Why does ##u## need to be small to represent the Taylor expansion

In summary: So, although the function is not continuous at ##x=p##, the Taylor series for ##f## does converge to a value at ##x=p##.
  • #1
Reuben_Leib
6
1
TL;DR Summary
Taylor series
Necessary condition for a curve to provide a weak extremum.
Let ##x(t)## be the extremum curve.
Let ##x=x(t,u) = x(t) + u\eta(t)## be the curve with variation in the neighbourhood of ##(\varepsilon,\varepsilon')##.
Let $$I(u) = \int^b_aL(t,x(t,u),\dot{x}(t,u))dt = \int^b_aL(t,x(t) + u\eta(t),\dot{x}(t) + u\dot{\eta}(t))dt$$
"Taylor’s theorem indicates that, for ##u## sufficiently small, ##I(u)## can be represented by"
$$I(u) = I(0) + u \left(\frac{\textrm{d}I}{\textrm{d}u}\right)_{u=0} + O(u^2)$$
My question is: Why does ##u## need to be small to represent the Taylor expansion of ##I(u)##? doesn't the Taylor series work for any size ##u##?
 
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  • #2
Reuben_Leib said:
My question is: Why does ##u## need to be small to represent the Taylor expansion of ##I(u)##? doesn't the Taylor series work for any size ##u##?
It does, but then you will not be able to ignore "##+O(u^2)##" because the higher order terms will become significant relative to the zeroth and first order terms.
 
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  • #3
If you take the series expansion to infinite order then yes, it will work for any size ##u##. But in this case you are taking only the first term in the expansion, so this will deviate from the original function with errors of order ##O(u^2)##. Those will only be small if ##u## is small.
 
  • #4
Reuben_Leib said:
My question is: Why does ##u## need to be small to represent the Taylor expansion of ##I(u)##? doesn't the Taylor series work for any size ##u##?

Yes, it does.

Where did you read it? Small values of ##u## are the second nature of physicists if they read, write, or hear Taylor. It is usually applied to discover linear approximations, i.e. you need small, neglectable values for ##u^n \;(n>1).##

There is simply not much use of the series for ##u\gg 0.## Furthermore, note that differentiation is a local quality. Differentiable in a neighborhood of ##0## means in a very small radius around ##0.## So we are already limited to a local phenomenon. Why does it make sense to have local behavior in the derivative terms and global in the stretching terms? But yes, formally, there is no restriction for calling the series a Taylor series. However, there is something "small" if we write down the theorem:

Let ##f:I\longrightarrow \mathbb{R}## be a ##n## times continuously differentiable function, and ##p\in I,## Then we have for all ##x\in I##
$$
f(x)=\sum_{k=0}^n \dfrac{f^{(k)}(p)}{k!}\,(x-p)^k + \eta(x)(x-p)^n
$$
where ##\displaystyle{\lim_{x\to p}\eta(x)=0.}##

The estimation for the remainder term converges to zero.
 
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FAQ: Why does ##u## need to be small to represent the Taylor expansion

Why does ##u## need to be small to represent the Taylor expansion?

The Taylor expansion is an approximation of a function around a specific point, typically using a series of its derivatives at that point. For the approximation to be accurate, the variable ##u##, which represents the deviation from the point of expansion, must be small. This ensures that higher-order terms (which involve higher powers of ##u##) contribute less to the total sum, allowing the series to converge more rapidly to the actual function value.

What happens if ##u## is not small in the Taylor expansion?

If ##u## is not small, the higher-order terms in the Taylor series can become significant, potentially leading to a poor approximation of the function. The series may not converge, or it may converge to a value that is far from the actual function value. This reduces the utility of the Taylor series as an approximation tool.

Can the Taylor expansion be used for large values of ##u##?

In general, the Taylor expansion is most effective for small values of ##u##. For larger values of ##u##, other methods such as asymptotic expansions or numerical techniques might be more appropriate. In some cases, a Taylor series can be used for larger ##u## if the series converges quickly, but this is not guaranteed and depends on the function being approximated.

Is there a specific threshold for how small ##u## needs to be?

There is no universal threshold for how small ##u## needs to be, as it depends on the function and the desired accuracy of the approximation. However, in practice, ##u## is often considered small if the higher-order terms (beyond the first few) contribute negligibly to the sum. This can be assessed by examining the magnitude of these terms relative to the leading terms in the series.

How does the choice of ##u## affect the convergence of the Taylor series?

The choice of ##u## directly impacts the convergence of the Taylor series. For small values of ##u##, the series is more likely to converge quickly because the higher-order terms (which involve higher powers of ##u##) diminish rapidly. For larger values of ##u##, these terms do not diminish as quickly, which can slow convergence or even prevent it altogether, leading to a less accurate approximation.

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