Linearization Error: Find Approximate Error of f(x) |x| < 1

In summary: I still don't quite understand. Would you mind elaborating?In summary, you are unsure of how to find the maximum error for a function when given a particular degree of Taylor's polynomial.
  • #1
santais
18
0

Homework Statement



I've been given the assignment to make a linearization of the function f(x) = Sin(x) e^(x) by using Taylors polynominal of 3. degree.

Next is to find the approximately error of f(x) when |x| < 1 (expressed as a function of x).

Homework Equations



So finding the linearization is rather easy to get through, however finding the estimated error is where I just can't figure it out.

The formula that I've been given from my book is as follows:

[itex](f''(s)/2)(x-a)^2[/itex]

for some s lying between a og x. But finding the s value is just something, I have no idea how to figure out. I tried to search some and found out that you could just find the maximum y- value of your function, within that interval, and put it instead of f''(s). Tried that and got a very unexact approximation.

Hope some of you have an idea, how to solve this problem :)
 
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  • #2
santais said:

Homework Statement



I've been given the assignment to make a linearization of the function f(x) = Sin(x) e^(x) by using Taylors polynominal of 3. degree.

Next is to find the approximately error of f(x) when |x| < 1 (expressed as a function of x).

Homework Equations



So finding the linearization is rather easy to get through, however finding the estimated error is where I just can't figure it out.

The formula that I've been given from my book is as follows:

[itex](f''(s)/2)(x-a)^2[/itex]

for some s lying between a og x. But finding the s value is just something, I have no idea how to figure out. I tried to search some and found out that you could just find the maximum y- value of your function, within that interval, and put it instead of f''(s). Tried that and got a very unexact approximation.

Hope some of you have an idea, how to solve this problem :)

You are using the wrong error formula. The formula above is for the error in a _linear_ approximation (i.e., using a polynomial of degree 1). You have been asked to use a polynomial of degree 3 (a cubic approximation), so the error will involve the 4th derivative.

Anyway, you can easily estimate the "maximum possible absolute error" as a function of (x-a), just by estimating max_s{|f''''(s)|, a <= s <= x} and using that instead of |f''''(s)|. However, to do this we need to be able to compute exactly the values of exp(x)*sin(x) at various x. This would be OK if we wanted to do this once and then use the approximate formula thereafter (in other words, do a lot of work off-line in order to validate a formula to be used on-line). An alternative might be to use a cruder estimate that does not involve already knowing how to compute exp(x)*sin(x). Again, though, you would be estimating the maximum possible error, not the actual error itself.

RGV
 
  • #3
Ray Vickson said:
You are using the wrong error formula. The formula above is for the error in a _linear_ approximation (i.e., using a polynomial of degree 1). You have been asked to use a polynomial of degree 3 (a cubic approximation), so the error will involve the 4th derivative.

Anyway, you can easily estimate the "maximum possible absolute error" as a function of (x-a), just by estimating max_s{|f''''(s)|, a <= s <= x} and using that instead of |f''''(s)|. However, to do this we need to be able to compute exactly the values of exp(x)*sin(x) at various x. This would be OK if we wanted to do this once and then use the approximate formula thereafter (in other words, do a lot of work off-line in order to validate a formula to be used on-line). An alternative might be to use a cruder estimate that does not involve already knowing how to compute exp(x)*sin(x). Again, though, you would be estimating the maximum possible error, not the actual error itself.

RGV

Thanks for the answer. And sorry for the late reply.

I'm still quite unclear of what you mean, to be exact. In words, the assigment says that I have to estimate the error for |x| < 1. So I guess that has to be the maximum error, in form of a function.

But then you say, then when I have a 3rd degree Taylor's polynominal, I have to use f^(n+1) = f''''(s). But how exactly to find the maximum value of s within that interval numerical? One thing is that I can see it quite clearly on the graph, but if it happens to be a graph, where you almost have to identical points, with just a slightly difference, then there must be some way to solve it numerical.
 

What is linearization error?

Linearization error is the difference between the actual value of a function and its linear approximation at a given point. It is also known as the approximate error or local error.

How is the linearization error calculated?

The linearization error can be calculated using the following formula: Error = |f(x) - L(x)|, where f(x) is the actual function, L(x) is its linear approximation, and x is the point where the error is being calculated.

What does |x| < 1 mean in the context of linearization error?

In this context, |x| < 1 means that the linear approximation is valid for values of x that are close to 0. This is because the linear approximation is most accurate near the point where it is being calculated.

Why is it important to find the approximate error of a function?

Finding the approximate error allows us to determine how accurate our linear approximation is and how much the actual value of the function differs from it. This can be useful in various fields such as engineering, physics, and economics where linear approximations are commonly used.

How can the linearization error be minimized?

The linearization error can be minimized by choosing a more accurate linear approximation, using a smaller interval around the point of interest, and/or using a higher order approximation such as a quadratic or cubic approximation instead of a linear one.

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