What is the assumption for making approximations in expressions?

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Discussion Overview

The discussion revolves around the assumptions made when approximating mathematical expressions, particularly in the context of Taylor series and error analysis. Participants explore the conditions under which certain terms can be neglected and the implications of these approximations in both theoretical and applied scenarios.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant suggests that for the approximation y ≈ 1 + ε² to hold, the condition 5ε⁴ ≪ 1 + ε² must be satisfied, particularly if 5ε⁴ ≪ 1.
  • Another participant emphasizes the importance of computing bounds on the error when truncating a series, noting that the signs in the Taylor expansion were initially incorrect.
  • A different viewpoint highlights that when x ≪ 1, the terms in the series decrease in magnitude, allowing the leading order term to dominate the approximation.
  • One participant mentions the use of error propagation techniques, such as the triangle inequality, to analyze how independent variables affect the model.
  • Another participant acknowledges an error in their previous post regarding the signs in the Taylor series expansion and expresses gratitude for the correction.
  • It is noted that there are specific formulas for the remainder term in Taylor series that can help bound the error, referencing external resources for further reading.

Areas of Agreement / Disagreement

Participants express various viewpoints on the assumptions and methods for approximating expressions, indicating that there is no consensus on a singular approach or understanding. Disagreements arise regarding the specifics of error bounds and the implications of truncating series.

Contextual Notes

Limitations include the dependence on the specific values of ε and x, the assumptions about the coefficients in the series, and the unresolved nature of the error bounds discussed. The discussion also reflects varying interpretations of the Taylor series expansion and its application.

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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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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.
 
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.)
 
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.
 
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.
 
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
 

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