Consider the 0-1 knapsack problem with the Capacity C = 8 and 5 items as shown in the table. Find which items will fill the knapsack and have the maximum possible benefit. Use dynamic programming solution . Show all your work item value weight 1 7 18 3 22 2 4 50 3 20 4
Q: 2. Solve the following instance of the 0/1 knapsack problem using dynamic programming approach.…
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Q: Consider the 0-1 knapsack problem with the Capacity C = 8 and 5 items as shown in the table. Find…
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Q: Consider the 0-1 knapsack problem with the Capacity C = 8 and 5 items as shown in the table. Find…
A: #include <bits/stdc++.h>using namespace std; int max(int a, int b) { return (a > b) ? a :…
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Q: 1. Consider the knapsack problem with the capacity C= 8 and 5 items with weights 3, 6, 2, 5, 3. a)…
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A: Weight = 2 and Value = 12 Weight = 1 and Value = 10 Weight = 3 and Value = 20 Weight = 2 and value =…
Q: Q: Apply the dynamic algorithm of Knapsack problem for the following inputs. Knapsack of capacity W…
A: Knapsack Algorithm follows the following steps: Put these items in a knapsack of capacity W, given…
Q: Consider the following instance of 0-1 Knapsack problem: Item Profit Weight 11 9 5 12 6 13 7 4 14 5…
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Q: 1. Consider the knapsack problem with the capacity C= 8 and 5 items with weights 3, 6, 2, 5, 3. a)…
A: The answer given as below:
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Q: Given a knapsack of capacity W = 1.5Kg, solve the following 0/1 knapsack problem using DP.…
A: # Base Case if n == 0 or W == 0: return 0 if (wt[n-1] > W): return knapSack(W, wt,…
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Q: using dynamic programming, which is the optimal value in the co maximum capacity of knapsack is 6…
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A: You can use code like this for a fighting game import random class Fight(object): def enter(self):…
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- The Manhattan Tourist Problem (1) Given setting in Figure 6.4, calculate exact number of different paths by dynamic programming. (2) For a general setting, i.e., from source(0,0) to sink (n, m), how many different paths by dynamic programming?1 4 3 3 4 1 0 4 3 N 5 1In your own words describe the 'knapsack problem'. Further, compare and contrast the use of brute force and a dynamic programming algorithm to solve this problem in terms of the advantage and disadvantages of each. An analysis of the asymptotic complexity of each is required as part of this assignment. Please include your references as this would be checked.Explain the advantages and disadvantages of using a brute force approach to solving the Knapsack problem and how dynamic programing imporves upon this. Written work only
- Q: Apply the dynamic algorithm of Knapsack problem for the following inputs. Knapsack of capacity W = 10 item weight value 1 1 $10 2 2 $40 3 1 $20 4 3 $20 5 5 $60a. Given n items, where each item has a weight and a value, and a knapsack that can carry at most W You are expected to fill in the knapsack with a subset of items in order to maximize the total value without exceeding the weight limit. For instance, if n = 6 and items = {(A, 10, 40), (B, 50, 30), (C, 40, 80), (D, 20, 60), (E, 40, 10), (F, 10, 60)} where each entry is represented as (itemIdi, weighti, valuei). Use greedy algorithm to solve the fractional knapsack problem. b. Given an array of n numbers, write a java or python program to find the k largest numbers using a comparison-based algorithm. We are not interested in the relative order of the k numbers and assuming that (i) k is a small constant (e.g., k = 5) independent of n, and (ii) k is a constant fraction of n (e.g., k = n/4). Provide the Big-Oh characterization of your algorithm.Check Schedulability With LocksData: Periodic program P with locks, WCET estimates Ci for τi and Ci l,k for lock block Bi l,kResult: P schedulable or not; if schedulable, WCRT estimates for each task
- Backtracking search algorithm is guaranteed to compute the global optimum of the respective objectives. True or False with detail Explanation?Hi Can you kindly please give me an example of a Python code that plots the efficient frontier curve of asset classes, whereby for a given range of risk levels (x-axis of efficient frontier), the curve must show the 1) portfolio weights, 2) the portfolio return and 3) Sharpe ratio for a given risk free rate. NB: The code must be in Python (Programming Language).It clarifies dynamic programming optimality?
- Write a python code of this problem Problem Statement Assume that there are two teams and they are team attacker and team defender. Therefore, at a state of the game one agent in each team is left alive respectively. Here, the defender is given a lifeline called HP which will be assigned randomly. Furthermore, the attacker agent will try to give maximum negative HP to the defender agent to decrease his(d) chances of survival in the game. On the other hand, the defender agent will try to protect himself by receiving the lowest negative HP possible from the attacker agent. Furthermore, the attacker can have a choice from a number of bullets from his gun and the optimal moves will cost a certain maximum negative HP (chosen from randomly assigned values within the range of minimum and maximum negative HP). Here, are the following things you need to do using Alpha-Beta Pruning algorithm: Sample Input 1: Enter your student id: 17301106 Minimum and Maximum value for the…Hi , is it possible for someone to insert in this genetic algorithm in matlab equations, values, equations of constraints and values in order to be able to have a code that can adapt to mine please? King regards % Define decision variables, objectives, and constraintsnvars = 5; % number of decision variablesnobj = 8; % number of objectivesncon = 20; % number of constraints % Set up genetic algorithm optionsoptions = optimoptions('ga', ... 'PopulationSize', 100, ... 'MaxGenerations', 100, ... 'MutationFcn', {@mutationuniform, 0.05}, ... 'CrossoverFcn', @crossoverarithmetic); % Define objective and constraint evaluation functionsobjfun = @(x) [obj1(x), obj2(x), obj3(x), obj4(x), -obj5(x), -obj6(x), -obj7(x), -obj8(x)];confun = @(x) deal(con1(x), con2(x), ..., con20(x)); % Run genetic algorithm[x, fval, exitflag, output, population, scores] = ga(objfun, nvars, [], [], [], [], [], [], confun, options); % Plot Pareto frontpareto(fval(:,1:nobj/2),…Not every greedy approach works. For activity selection problem, your book gives examples which shows that the following greedy strategies don't work: the earliest start time, the shortest-duration activity, and the activity with the fewest overlaps. Give concrete examples to show that the 0-1 knapsack problem doesn’t work for: (a) the heaviest item first, (b) the lightest item first, (c) the most expensive item first, and (d) the least expensive item first.