Change of variables when minimizing a function

In summary, the conversation discusses the process of minimizing the function ##f(a) = (1+4a^2)^3 \left( \frac{1}{4a^2} \right)^2## with the given condition that ##a>0##. The speaker suggests using a substitution, ##\alpha = 4a^2##, to simplify the function and make it easier to find the critical point. The derivative is then calculated and equated to zero, leading to the conclusion that the function has a local minimum at ##a = \frac{1}{\sqrt{2}}##. The speaker also justifies the use of the chain rule in this problem.
  • #1
issacnewton
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Homework Statement


I am trying to minimize the function ##f(a) = (1+4a^2)^3 \left( \frac{1}{4a^2} \right)^2##. Here we are given that ##a>0##

Homework Equations


Definition of a minimum of a function

The Attempt at a Solution


Now the derivative here will be ugly and equating it to zero and solving it will be messy. So I did a substitution, ##\alpha = 4a^2##. With this the function becomes ##f(\alpha) = \frac{(1+\alpha)^3}{\alpha^2}##. The derivative is easier to calculate $$f'(\alpha) = \frac{(1+\alpha)^2}{\alpha^2}\left[ 3 - \frac{2(1+\alpha)}{\alpha} \right] $$ Now since ##a>0##, we have ##\alpha >0## and when we equate ##f'(\alpha)## to zero, the equation just becomes $$\left[ 3 - \frac{2(1+\alpha)}{\alpha} \right] = 0$$ which is much easier to solve. ##\alpha = 2##. And using the relationship between ##a## and ##\alpha##, we can see that the function has a critical point when ##a = \frac{1}{\sqrt{2}}##. With either first derivative test or the second derivative test, we can confirm that this is where the function has a local minimum. Now is it ok to do change of variable like I have done here in maxima minima problems. How do I justify this ?

Thanks
##\ddot\smile##
 
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  • #2
You have ##f(a)=g(\alpha)=g(\alpha(a))## and want to know, where ##f'(a)=\frac{d}{da}f(a)=\frac{d}{da}g(\alpha(a))=0##. What does the chain rule tell you?
 
  • #3
Chain Rule tells us that ##\alpha'(a) g'(\alpha) = 0##
 
  • #4
IssacNewton said:
Chain Rule tells us that ##\alpha'(a) g'(\alpha) = 0##
Yes. So the question is whether ##\alpha'(a)## can be zero or not. If not, then ##f'=0## and ##g'=0## are equivalent.
 
  • #5
Here ##\alpha'(a) = 8a \ne 0## , which means that ##g'(\alpha) = 0## and we will solve for ##\alpha## and this will give us the critical value for ##f##. I think this is clear now.
 

What is a change of variables when minimizing a function?

A change of variables is a mathematical technique used to simplify a problem by replacing one set of variables with another. In the context of minimizing a function, it involves substituting the original variables with new ones in order to reduce the complexity of the function and make it easier to find the minimum value.

Why is a change of variables useful when minimizing a function?

A change of variables can help to eliminate unnecessary variables or simplify the form of a function, making it easier to analyze and find the minimum value. It can also allow for the application of other mathematical techniques, such as integration or differentiation, to further simplify the function.

How do you choose the new variables for a change of variables?

The choice of new variables depends on the specific function being minimized and the constraints of the problem. In general, the new variables should be chosen in such a way that the function becomes simpler and easier to work with. This can involve using known relationships between variables, such as trigonometric identities or logarithmic properties.

What are some common mistakes when using a change of variables to minimize a function?

Some common mistakes include choosing inappropriate new variables, incorrectly applying the substitution rule, and not properly accounting for the constraints of the problem. It is also important to check for any potential extraneous solutions that may arise from the change of variables.

Are there any limitations to using a change of variables when minimizing a function?

While a change of variables can be a powerful tool for simplifying a function and finding its minimum value, it may not always be applicable or effective. In some cases, the new variables may not lead to a simpler form of the function, making it difficult to analyze. Additionally, the change of variables may not work for functions with certain types of constraints or in higher dimensions.

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