0/1 knapsack problem, generate algorithmic solutions to this problem, with each of the design techniques, of the following algorithms, in Python language: - greedy algorithms - dynamic programming - backtracking - branching and pruning (brach and bound) We have n objects, each with a weight (w_i) and a benefit (v_i), and a backpack in which we can put objects, with a maximum weight capacity M. The objective is to maximize the benefit of the transported objects, where each object can be taken integer (x_i=1) or nothing (x_i=0).

0/1 knapsack problem, generate algorithmic solutions to this problem, with each of the design techniques, of the following algorithms, in Python language: - greedy algorithms - dynamic programming - backtracking - branching and pruning (brach and bound) We have n objects, each with a weight (w_i) and a benefit (v_i), and a backpack in which we can put objects, with a maximum weight capacity M. The objective is to maximize the benefit of the transported objects, where each object can be taken integer (x_i=1) or nothing (x_i=0).

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