Maximizing Non-Linear Functions with Lagrange Multipliers

In summary, the person is working on a paper for a class and has run into a block. They ask for help and explain that they are trying to maximize a function that takes three variables (x, W, and H). The person asks for help with understanding the expressions and how to take derivatives. They mention that they wrote a brute force algorithm to try all possible values.
  • #1
FrogPad
810
0
I am working on a paper for a class, and I've come to somewhat of a block. I'll keep the question general.

If I have three non-linear real valued functions,

(1) [tex] f_1(x) [/tex]
(2) [tex] f_2(x,W,H) [/tex]
(3) [tex] f_3(x,W,H) [/tex]

that form a function:

[tex] F(x,W,H) = f_1 + f_2 + f_3 [/tex]

How would I maximize [tex] F(x,W,H) [/tex].

Lagrange multipliers are ringing a bell... but before I get too invested in an idea, I would like the proper road to travel down. So If someone could point me in the right direction that would be awesome.
 
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  • #2
You're just minimizing the sum of the functions. Take partial derivatives of the sum wrt to x, W and H and set them all to zero.
 
  • #3
Are f1,f2,f3 convex?
 
  • #4
ARE W and H variables or are they constants? I see no reason to use "Lagrange multipliers" because you have no constraints.
 
  • #5
LeBrad said:
Are f1,f2,f3 convex?

I am not actually sure. They are mixed with a lot of different terms (sinh, cosh, ...), so it is hard (for me at least) to get an idea of what they look like.
 
  • #6
HallsofIvy said:
ARE W and H variables or are they constants? I see no reason to use "Lagrange multipliers" because you have no constraints.

I've recently found out that W corresponds to some constants in the functions, so I can no longer vary it.

So I will have the following:

f(x,W) = const = f1(x)+f2(x,H)+f3(x,H)

x and W both represent the length of a device. I am trying to find lengths that that maximize f(x,W), which actually represent a current.
 
  • #7
Thanks for the help everyone.

The expressions were too complicated to take derivatives of and solve in such a way (too time consuming at least). I ended up writing a brute force algorithm to try all possible values (from a pool of "intelligent" guesses) to maximize the function.

I appreciate the help.
 

1. What is the meaning of "Maximizing a set of functions"?

Maximizing a set of functions refers to finding the highest possible values for a group of related mathematical equations. This can be achieved by finding the optimal inputs for each function that result in the largest overall output.

2. How is "Maximizing a set of functions" different from "optimizing a single function"?

Maximizing a set of functions involves finding the highest values for multiple related equations, while optimizing a single function involves finding the highest value for a single equation. Maximizing a set of functions requires considering the relationships and dependencies between the equations, while optimizing a single function focuses on finding the best input for one specific equation.

3. What are some common strategies for maximizing a set of functions?

Some common strategies for maximizing a set of functions include using mathematical techniques such as gradient descent, linear programming, or convex optimization. Other approaches may involve using heuristics, trial and error, or machine learning algorithms to find optimal solutions.

4. In what fields or industries is maximizing a set of functions commonly used?

Maximizing a set of functions is commonly used in fields such as economics, finance, engineering, and computer science. It can be applied to various real-world problems, such as maximizing profits in business, optimizing resources in manufacturing, or finding the best parameters for a machine learning model.

5. Are there any limitations or challenges when maximizing a set of functions?

Yes, there are several limitations and challenges when maximizing a set of functions. One major limitation is the complexity and computational cost of solving for the optimal inputs for multiple equations simultaneously. Additionally, the relationships between the functions may not always be well-defined or easily quantifiable, making it difficult to find the global maximum for the entire set of functions.

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