Convert the following optimization problems to a Linear Program. Write the equivalent LP formulation. If a problem cannot be reformulated as an LP, identify the constraint(s) and/or Objective that creates issues. (a) min max{3 х+ у, 4x-у, 2х+3 у} s.t. Constraint 1: x/(x+y)< 0.2 Constraint 2: 5 x+15 y=3.2 Constraint 3: x+y> 0 x free, y free (b) min |5x+8y | s.t. Constraint 1: |7x+5y| < 8 x> 0, y free
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- 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.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 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 assigmentArc consistency in constrained satisfaction problems Suppose that we have three variables X1, X2 and X3, which are defined on the same domain of {1, 2, 3}. Two binary constraints for these three variables are defined according to the following: 1. Is X1 arc-consistent with respect to X2? And is X1 arc-consistent with respect to X3? and why? In addition, is X3 arc-consistent with respect X1 if the constraints between R1 and R3 are undirected (i.e., R31 is defined as {(X3, X1), [(2, 1),(1, 2),(1, 3),(3, 3)]} that switches the element order of every two-tuple of R13)? and why? 2. Suppose that, after some inference, the domain of X1 is reduced as {2, 3} and the constrains in R12 and R13 for X1 = 1 are removed accordingly. To be more specific, (1, 2) is removed from R13 due to reducing the domain of X1. Now is X1 still arc-consistent with respect to X2? And is X1 arc-consistent with respect to X3? and why? In addition, is X3 still arc-consistent with respect X1 if the constraints between…
- Consider the following linear programming problem: Min A + 2B s.t. A + 4B ≤ 21 2A + B ≥ 7 3A + 1.5B ≤ 21 -2A + 6B ≥ 0 A, B ≥ 0 Determine the amount of slack or surplus for each constraint. If required, round your answers to one decimal place. (1) A + 4B ≤ 21 Slack or surplus (2) 2A + B ≥ 7 Slack or surplus (3) 3A + 1.5B ≤ 21 Slack or surplus (4) -2A + 6B ≥ 0 Slack or surplusIf the optimal solution of a linear programming problem with two constraints is x=5, y=0, s1=3 and s2=0 , then the basic variables are ____.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…
- 8. Which of the following statements about linear programming is FALSE? Select one: a. If the change of the objective coefficient is out of the range of optimality, the optimal solution in terms of decision variables will usually change. b. A redundant constraint is also a non-binding constraint. c. In sensitivity analysis of LP problem, if the resource has been used up, the shadow price would be non-zero. d. A difference between minimization and maximization problems is that minimization problems often have unbounded regions. e. If the RHS of a constraint changes in a profit maximization problem, the iso-profit line will change as well.(a) Model this problem as an Integer Program. (b) Ignoring any integer constraints, derive the dual of the IP you found in part (a). Hint: rememberthat a binary constraint decomposes into ≥ 0, ≤ 1, and Integer.If it is possible to create an optimal solution for a problem by constructing optimal solutions for its subproblems, then the problem possesses the corresponding property. a) Subproblems which overlap b) Optimal substructure c) Memorization d) Greedy
- Bootstrapping procedure to achieve initial feasibility of a convex constrained region. INPUTS: NotSat: the set of inequality constraints having convex region effectsBuiding 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≤tsShow how a single ternary constraint such as “A + B = C” can be turned into three binaryconstraints by using an auxiliary variable. You may assume finite domains. (Hint: Consider a newvariable that takes on values that are pairs of other values, and consider constraints such as “X is thefirst element of the pair Y .”) Next, show how constraints with more than three variables can betreated similarly. Finally, show how unary constraints can be eliminated by altering the domains ofvariables. This completes the demonstration that any CSP can be transformed into a CSP with onlybinary constraints.