Python Knapsack: you are carrying a knapsack with capacity to hold a total of weight C. You are selecting among n items with values A={a1, a2, ... , an} and associated weights W={w1, w2, ... , wn}. Here the weights and values are all positive. You wish to maximize the total value of the items you select not exceeding the given weight capacity, example, maximize sum_{a in A} such that sum_{w in W} <= C. Note that you can only select your items once. In Python, create function DP_K (C, A, W) as stated above. Input an array to the function to hold the backtrace. What is the best set of items for C = 30, A = [40, 110, 100] and w = [20, 30, 40]? This can be shown using auxiliary printing function.

Python Knapsack: you are carrying a knapsack with capacity to hold a total of weight C. You are selecting among n items with values A={a1, a2, ... , an} and associated weights W={w1, w2, ... , wn}. Here the weights and values are all positive. You wish to maximize the total value of the items you select not exceeding the given weight capacity, example, maximize sum_{a in A} such that sum_{w in W} <= C. Note that you can only select your items once. In Python, create function DP_K (C, A, W) as stated above. Input an array to the function to hold the backtrace. What is the best set of items for C = 30, A = [40, 110, 100] and w = [20, 30, 40]? This can be shown using auxiliary printing function.

C++ Programming: From Problem Analysis to Program Design

8th Edition

ISBN:9781337102087

Author:D. S. Malik

Publisher:D. S. Malik

Chapter5: Control Structures Ii (repetition)

Section: Chapter Questions

Problem 27PE

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