Approximating different functions

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In summary, the conversation discusses the approximation of two functions with same domain but different codomains, given a small difference in the input values. The example provided shows that this approximation cannot be generalized, as demonstrated by a specific counterexample.
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kent davidge
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I have a question regarding different functions. Suppose we have two functions ##f## and ##f'## with same domain, but different codomains.

Consider that ##f': x' \mapsto f'(x')## and ##f: x \mapsto f(x)##. If ##x' = x + \sigma##, with ##|\sigma| << 1##, can we say that ##f'(x') \approx f(x')##?
 
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No. Not in this generality. Let's say ##f(x)=x^{-2}\; , \;f\,'(x)=x^{-1}##. Then we have ##x'=-{10}^{-n}=x+\sigma = {10}^{-n}-2\cdot {10}^{-n}## and ##|\sigma|=|2\cdot {10}^{-n}| << 1## but ##-{10}^{n} =f\,'(x') \not \approx {10}^{2n}=f(x')##.
 
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kent davidge said:
I have a question regarding different functions. Suppose we have two functions ##f## and ##f'## with same domain, but different codomains.

Consider that ##f': x' \mapsto f'(x')## and ##f: x \mapsto f(x)##. If ##x' = x + \sigma##, with ##|\sigma| << 1##, can we say that ##f'(x') \approx f(x')##?
If you have two different functions, why not call them f and g? Or ##f_1## and ##f_2##? Your notation ##f'(x')## looks like we're evaluating the derivative of f at a number x'.
 
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Mark44 said:
If you have two different functions, why not call them f and g? Or ##f_1## and ##f_2##? Your notation ##f'(x')## looks like we're evaluating the derivative of f at a number x'.
Yea, I agree. Coincidentally I was going to write them down as ##f## and ##g## in the opening post, but I used the more abstract notation.
 

FAQ: Approximating different functions

1. How do you approximate a function?

To approximate a function, you can use techniques such as linear interpolation, polynomial interpolation, or regression analysis. These methods involve finding a mathematical equation that best fits the data points of the function, allowing for more accurate approximations.

2. Why is it important to approximate functions?

Approximating functions allows us to simplify complex mathematical equations and make predictions based on limited data. It is also useful in fields such as engineering and physics, where precise calculations are necessary for practical applications.

3. What are the limitations of approximating functions?

One limitation of approximating functions is that they may not accurately represent the behavior of the function outside of the given data points. Additionally, the chosen approximation method may introduce some error or bias into the results.

4. Can you approximate any type of function?

In theory, any function can be approximated. However, the accuracy of the approximation may vary depending on the complexity of the function and the chosen approximation method. Some functions may also require more advanced techniques to achieve accurate approximations.

5. How do you determine the best approximation method for a function?

The best approximation method for a function depends on various factors such as the type of function, the available data, and the desired level of accuracy. It is important to consider these factors and compare the results of different methods to determine the most suitable one for the given function.

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