Multivariable Optimization Problem

In summary, the conversation discusses the proof of a cube being the box with fixed surface area and maximum volume. The "Lagrange multiplier" method is suggested to solve the problem. The difference between a box and a parallelpiped is also clarified, with a box being characterized by right angles while a parallelpiped is not.
  • #1
StonedPanda
60
0
I have two questions.

A) Show the parallelipided with fixed surface area and maximum volume is a cube.

I've already proven that we can narrow down the proof to a box. So, basically, I'm really lost on how do prove that a cube is the box with a fixed surface area and maximum volume.

B) We might not have covered how to do part B yet, so i'll create a new topic if I still don't understand after tomorrow's lecture.
 
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  • #2
How is a "box" different from a parallelpiped?

Call the lengths of the sides of your parallelpiped x, y, and z.

The volume is V= xyz.

The surface area is 2xy+ 2xz+ 2yz= A (a constant).

Now use the "Lagrange multiplier" method.

In order that V= xyz be a minimum (or maximum!) on the surface U=2xy+2xz+ 2yz- A=0, the two gradient vectors, grad V= <yz, xz, xy> and grad U= <2y+ 2z,2x+ 2z, 2x+ 2y> must be parallel. That is we must have <yz, xz, xy>= some multiple of <2y+2z, 2x+ 2z, 2x+ 2y> so that yz= &lambda;(2y+ 2z), xz= &lambda;(2x+ 2z), and
xy= &lambda;<2x+ 2y>. Eliminate &lambda; from tose equations and see what happens.
 
  • #3
A box is characterized with right angles, whereas a parallellepiped need not be subject to this constraint.
 
  • #4
Ah, right. Thanks.
 

Related to Multivariable Optimization Problem

What is a Multivariable Optimization Problem?

A Multivariable Optimization Problem is a type of mathematical problem that involves finding the optimal value of a function with multiple variables, subject to certain constraints. It is often used to model real-world situations and find the best solution for a given set of variables.

What are some common applications of Multivariable Optimization?

Multivariable Optimization is used in a variety of fields, including engineering, economics, physics, and computer science. Some common applications include optimizing production processes, designing efficient systems, and finding the best investment strategies.

What are the different methods for solving Multivariable Optimization Problems?

There are several methods for solving Multivariable Optimization Problems, including gradient descent, Newton's method, and the simplex method. These methods use different approaches to find the optimal solution, and the choice of method depends on the specific problem and constraints.

How do you determine the optimal solution for a Multivariable Optimization Problem?

To determine the optimal solution for a Multivariable Optimization Problem, you must first define the objective function and any constraints. Then, you can use mathematical methods, such as derivatives and linear programming, to find the critical points and determine the optimal value of the function.

What are some challenges of solving Multivariable Optimization Problems?

Solving Multivariable Optimization Problems can be challenging due to the complexity of the functions and constraints involved. It can also be difficult to find the global optimal solution, as some methods may only find local optimal solutions. In addition, the process of formulating the problem and choosing the appropriate method can be time-consuming and require a strong understanding of mathematics.

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