Estimating Error for an Infinite Series (Mclaurin)

In summary, the problem discusses the difficulty of finding the sum of an infinite series and presents a simple alternative for a series of positive decreasing terms. It asks to show that the sum of the rest of the series is between two integrals. The attempt at a solution raises concerns about integrating a sequence that contains n!. However, the main issue is understanding why the inequality between the sum and the integrals holds, which can be seen through the figures provided in the problem.
  • #1
kq6up
368
13

Homework Statement



Problem # 30 in Ch1 Section 16 in Mary L. Boas' Math Methods in the Physical Sciences

It is clear that you (or your computer) can’t find the sum of an infinite series
just by adding up the terms one by one. For example, to get [tex]\zeta (1.1)=\sum _{ n=1 }^{ \infty }{ \frac { 1 }{ { n }^{ 1.1 } } } [/tex] (see Problem 15.22) with error < 0.005 takes about [tex]10^{33}[/tex] terms.
To see a simple alternative (for a series of positive decreasing terms) look at
Figures 6.1 and 6.2. Show that when you have summed N terms, the sum RN
of the rest of the series is between [tex]{ I }_{ N }=\int _{ N }^{ \infty }{ { a }_{ n } } dn\quad and\quad { I }_{ N+1 }=\int _{ N+1 }^{ \infty }{ { a }_{ n } } dn[/tex]



Homework Equations



They are above.


The Attempt at a Solution



I am not sure how I am supposed to integrate an [tex] a_{n} [/tex] since it contains n!.

Thanks,
Chris Maness
 
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  • #2
kq6up said:

Homework Statement



Problem # 30 in Ch1 Section 16 in Mary L. Boas' Math Methods in the Physical Sciences

It is clear that you (or your computer) can’t find the sum of an infinite series
just by adding up the terms one by one. For example, to get [tex]\zeta (1.1)=\sum _{ n=1 }^{ \infty }{ \frac { 1 }{ { n }^{ 1.1 } } } [/tex] (see Problem 15.22) with error < 0.005 takes about [tex]10^{33}[/tex] terms.
To see a simple alternative (for a series of positive decreasing terms) look at
Figures 6.1 and 6.2. Show that when you have summed N terms, the sum RN
of the rest of the series is between [tex]{ I }_{ N }=\int _{ N }^{ \infty }{ { a }_{ n } } dn\quad and\quad { I }_{ N+1 }=\int _{ N+1 }^{ \infty }{ { a }_{ n } } dn[/tex]



Homework Equations



They are above.


The Attempt at a Solution



I am not sure how I am supposed to integrate an [tex] a_{n} [/tex] since it contains n!.

Thanks,
Chris Maness
You haven't told us what an is.
 
  • #3
[tex]a_n[/tex] is the coefficient for the Mclaurin series. [tex]a_n=f(x)^n/n![/tex]

Chris
 
  • #4
kq6up said:

Homework Statement



Problem # 30 in Ch1 Section 16 in Mary L. Boas' Math Methods in the Physical Sciences

It is clear that you (or your computer) can’t find the sum of an infinite series
just by adding up the terms one by one. For example, to get [tex]\zeta (1.1)=\sum _{ n=1 }^{ \infty }{ \frac { 1 }{ { n }^{ 1.1 } } } [/tex] (see Problem 15.22) with error < 0.005 takes about [tex]10^{33}[/tex] terms.
To see a simple alternative (for a series of positive decreasing terms) look at
Figures 6.1 and 6.2. Show that when you have summed N terms, the sum RN
of the rest of the series is between [tex]{ I }_{ N }=\int _{ N }^{ \infty }{ { a }_{ n } } dn\quad and\quad { I }_{ N+1 }=\int _{ N+1 }^{ \infty }{ { a }_{ n } } dn[/tex]

Homework Equations



They are above.

The Attempt at a Solution



I am not sure how I am supposed to integrate an [tex] a_{n} [/tex] since it contains n!.

Thanks,
Chris Maness

The more serious problem would be attempting to integrate [itex]f^{(n)}(0)[/itex] with respect to [itex]n[/itex].

The wording "To see a simple alternative (for a series of positive decreasing terms)" suggests that [itex]a_n[/itex] is an arbitrary sequence of positive decreasing terms which decreases fast enough for [itex]\sum a_n[/itex] to converge, and not the Mclaurin series of an arbitrary function (which may contain negative terms).

In any event, you are not asked to do the actual integration, but to explain why, if [itex]a_n[/itex] is a decreasing positive sequence and [itex]f : \mathbb{R} \to \mathbb{R}[/itex] is a decreasing continuous positive function such that [itex]f(n) = a_n[/itex] for all [itex]n[/itex], it is the case that
[tex]
\int_N^{\infty} f(x)\,dx \leq \sum_{N+1}^\infty a_n \leq \int_{N+1}^\infty f(x)\,dx.
[/tex]
I suggest you look at the figures you are invited to look at, and see if that suggests a reason.
 

FAQ: Estimating Error for an Infinite Series (Mclaurin)

What is an infinite series?

An infinite series is a sum of infinitely many terms. It is typically written in the form of sigma notation, where the index starts at 0 and goes to infinity. For example, the infinite series for pi (π) is written as:
π = 4 + (4/3) + (4/5) + (4/7) + (4/9) + ...

What is a Maclaurin series?

A Maclaurin series is a special type of infinite series that is used to approximate a function by expressing it as a polynomial with infinitely many terms. It is named after the Scottish mathematician Colin Maclaurin.

How do you estimate error for an infinite series?

To estimate the error for an infinite series, you can use the Maclaurin error formula, which is given by:
E = R_n(x) = f^(n+1)(c) * (x-a)^(n+1) / (n+1)!
where n is the degree of the Maclaurin polynomial, x is the value at which you are evaluating the series, and c is some value between a and x. This formula allows you to find an upper bound for the error of the Maclaurin series approximation.

Why is estimating error important for infinite series?

Estimating error is important for infinite series because it allows us to determine the accuracy of our approximations. Since we cannot compute an infinite number of terms in a series, we have to use a finite number of terms. By estimating the error, we can determine how many terms we need to use in order to get a desired level of accuracy.

What are some applications of estimating error for infinite series?

Estimating error for infinite series has many applications in fields such as physics, engineering, and economics. For example, it can be used to approximate functions that are difficult to integrate, to model and predict complex systems, and to calculate values for important constants such as pi (π) and e (the base of the natural logarithm).

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