Optimal solution of lp problem

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In summary, the conversation discusses finding values of s and t that make the given problem infeasible, unbounded, and have an optimal solution. Examples of infeasible and unbounded solutions are provided, and the question of whether the given statement holds for all x and y is raised.
  • #1
jagbrar
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Equation: maximize f(x,y)=x+y
Subject to
sx+ty<=1
x,y>=0



So the question asks for values of s and t that make the problem infeasible, unbounded, and have an optimal solution. I completed the infeasible with values s=-1 and t=-1. unbounded I got s=2 and t=-4. for the optimal solution I tried using fractions like 1/4 and 1/2 but it does not satisfy sx-ty<=1. Thanks for help!
 
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  • #2
jagbrar said:
Equation: maximize f(x,y)=x+y
Subject to
sx+ty<=1
x,y>=0

So the question asks for values of s and t that make the problem infeasible, unbounded, and have an optimal solution. I completed the infeasible with values s=-1 and t=-1. unbounded I got s=2 and t=-4. for the optimal solution I tried using fractions like 1/4 and 1/2 but it does not satisfy sx-ty<=1. Thanks for help!

You claim that (1/4)*x + (1/2)*y <= 1 is not satisfied. This statement only makes sense if it holds for ALL x and y >= 0. Is that the case?

RGV
 

FAQ: Optimal solution of lp problem

What is an optimal solution in an LP problem?

An optimal solution in an LP (linear programming) problem is a solution that satisfies all of the constraints and maximizes or minimizes the objective function. It is the most efficient solution that meets all of the criteria set by the problem.

How is the optimal solution determined in an LP problem?

The optimal solution is determined by using mathematical techniques, such as the simplex method, to analyze the objective function and constraints of the problem. The goal is to find the optimal values for the decision variables that will maximize or minimize the objective function.

What happens if there is no feasible solution in an LP problem?

If there is no feasible solution in an LP problem, it means that the constraints are too restrictive and there is no combination of values for the decision variables that can meet all of the criteria. In this case, the problem is considered infeasible and cannot be solved.

Can there be multiple optimal solutions in an LP problem?

Yes, there can be multiple optimal solutions in an LP problem. This occurs when there are different combinations of values for the decision variables that result in the same optimal objective function value. In this case, any of the optimal solutions can be chosen as the final solution.

How can the sensitivity analysis be used to evaluate the optimal solution of an LP problem?

Sensitivity analysis is a mathematical technique that can be used to evaluate the impact of changes in the objective function coefficients and constraint values on the optimal solution of an LP problem. It helps to understand how changes in the problem parameters affect the optimal solution and can assist in making more informed decisions.

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