Python Knapsack Problem: imagine you are carrying a knapsack with capacity to hold a total of weight C. You are selecting among n items with values A={a_1, a_2, ... , a_n} and associated weights W={w_1, w_2, ... , w_n}. 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. Reformulate
Python Knapsack Problem: imagine you are carrying a knapsack with capacity to hold a total of weight C. You are selecting among n items with values A={a_1, a_2, ... , a_n} and associated weights W={w_1, w_2, ... , w_n}. 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.
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Reformulate this as a bottom-up dynamic programming problem as follows. Define K_{i,j} as the highest possible value sum considering items 1 through i and total weight capacity j (j <= C). What is the base case i.e. K_{0,j} for all j and K_{i,0} for all i. What is the loop statement?
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