Constrained Maximization

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In summary, the problem is to find the dimensions of the largest volume package that a particular parcel service will accept. The objective function is the volume of the package, and the constraints are the length and girth restrictions. The attempt at a solution shown here is incorrect and would not lead to a successful solution.
  • #1
MathNoob123
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Homework Statement


A particular parcel sercive will accept only packages with length no more than 128 inches and length plus girth(xz)(width times height) no more than 145 inches.

What are the dimensions of the largest volume package the parcel service will accept?

This is the problem, but I just need to figure/know the objective equation and subject equation.
Do i use 2zx+2xy+2yz as my objective function and for my subject function would it be something like zx-17=0?

NOTICE: THIS IS A RECTANGULAR BOX

Homework Equations



Using Lagrange Multipliers
A=xy+xz+yz

The Attempt at a Solution



considering that i know that the area function is 2zx+2xy+2yz
Do i use 2zx+2xy+2yz as my objective function and for my subject function would it be something like zx-17=0?
 
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  • #2
hi mathnoob123

I'm not sure where you got that equation from

what is the volume of a package? this is what you're trying to maximise...

then the constraints are as given, you can have more than one

what is girth? are you sure is just not width as it is given in inches not a areal unit...
also you may have to be careful about x>y>z in defining length, width etc.
 
  • #3
I have successfully solved this problem. Thank you to all my fans. I am slowly, but surely becoming a powerful mathmatician. Muuuuuuhahahahaahhahahha!
 
  • #4
MathNoob123 said:

Homework Statement


A particular parcel sercive will accept only packages with length no more than 128 inches and length plus girth(xz)(width times height) no more than 145 inches.
The words and the expressions don't match here. The girth is how far around the package is. If the longest dimension of the package is z, then the girth would be 2x + 2y.
MathNoob123 said:
What are the dimensions of the largest volume package the parcel service will accept?

This is the problem, but I just need to figure/know the objective equation and subject equation.
Do i use 2zx+2xy+2yz as my objective function and for my subject function would it be something like zx-17=0?
No and no. What you show for your objective function--2zx+2xy+2yz-- is just the area of the 6 sides of the package. And I have no idea where zx - 17 = 0 comes from.
MathNoob123 said:
NOTICE: THIS IS A RECTANGULAR BOX

Homework Equations



Using Lagrange Multipliers
A=xy+xz+yz

The Attempt at a Solution



considering that i know that the area function is 2zx+2xy+2yz
Do i use 2zx+2xy+2yz as my objective function and for my subject function would it be something like zx-17=0?

What you want to do is maximize the volume of the package (xyz) subject to the length restraint and the girth restraint.

I'm glad you were able to solve this problem successfully. However, the work shown here would not lead to a successful conclusion, as far as I can see.
 

1. What is constrained maximization?

Constrained maximization is a mathematical optimization problem where the objective function (usually profit or utility) is maximized subject to a set of constraints. These constraints limit the feasible values of the decision variables.

2. How is constrained maximization different from unconstrained maximization?

In unconstrained maximization, there are no restrictions on the values of the decision variables, whereas in constrained maximization, there are certain constraints that limit the feasible values. This makes constrained maximization a more complex and challenging problem to solve.

3. What are some common techniques used for constrained maximization?

Some common techniques used for constrained maximization include Lagrange multipliers, Kuhn-Tucker conditions, and interior point methods. These techniques involve manipulating the objective function and constraints to find the optimal solution.

4. What are some real-world applications of constrained maximization?

Constrained maximization is used in various fields such as economics, engineering, and finance. It can be used to maximize profits in a business, optimize the design of a structure, or find the optimal portfolio for an investor.

5. What are the challenges of solving a constrained maximization problem?

Solving a constrained maximization problem can be challenging due to the complex nature of the problem. It may involve multiple constraints and decision variables, and finding the optimal solution may require iterative calculations and trial and error. Additionally, the optimal solution may not always be guaranteed to be the global maximum.

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