Numerical Integration Scheme: Interpolating Polynomials Explained

In summary, the composite Simpson's rule is only exact for polynomials up to order 3 with an error term proportional to h4. It is unclear if this is the same case for interpolating polynomials. Due to MHB policy, this topic will now be closed. If this question is not a graded assignment, please let me know and I will reopen the topic.
  • #1
f00lishroy
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I am struggling to understand interpolating polynomials and their errors. I have a problem off of a study guide here:
http://terminus.sdsu.edu/SDSU/Math541_f2012/Resources/studyguide-mt01.pdf

View attachment 810

I understand that the composite simpsons rule is only exact for polynomials up to order 3, with error term proportional to h4. Is this the same case? A detailed explanation would be much appreciated, as I mentioned I'm having a hard time grasping this subject. Thank you

-L
 

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  • #2
Hello Lwooley90,

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I am now directed by MHB policy to close this topic. If this question is from a past exam or for any other reason is not actually a graded assignment, please let me know and I will reopen the topic.
 

FAQ: Numerical Integration Scheme: Interpolating Polynomials Explained

What is numerical integration?

Numerical integration is a method used to approximate the value of a definite integral by dividing the interval into smaller subintervals and using numerical methods, such as interpolating polynomials, to calculate the area under a curve.

How does an interpolating polynomial work in numerical integration?

An interpolating polynomial is a polynomial function that passes through a set of given points. In numerical integration, it is used to approximate the curve of a function by connecting the given data points and calculating the area under the curve.

What are the advantages of using interpolating polynomials in numerical integration?

Interpolating polynomials offer a simple and efficient way to approximate the value of a curve in numerical integration. They also allow for a more accurate estimation of the area under the curve compared to other numerical integration methods.

What are some limitations of using interpolating polynomials in numerical integration?

Interpolating polynomials can be sensitive to the choice of data points and can produce inaccurate results if the points are not chosen carefully. Additionally, they may not be suitable for functions with complex curves or rapidly changing slopes.

How can I determine the accuracy of my numerical integration using interpolating polynomials?

The accuracy of numerical integration using interpolating polynomials can be determined by comparing the estimated value with the actual value of the integral. The error can also be reduced by increasing the number of data points or using a higher degree polynomial.

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