Generate algorithmic solutions to the problems, with each of the following algorithm design techniques, in the Python language. - Greedy Algorithms - Dynamic Programming - Backtracking - Branch and Bound Calculate and analyze the complexity t(n) of each algorithm. Perform average comparison (minimum 3 executions with the same data pool) of time and memory consumption for each technique and algorithm. Description of the problem: 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).

Generate algorithmic solutions to the problems, with each of the following algorithm design techniques, in the Python language. - Greedy Algorithms - Dynamic Programming - Backtracking - Branch and Bound Calculate and analyze the complexity t(n) of each algorithm. Perform average comparison (minimum 3 executions with the same data pool) of time and memory consumption for each technique and algorithm. Description of the problem: 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).