Error in Series Approximations

In summary, for an alternating series with a real value that converges, the difference between the total sum and the partial sum through the nth term will always be less than or equal to the absolute value of the (n+1)th term. This is because the next term will either overestimate or underestimate the limit, and the error will always be at most the size of the next term.
  • #1
Leo Authersh
It has been defined that for an alternating series, the difference between the total sum of the series and the partial sum of the series through nth term is always less than or equal to the (n+1)th term. Can anyone explain the intuitive reason behind this?
 
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  • #2
It's an interesting "definition" - but there could be exceptions.

For an alternating series of real values that converges, what is most commonly seen is that ending on a "plus" term puts you over the correct value and ending on a "minus" term puts you under the correct value. So you steadily close in on the correct value, but you keep crossing from one side to the other as you add each consecutive term.

Given that scenario, you would know that the next term (N+1) is going to overshoot the correct value - and therefor your current error is less than the absolute value of that term.

For example, consider the series ## \sum_{n=0}^{\infty}V^{n} = 1/{V+1} = V + V^2 + V^3 + ...## where ##0>V>-1##.
For V=-0.5, the full sum ("correct value") is 2/3. The partial sums go: 1, 1/2, 3/4, 5/8, 11/16, 23/32, ... . In each case the next partial sum ends up beyond the correct value.

Ahhh. See mfb's post below for the rest of the story.
 
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  • #3
You need some additional condition for that. ##|a_{n+1}|<|a_n|## is not necessary but sufficient to make that statement true.

Let's assume we have this additional condition, and let's say ##a_n>0## (the proof works exactly the same with flipped signs). Then ##a_{n+1} + a_{n+2}<0## and ##a_{n+3} + a_{n+4}<0## and so on. The sum of all these pairs is negative as well, so the sum of the first n terms overestimates the limit.
At the same time, we know that ##a_{n+2} + a_{n+3}>0## and ##a_{n+4} + a_{n+5}>0## and so on. The sum of the first n+1 terms underestimates the limit. It is ##|a_{n+1}|## smaller than the sum of the first n terms, therefore the error after the nth term is at most the size of the next term.
 
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  • #4
Thank you for the excellent intuitive answers.
 

Related to Error in Series Approximations

What is an error in series approximation?

An error in series approximation refers to the difference between the exact value of a mathematical function and its approximation using a series expansion. It is a measure of how accurate the approximation is and can be positive or negative.

How is error in series approximation calculated?

The error in series approximation can be calculated using the Taylor series formula, which involves taking the difference between the exact value and the approximation at a specific point, and then taking the absolute value of that difference.

What factors can affect the accuracy of a series approximation?

The accuracy of a series approximation can be affected by the order of the series, the number of terms used in the expansion, and the interval of approximation. Additionally, rounding errors in calculations and machine precision can also contribute to the error.

Can error in series approximation be reduced?

Yes, error in series approximation can be reduced by using a higher order series, increasing the number of terms in the expansion, and choosing a smaller interval of approximation. However, it is important to note that the error can never be completely eliminated.

How is series approximation used in real-life applications?

Series approximation is commonly used in fields such as engineering, physics, and finance to approximate complex mathematical functions. It allows for faster and more efficient calculations, but it is important to consider the potential error and its impact on the overall result.

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