New Enroll for RBI, PFRDA & NABARD Intro Class! April 18 at 9:15 PM

    Question

    Which of the following is the most appropriate

    algorithmic approach for solving the "Knapsack Problem" where the goal is to maximize the value within a weight limit?
    A Divide and Conquer Correct Answer Incorrect Answer
    B Greedy Correct Answer Incorrect Answer
    C Dynamic Programming Correct Answer Incorrect Answer
    D Backtracking Correct Answer Incorrect Answer
    E Brute Force Correct Answer Incorrect Answer

    Solution

    The Knapsack Problem is a well-known combinatorial optimization problem that can be efficiently solved using dynamic programming (DP). In this problem, you are given a set of items, each with a weight and a value, and a knapsack with a weight limit. The objective is to determine the maximum value of items that can be put into the knapsack without exceeding its weight limit. DP is the best approach for solving the Knapsack Problem because it breaks the problem down into smaller subproblems and stores the solutions to these subproblems to avoid redundant calculations. The time complexity of the DP approach is O(nW), where nnn is the number of items and W is the weight capacity of the knapsack. • Why this is correct: Dynamic programming is optimal for the Knapsack Problem because it handles overlapping subproblems efficiently and provides an exact solution by storing intermediate results. ________________________________________ Why Other Options Are Incorrect: 1. Divide and Conquer: Divide and conquer is not suitable for the Knapsack Problem as it doesn’t efficiently handle overlapping subproblems or store intermediate results. 2. Greedy: The greedy algorithm does not guarantee an optimal solution for the Knapsack Problem, as it makes decisions based on local optimal choices, which might not lead to a globally optimal solution. 3. Backtracking: Backtracking is a less efficient approach for the Knapsack Problem and can result in an exponential time complexity, as it explores all possible combinations without pruning suboptimal solutions. 4. Brute Force: Brute force examines all possible subsets of items, which has an exponential time complexity and is not efficient for larger instances of the problem.

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