Consider the 0/1/2/3 Knapsack Problem. Unlike 0/1 Knapsack problem which restricts xi to be either O or 1, 0/1/2/3 Knapsack Problem allows xi to be either O or 1 or 2 or 3 (that is, we assume that 3 copies of each object i are available, for all i). (a) Obtain the dynamic programming functional equation to solve (find optimal profit) the 0/1/2/3 Knapsack Problem. (b) Give an algorithm to implement your functional equation. (c) What is the complexity of your algorithm?
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- Consider the following linear programming model: Max 2X1 + 3X2 Subject to: X1 ≤ 2 X2 ≤ 3 X1 ≤ 1 X1, X2 ≥ 0 This linear programming model has: a. alternate optimal solutions b. infeasible solution c. redundant constraint d. unbounded solutionNeed help on this problem. Integral Knapsack problem. 1a.by applying the dynamic programming. how many distinct subproblems does this instances have? the original prblem should aslo be included. 1b. what values of an optimal solution for this instance, that is, the total value of most valuable subset of items with total weight <= 5?. Find an optimal solution to the fractional knapsack problem for an instancewith number of items 5, Capacity of the sack W=15, profit associated withthe items (p1,p2,…,p5)= (15,7,6,18,3) and weight associated with each item(w1,w2,…,w7)= (2,3,5,7,4, ).
- In this question you will explore the Traveling Salesperson Problem (TSP). (a) In every instance (i.e., example) of the TSP, we are given n cities, where each pair of cities is connected by a weighted edge that measures the cost of traveling between those two cities. Our goal is to find the optimal TSP tour, minimizing the total cost of a Hamiltonian cycle in G. Although it is NP-complete to solve the TSP, we found a simple 2-approximation by first generating a minimum-weight spanning tree of G and using this output to determine our TSP tour. Prove that our output is guaranteed to be a 2-approximation, provided the Triangle Inequality holds. In other words, if OP T is the total cost of the optimal solution, and AP P is the total cost of our approximate solution, clearly explain why AP P ≤ 2 × OP T.Develop a dynamic programming algorithm for the knapsack problem: given n items of know weights w1, . . . , wn and values v1, . . . ,vn and a knapsack of capacity W, find the most valuable subset of the items that fit into the knapsack. We assume that all the weights and the knapsack’s capacity are positive integers, while the item values are positive real numbers. (This is the 0-1 knapsack problem). Analyze the structure of an optimal solution. Give the recursive solution. Give a solution to this problem by writing pseudo code procedures. Analyze the running time for your algorithms.Please anwer with proper explanation and step by step solution. i will give upvote. Which of the following pairs is the best solution choice of the LP problem: Min u1 + 2u2 subject to 3u1 + u2 ≥ 7 u1 + 4u2 ≥ 6 u1, u2 ≥ 0 1. (u1, u2) = (1, 1) 2. (u1, u2) = (6, 0) 3. (u1, u2) = (1, 4) 4. (u1, u2) = (0, 7) 5. (u1, u2) = (2, 1)
- In regards of the problem:max cTx subject to Ax = b, with an optimal solution of value v. Suppose the problem min cT x, subject to Ax = b have great with the same value, v. It can be concluded that there is a singlegood point for both? How is the feasible region geometrically?Find an optimal solution to the fractional knapsack problem for an Instance with number of items i.e n= 3, Capacity of the sack M=20, profit associated with the items (p1,p2,p3)= (25,24,15) and weight associated with each item (w1,w2,w3)= (18,15,10).please solve step py step no code . Find an optimal solution to the fractional knapsack problem for an instancewith number of items 5, Capacity of the sack W=15, profit associated withthe items (p1,p2,…,p5)= (15,7,6,18,3) and weight associated with each item(w1,w2,…,w7)= (2,3,5,7,4, ).
- 7. The valid questions for Dynamic Programming are 1) Principle of Optimality (Optimal Substructure), and 2) the condition of insufficient Overlapping Subproblems. Take the example of All-Pairs Shortest Paths as an example to illustrate the problem of satisfying these two conditions.Solve the following exercise using jupyter notebook for Python, to find the objective function, variables, constraint matrix and print the graph with the optimal solution. A farm specializes in the production of a special cattle feed, which is a mixture of corn and soybeans. The nutritional composition of these ingredients and their costs are as follows: - Corn contains 0.09 g of protein and 0.02 g of fiber per gram, with a cost of.$0.30 per gram.- Soybeans contain 0.60 g of protein and 0.06 g of fiber per gram, at a cost of $0.90 per gram.0.90 per gram. The dietary needs of the specialty food require a minimum of 30% protein and a maximum of 5% fiber. The farm wishes to determine the optimum ratios of corn and soybeans to produce a feed with minimal costs while maintaining nutritional constraints and ensuring that a minimum of 800 grams of feed is used daily. Restrictions 1. The total amount of feed should be at least 800 grams per day.2. The feed should contain at least 30% protein…Consider the problem of providing change for an amount of n-bahts while using the smallest possible quantity of coins. You may assume that each coin’s value is integer. Assume that the coins available for use have values that can be expressed as powers of c. To clarify, the denominations of the coins can be written as c0,c1,..., ck , where c is an integer greater than 1, and k is a positive integer. Prove that the greedy algorithm always yields an optimal solution.