Consider the algorithm SeldeLP. Construct an example to show that the optimum of the linear program defined by the constraints in B(H\h) u {h} may be different from the optimum of the linear program defined by H. Thus, if the test in Step 2.1 fails and we proceed to Step 2.2, it does not suffice to consider the constraints in B(H\h) u {h} alone.
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- Consider the SeldeLP algorithm. Create a scenario to illustrate how the linear program's optimum as defined by B(Hh) u h may vary from the linear program's optimum as defined by H.This characteristic is met by a problem if it is feasible to develop an optimum solution for it by first creating optimal solutions for its subproblems. a) Subproblems that overlap; b) substructure that's optimum; c) memorization; d) greedyHi , 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 MAIN MATLAB CODE % Define the objective functions to maximize and minimize f = @(x) [x(1)^2, x(2)^2, -x(3)^2, -x(4)^2]; g = @(x) [-x(1)^2, -x(2)^2, x(3)^2, x(4)^2]; % Define the constraints A = [-1 0 0 1; 0 -1 0 1; 0 0 -1 1; 1 1 1 1]; b = [-1; -1; -1; 1]; % Set the GA options options = optimoptions('gamultiobj', 'Display', 'iter', 'PlotFcn', {@gaplotpareto, @gaplotscorediversity}); % Run the GA [x, fval] = gamultiobj(@(x) [f(x), g(x)], 4, A, b, [], [], [], [], options); % Plot the Pareto front in 3D scatter3(fval(:,1), fval(:,2), fval(:,3), 20, 'filled'); xlabel('f_1(x)'); ylabel('f_2(x)'); zlabel('f_3(x)'); title('Pareto Front'); grid on; NOTE this is an personal project , not an assigment
- Using c++ Apply both breadth-first search and best-first search to a modified version of MC problem. In the modified MC, a state can contain any number of M’s and any number of C’s on either side of the river. Assume the goal is always to move all the persons on the left side to the right side. The Initial state should be a parameter given to the program at beginning of execution. As in the original problem, boat capacity =2, the boat cannot move by itself, and on either side C’s should not outnumber M’s. For best-first search, you need to come up with an appropriate heuristic. In addition to solving the problem, your grade will also be based on th effectiveness of the heuristic. As an example, the program should execute as follows. Initial state… Enter number of M’s on left side of the river: 3 Enter number of C’s on left side of the river: 1 Enter number of M’s on right side of the river: 0 Enter number of C’s on right side of the river: 0 Enter location of the boat: L The output…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 answer the following question in detail. With all the proofs and assumptions explained. We have seen various search strategies in class, and analyzed their worst-case running time. Prove that any deterministic search algorithm will, in the worst case, search the entire state space. More formally, we define a search problem by a finite set of states S, a set of actions A, and a cost function c : S ×A×S → {1,∞} (i.e. the cost is uniform, but some states cannot be reached from some others, or equivalently have a cost of ∞), and a starting state s0 ∈ S. We assume that every state s ∈ S is reachable from s0, i.e. that there is a sequence of actions one can take from state s0 such that one reaches the state s after performing this sequence of actions, and such that the cost of reaching s is finite. Prove the following theorem. Theorem1. LetAlgbesomecomplete, deterministic uninformed search algorithm. Then for any search problem defined as above, there exists some choice of a state…
- Any linear program L, given in standard form, either1. has an optimal solution with a finite objective value,2. is infeasible, or3. is unbounded.If L is infeasible, SIMPLEX returns “infeasible.” If L is unbounded, SIMPLEXreturns “unbounded.” Otherwise, SIMPLEX returns an optimal solution with a finiteobjective value.Please don't use handwritting for this question How would you modify the dynamic programming algorithm for the coin collecting problem if some cells on the board are inaccessible for the robot? Apply your algorithm to the board below, where the inaccessible cells are shown by X’s. How many optimal paths are there for this board? You need to provide 1) a modified recurrence relation, 2) a pseudo code description of the algorithm, and 3) a table that stores solutions to the subproblems.How would you modify the dynamic programming algorithm for the coin collecting problem if some cells on the board are inaccessible for the robot? Apply your algorithm to the board below, where the inaccessible cells are shown by X’s. How many optimal paths are there for this board? You need to provide 1) a modified recurrence relation, 2) a pseudo code description of the algorithm, and 3) a table that stores solutions to the subproblems.
- Buiding an optimization model for the building a house problem, and solve it using JuMP and Clp.Example: building a house A small sample: Let t1,t0,tm,tn,tt,ts be start times of the associated tasks. Now use the graph to write the dependency constraints: Tasks 0,m, and n can't start until task I is finished, and task I takes 3 days to finish. So the constraints are: t1+3≤3≤t0,t1+3≤tm,t1+3≤tn Task t can't start until tasks m and n are finished. Therefore: tm+1≤tt,tn+2≤tt,takes 3 days to finish. So the constraints are: Task s can't start until tasks 0 and t are finished. Therefore: t0+3≤ts,tt+3≤tsConsider a best first search (BFS) algorithm that tries to find the optimal goal state with minimal cost. Consider heuristics h1, h2 with h1(n) > h2(n) for all states n. BFS with h1 is guaranteed to expand fewer nodes or an equal number of nodes to arrive at the optimal goal state than BFS with h2 Select one: True FalseConsider a best first search (BFS) algorithm that tries to find the optimal goal state with minimal cost. Consider heuristics h1, h2 with h1(n) > h2(n) for all states n. BFS with h1 is guaranteed to expand fewer nodes or an equal number of nodes to arrive at the optimal goal state than BFS with h2 Select one: True False The rational agent always perform the optimal action Select one: True False Fuzzy logic is useful for both commercial and practical purposes. Select one: True False