Lagraingian constrained optimization problem

In summary, the conversation discusses an optimization problem involving an individual's labor supply and utility. The individual's utility function is described by a function that takes into account consumption and leisure. The individual also has a combined time/income constraint and the government imposes a progressive income tax on all income above $25,000. The discussion also touches on the methods for solving the problem, including using Lagrange multipliers and splitting the problem into two cases based on income.
  • #1
adeel
45
0
Im not sure if this is the right place, but I have an optimization problem where I assume we are supposed to use the Lagraingian method:

Consider the labour supply problem for an individual over an entire year. Suppose the individuals utility is described by the function U = (C^0.5) x (H^0.5). Further, suppose that the individuals combined time/income constraint is given by the equation C + wh = Tw, where T = 8760 is the number of hours in a standard year. Suppose the initial wage rate is $ 10/hour. Suppose that the government imposes a progressive income tax of 10% on all income above $ 25,000. That is, the individual pays no tax on the first $ 25,000 they earn. However, any income above $ 25,000 per year is taxed at a rate of 10%. Given this tax, what are the individuals optimum choices of consumption (C) and leisure (H).

I know how to do the problem without the tax, but i have no idea how to deal with the tax. Any help is greatly appreciated (its going to be on a final i have tomorow, so the faster the better. Thanks.
 
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  • #2
any ideas? anyone?
 
  • #3
It might help if you would tell us what your variables, C, H, wh (or is that c*h?) mean! I can see no relationship between U and the tax!
 
  • #4
I agree with Hall's, the variables involved are not very clearly explained and the mixed case (H vs h etc) is confusing.

Anyway, I assume the consumption contraint, c = (T-h)w, is just for the non-taxed case. That is where w is a constant of $10.00 per hour.

This case is very easily solved, no need to use Lagrange multiplers (though you can if you wish). You just need to subst the constraint into the functional to make it a simple function of one variable "h" and the find the maxima.

If you do this it will tell you that you have to work 12hrs per day, 7 days per week and 365 days per year just to be maximally happy, Rats! Thank God for taxation however (j/k) because when you repeat the problem with taxation included then at least it tells you to work a little bit less.

To handle the case with taxation it's best to split the problem into two regions (case 1 for annual income <= 25000 and case two for annual income > 25000). Remember that if a local maxima for a given case does not fall within the required region for that case then the actual maximum will occur at the boundary.

For case 2 (income>25000) use the modified constraint of c = 2500 + 9(T-h) ok.
 

1. What is a Lagrangian constrained optimization problem?

A Lagrangian constrained optimization problem is a type of mathematical optimization problem that involves maximizing or minimizing a function, subject to constraints that can be expressed as equations using Lagrange multipliers. This approach allows for the incorporation of constraints into the optimization process, making it more flexible and applicable to a wider range of problems.

2. How is a Lagrangian constrained optimization problem solved?

The Lagrangian constrained optimization problem is typically solved using the method of Lagrange multipliers. This involves converting the original problem into a system of equations, known as the Lagrangian, and then solving for the optimal values using techniques such as differentiation and substitution.

3. What are the applications of Lagrangian constrained optimization?

Lagrangian constrained optimization has a wide range of applications in various fields such as economics, engineering, physics, and computer science. It can be used to solve problems involving resource allocation, portfolio optimization, optimal control, and many others.

4. What are the advantages of using Lagrangian constrained optimization?

One of the main advantages of Lagrangian constrained optimization is its ability to handle complex constraints in the optimization process. It also allows for the use of multiple constraints and can handle non-linear functions. Additionally, the Lagrangian approach provides a systematic way to incorporate constraints into the optimization problem.

5. Are there any limitations to Lagrangian constrained optimization?

While Lagrangian constrained optimization is a powerful tool for solving optimization problems, it does have some limitations. In some cases, the method may not provide a unique solution, and there may be multiple optimal points. It can also be computationally expensive for certain types of problems. However, these limitations can often be overcome by using alternative approaches or techniques.

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