Optimum Values of X and Y: Lagrange Multiplier Help for Maximizing U=XY

In summary, the conversation is about finding the optimum values of X and Y using first order conditions and an auxiliary function. The equations needed to solve for these values are F_x = 0, F_y = 0, and F_\lambda = 0.
  • #1
jack90
1
0
ok this is just an example so you can see where I am having problems with these(it isn't hw)

i need to find the optimum values of X and Y

U= XY

m= Psuby(Y) + Psubx(X)

the first order conditions are

Y +u*Psubx

X+ u*PsubY

m= Psuby(Y) + Psubx(X)


now , where I am having problems is how do i find the optimum values from that. I know you are supposed to plug the FOC in but i can't get the answer my book shows me. could somebody walk me through that part?
 
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  • #2
It's hard to walk you through anything when you haven't stated the problem clearly. You need a function you want to optimize which I assume is U(x,y). Then you need a constraint equation which you haven't stated. Is it P(x,y) = 0? Next you make the auxiliary function:

F(x,y) = U(x,y) + [tex]\lambda[/tex]P(x,y)
using your formulas for U(x,y) and P(x,y).

I have no idea what your m is and what you call your "first order conditions" aren't even equations; they are just expressions. The equations you need to set up and solve are

F_x = 0
F_y = 0
F_[tex]\lambda[/tex] = 0

Three equations in three unknowns.
 

Related to Optimum Values of X and Y: Lagrange Multiplier Help for Maximizing U=XY

What is a Lagrange multiplier and how is it used?

A Lagrange multiplier is a mathematical technique used in optimization problems to find the maximum or minimum value of a function subject to certain constraints. It involves creating a new function called the Lagrangian, which combines the original function with the constraints. By taking the derivatives of the Lagrangian and setting them equal to zero, you can find the values of the variables that will optimize the original function while still satisfying the constraints.

Why is the Lagrange multiplier method useful?

The Lagrange multiplier method is useful because it allows us to find the optimal solution to a problem with constraints, which may not have been possible using traditional optimization methods. It also provides a way to incorporate constraints into the optimization process, rather than treating them as separate entities.

What types of problems can the Lagrange multiplier method be applied to?

The Lagrange multiplier method can be applied to a variety of problems in mathematics, physics, economics, and engineering. It is commonly used in optimization problems involving multiple variables and constraints, such as finding the shortest path between two points with obstacles in the way or maximizing profit while minimizing cost.

What are the limitations of the Lagrange multiplier method?

While the Lagrange multiplier method is a powerful tool for solving optimization problems, it does have some limitations. It may not always produce a unique solution, and it can be computationally expensive for problems with a large number of variables and constraints. Additionally, it may not work for problems with non-linear constraints or functions.

Can the Lagrange multiplier method be extended to handle more complex problems?

Yes, the Lagrange multiplier method can be extended to handle more complex problems by using higher-order derivatives and incorporating additional constraints. It can also be adapted for problems with inequality constraints, known as the KKT conditions. However, these extensions may require more advanced mathematical techniques and may not always produce a closed-form solution.

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