Truncation Error and its Bounds

In summary, truncation error is a type of error that occurs in numerical computations when an approximation is used instead of the exact value. It is caused by rounding or truncating numbers and can be calculated by finding the difference between the exact and approximate values. To reduce truncation error, more precise methods and data types can be used. The bounds of truncation error can vary depending on the precision and numerical method used, but can generally be decreased by using more precise methods and smaller intervals in calculations.
  • #1
theuniverse
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Homework Statement


a) Derive a Taylor series with n+1 terms and the associated truncation error for
the function [tex]f(x)=(log(x)-x+1)/(x-1)^2[/tex]
b) Construct an expression that bounds the truncation error, assuming n > 2, for a given value of x

2. The attempt at a solution
a) So I came up with the following series: (from k=2 to k=n+2) [tex]Ʃ((-1)^k*(x-1)^{k-1})/(k+1)[/tex]

But I'm not really sure how to derive the truncation error. Is the error simply in the n+2 term, and all I have to do is just sub it into the Lagrange remainder equation?

b) not sure on what interval I should be doing it. As well as taking taking n+2 derivatives to figure out the bound doesn't seem right...

Thanks for your help,
 
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  • #2

a) To derive the truncation error, we can use the Lagrange remainder formula for Taylor series. This formula states that the truncation error, denoted by R_n(x), can be written as R_n(x) = f^(n+1)(c)(x-a)^(n+1)/(n+1)! for some c between a and x. In this case, our function f(x) = (log(x)-x+1)/(x-1)^2 has a singularity at x=1, so we will choose a = 1. Then, we need to find the (n+1)th derivative of f(x). This can be done by using the quotient rule repeatedly, or by using the general formula for the derivative of a quotient function. After some simplification, we get:

f^(n+1)(x) = (-1)^n*(n+1)!*(x-1)/x^(n+2)

Substituting this into the Lagrange remainder formula, we get:

R_n(x) = (-1)^n*(x-1)^(n+1)/(n+2)

b) To find an expression that bounds the truncation error, we can use the fact that for any n > 2, the (n+2)th derivative of f(x) will be a decreasing function for x > 1. This means that the maximum value of the (n+2)th derivative will occur at x = 1. Therefore, we can bound the truncation error as follows:

|R_n(x)| ≤ |f^(n+1)(1)|*|x-1|^(n+1)/(n+1)!

Substituting in our expression for f^(n+1)(x) from part (a), we get:

|R_n(x)| ≤ (n+1)!*|x-1|^(n+1)/(n+2)^(n+2)

This gives us an expression that bounds the truncation error for any given value of x > 1.
 

FAQ: Truncation Error and its Bounds

What is truncation error?

Truncation error is the amount of error introduced in a numerical computation due to rounding or truncating numbers. It is a type of error that occurs when an approximation is used instead of the exact value.

What are the sources of truncation error?

The two main sources of truncation error are rounding and truncation. Rounding occurs when numbers are rounded to a specific number of decimal places, while truncation occurs when numbers are cut off after a certain number of digits.

How is truncation error calculated?

Truncation error can be calculated by finding the difference between the exact value and the approximate value. The absolute value of this difference is the truncation error.

How can truncation error be reduced?

Truncation error can be reduced by using more precise methods of calculation, such as increasing the number of decimal places used in calculations or using higher precision data types. Additionally, using smaller intervals in numerical methods can also help reduce truncation error.

What are the bounds of truncation error?

The bounds of truncation error depend on the precision used in calculations and the type of numerical method being used. In general, the bounds of truncation error can be decreased by using more precise methods and data types, as well as decreasing the size of intervals used in calculations.

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