![]() ![]() So we include it partially.įinal volume = 20+60+50x(15/25)=80+30=110Īssuming items to be indivisible- In this case we will have to leave one item due to insufficient capacity. So, now only 15 units of volume are left for second item. So we include third and first items wholly. For example, one can find an upper bound for a 01 knapsack problem by solving its corresponding fractional knapsack problem. Fractional knapsack problem can be solved in time O(n).Ĭlarification: It is possible to solve the problem in O(n) time by adapting the algorithm for finding weighted medians.ħ. Greedy algorithms implement optimal local selections in the hope that those selections will lead to an optimal global solution for the problem to be solved. So the time complexity will be O(n log n) if we use quick sort for sorting.Ħ. ![]() Given a set of N items each having value V with weight W and the total capacity of a knapsack. Time complexity of fractional knapsack problem is _Ĭlarification: As the main time taking a step is of sorting so it defines the time complexity of our code. Fractional Knapsack Problem Problem Statement. Which of the following statement about 0/1 knapsack and fractional knapsack problem is correct?Ī) In 0/1 knapsack problem items are divisible and in fractional knapsack items are indivisibleĬ) 0/1 knapsack is solved using a greedy algorithm and fractional knapsack is solved using dynamic programmingĭ) In 0/1 knapsack problem items are indivisible and in fractional knapsack items are divisibleĬlarification: In fractional knapsack problem we can partially include an item into the knapsack whereas in 0/1 knapsack we have to either include or exclude the item wholly.ĥ. What is the objective of the knapsack problem?Ī) To get maximum total value in the knapsackī) To get minimum total value in the knapsackĬlarification: The objective is to fill the knapsack of some given volume with different materials such that the value of selected items is maximized.Ĥ. Fractional knapsack problem is solved using greedy method in the following steps- Step-01: For each item, compute its value / weight ratio. The correct option is (d) Fractional knapsack problem Best explanation: The fractional knapsack problem is solved using a greedy algorithm. At the end, we add the next item as much as we can.ģ. We first sort items according to their value/weight ratio and then add item with highest ratio until we cannot add the next item as a whole. Keywords: packing, knapsack problem, dynamic programming, approximation algorithms, computational experiments. ![]() Fractional knapsack problem is solved most efficiently by which of the following algorithm?Ĭlarification: Greedy algorithm is used to solve this problem. Fractional knapsack is solved using dynamic programming.Ģ. Fractional knapsack problem is also known as _Ĭlarification: Fractional knapsack problem is also called continuous knapsack problem. You have a knapsack that can hold 20 pounds. Data Structures & Algorithms Multiple Choice Questions on “Fractional Knapsack Problem”.ġ. For the Fractional Knapsack Problem, determine the maximum total value of your backpack. ![]()
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