What is the assumption for making approximations in expressions?

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In summary, when making approximations, it is important to consider the specific assumptions being made and to compute a bound on the error that arises. In the case of a Taylor series, there are formulas for the remainder term that can be used to bound the error. It is also helpful to use norm and metric identities, such as the triangle inequality, to get a bound on the error and convert the variables separately. Additionally, when dealing with a series that alternates in sign, it is often possible to improve the bound by combining pairs of consecutive terms.
  • #1
weetabixharry
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If I have some expression such as: [tex]y = 1 + \epsilon^2 - 5\epsilon^4[/tex] and then make this approximation:[tex]y \approx 1 + \epsilon^2[/tex] then, if I understand correctly, my specific assumption is that [itex]5\epsilon^4 \ll 1 + \epsilon^2[/itex] which, for example, would always be satisified if I happened to know that [itex]5\epsilon^4 \ll 1[/itex].
However, if I have something like a Taylor series, I'm not sure exactly what my assumption is. For example:[tex]\sqrt{1 - x} = 1 - \frac{1}{2}x - \frac{1}{8}x^2 - \frac{1}{16}x^3 - \frac{5}{128}x^4 - \dots \\ \ \ \ \ \ \ \ \ \ \ \ \ \approx 1 - \frac{1}{2}x[/tex] It seems that my exact assumption here involves an infinite series that might be tricky to evaluate.
So, more loosely speaking, is the approximation satisfied, for example, if [itex]\frac{1}{8}x^2 \ll 1[/itex]? Is there a sensible/rigorous way of dealing with things like this?
 
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  • #2
It is certainly a good idea to compute a bound on the error that arises by curtailing the expansion.
In the example you give, you have the signs wrong in the expansion. All except the first term should be negative. The general term has magnitude (x/4)r 2rCr. Asymptotically, that approximates xr/√r, which is clearly less than xr. If you curtail the expansion at r = n, the sum of the remaining terms cannot exceed xn/(1-x).
Faced with a series which does alternate in sign, you can usually do better than this by combining pairs of consecutive terms and putting a bound on those.
 
  • #3
Well when [itex] x \ll 1 [/itex], [itex] x^2 \gg x^4 \gg x^6 \cdots [/itex], So each next term in the series is much smaller than the one which preceded it.

And since [itex] x \ll 1 [/itex] you know that the 1 out front is important, so you simply keep the leading order term in x, namely the linear one.

(This does assume something about the coefficients, however. But from Taylor we know that they're decreasing as well so this specific example is OK. If the coefficients were, for some reason, increasing, then a more detailed analysis would be in order.)
 
  • #4
Typically what happens is that you have the independent variables in the form of x + epsilon and then you expand it out in the context of the model to see how the error propagates in the model.

One common and powerful way to do this is to use the norm and metric identities like the triangle inequality since you can get a bound on on the error and convert the x + epsilon in terms of x and epsilon separately.
 
  • #5
haruspex said:
In the example you give, you have the signs wrong in the expansion.
You're right - thanks for spotting my error. I'll edit the OP to avoid confusion.

haruspex said:
If you curtail the expansion at r = n, the sum of the remaining terms cannot exceed xn/(1-x).
I'll have to have a think about this, but I expect this is exactly what I'm looking for.

haruspex said:
Faced with a series which does alternate in sign, you can usually do better than this by combining pairs of consecutive terms and putting a bound on those.
Yes - this makes intuitive sense. Many thanks for your clear explanations.
 
  • #6
In the specific case of Taylor series, there are formulas for the remainder term which can be used to bound the error. See the section titled "Explicit formulae for the remainder" in

http://en.wikipedia.org/wiki/Taylor's_theorem
 

FAQ: What is the assumption for making approximations in expressions?

1. How do I determine the accuracy of an approximation?

The accuracy of an approximation can be determined by comparing it to the exact value or by calculating the percentage error. The closer the approximation is to the exact value, the more accurate it is.

2. Can approximations be used in scientific calculations?

Yes, approximations are commonly used in scientific calculations as they can simplify complex equations and make them more manageable. However, it is important to consider the level of accuracy needed for the specific calculation.

3. What are the different types of approximations?

There are two main types of approximations: numerical approximations and analytical approximations. Numerical approximations involve using numerical methods, such as interpolation or extrapolation, to estimate a value. Analytical approximations involve using mathematical techniques, such as Taylor series or asymptotic expansions, to approximate a function.

4. How can I improve the accuracy of an approximation?

The accuracy of an approximation can be improved by using more sophisticated techniques, such as higher-order approximations or numerical methods with smaller step sizes. It is also important to consider the sources of error and minimize them as much as possible.

5. Are there any limitations to using approximations?

Yes, there are some limitations to using approximations. They may not be suitable for all types of calculations, especially those that require a high level of accuracy. Additionally, the accuracy of an approximation is dependent on the assumptions and simplifications made, so it is important to carefully consider their validity in the context of the problem at hand.

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