Now we have to fill the knapsack in such a way so that the sum of the values of items filled in the knapsack is maximum. The values and weights associated with each item are shown in the diagram. We have three items namely item1, item2, and item3. ![]() The diagram above shows a Knapsack that can hold up to a maximum weight of 30 units. Select item 2 and Item 3 which will give us total value of 140 + 60 = 200 which is the maximum value we can get among all valid combinations. Refer to the diagram below for a better understanding. Now we have to fill the knapsack in such a way so that the sum of the total weights of the filled items does not exceed the maximum capacity of the knapsack and the sum of the values of the filled items is maximum. We have various items that have different weights and values associated with them. In the 0-1 Knapsack Problem, we are given a Knapsack or a Bag that can hold weight up to a certain value. We also learn two measures of its efficiency: Time and Space Complexity for all the approaches.We learn the implementation of the recursive, top-down, and bottom-up approaches to solve the 0-1 Knapsack Problem.This article defines the 0-1 Knapsack Problem and explains the intuitive logic of this algorithm.In this article, we will discuss 0-1 Knapsack in detail. There are three types of knapsack problems : 0-1 Knapsack, Fractional Knapsack and Unbounded Knapsack. ![]() We have to find the optimal solution considering all the given items. In this problem, we are given a set of items having different weights and values. The Knapsack Problem is an Optimization Problem in which we have to find an optimal answer among all the possible combinations.
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