capacity of the knapsack.

Which of the following problems is not preferred to be solved using dynamic programming?1 point0/1 KnapsackMatrix Chain MultiplicationFractional KnapsackCoin change

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