What is the error bound in cubic spline approximation?

In summary, the conversation discusses finding the error bound for the approximation of a function using a cubic spline. The process involves using Taylor expansion and considering the fourth derivative of the function. The final error bound is given by taking the absolute value of the difference between the function and the spline, with the interval being determined by the nodes and the fourth derivative of the function.
  • #1
Amer
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Find the error bound of approximation of f using the cubic spline

want to find a cubic spline for f on the interval [a,b]
suppose we have n nodes with n-1 different intervals
I tried to find it using the Taylor expansion around any nodes say [tex]x_i \in [a,b][/tex]

[tex] f(x) - S(x) = f(x_i)-S(x_i) + (x-x_i)(f'(x_i)-S'(x_i))+ \frac{(x-x_i)^2(f''(x_i)-S''(x_i))}{2} + \frac{(x-x_i)^3(f'''(x_i)-S'''(x_i))}{3!} + \frac{(x-x_i)^4f^{(4)}(c)}{4!} [/tex]

i considered that the first three terms are zeros so
instead of x i sub [tex]x_{i+1} [/tex] since it is most sutiable x in the subinterval and we shall take the bound of the forth derivative of the function
ending with this
[tex]\mid f(x) - S(x) \mid = \frac{(x_{i+1}-x_i)^4 f^{(4)}(c)}{4!}[/tex]

is it right ?
 
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  • #2
Amer said:
Find the error bound of approximation of f using the cubic spline

want to find a cubic spline for f on the interval [a,b]
suppose we have n nodes with n-1 different intervals
I tried to find it using the Taylor expansion around any nodes say [tex]x_i \in [a,b][/tex]

[tex] f(x) - S(x) = f(x_i)-S(x_i) + (x-x_i)(f'(x_i)-S'(x_i))+ \frac{(x-x_i)^2(f''(x_i)-S''(x_i))}{2} + \frac{(x-x_i)^3(f'''(x_i)-S'''(x_i))}{3!} + \frac{(x-x_i)^4f^{(4)}(c)}{4!} [/tex]

i considered that the first three terms are zeros so
instead of x i sub [tex]x_{i+1} [/tex] since it is most sutiable x in the subinterval and we shall take the bound of the forth derivative of the function
ending with this
[tex]\mid f(x) - S(x) \mid = \frac{(x_{i+1}-x_i)^4 f^{(4)}(c)}{4!}[/tex]

is it right ?

Hi Amer, :)

A more tight error bound for the cubic spline with clamped boundary conditions can be found here.

Kind Regards,
Sudharaka.
 

FAQ: What is the error bound in cubic spline approximation?

What is the error bound in cubic spline interpolation?

The error bound in cubic spline interpolation is a measure of how closely the interpolated curve approximates the original data points. It is the maximum distance between any point on the interpolated curve and the corresponding data point. In other words, it is the maximum deviation from the true function.

How is the error bound calculated for cubic spline interpolation?

The error bound is calculated by using the formula max|f(x) - S(x)|, where f(x) is the original function and S(x) is the interpolated cubic spline function. This formula takes the maximum value of the absolute difference between the original function and the interpolated function at any given point.

What factors affect the error bound in cubic spline interpolation?

There are several factors that can affect the error bound in cubic spline interpolation. These include the number of data points, the spacing between the data points, and the degree of the spline function. Generally, a larger number of data points and a smaller spacing between them will result in a smaller error bound.

Can the error bound in cubic spline interpolation be reduced?

Yes, the error bound in cubic spline interpolation can be reduced by increasing the degree of the spline function or by using a higher order interpolation method. However, these methods may also result in a more complex and computationally intensive interpolation process.

How important is it to consider the error bound in cubic spline interpolation?

The error bound is an important consideration in cubic spline interpolation as it provides a measure of the accuracy of the interpolated curve. It can help determine the suitability of using a cubic spline for a particular set of data and can also guide the selection of interpolation parameters to achieve a desired level of accuracy.

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