Simple divergence test

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01 Theory

Theory 1

Simple Divergence Test (SDT)

Applicability: Any series.

Test Statement:

limn→∞⁡an≠0⟹∑n=1∞andiverges

AKA the “Not Even Close” test

The converse is not valid. For example, ∑n=1∞1n diverges even though limn→∞⁡1n=0.

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02 Illustration

Example - Simple Divergence Test: examples

Simple divergence test: examples

(a) Consider: ∑n=1∞n4n+1

This diverges by the SDT because an→14 and not 0.


(b) Consider: ∑n=1∞(−1)n−1nn+1

This diverges by the SDT because limn→∞⁡an=DNE.

We can say the terms “converge to the pattern +1,−1,+1,−1,…,” but that is not a limit value.

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Positive series

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03 Theory

Theory 1

Positive series

A series is called positive when its individual terms are positive, i.e. an>0 for all n.

The partial sum sequence SN is monotone increasing for a positive series.

By the monotonicity test for convergence of sequences, SN therefore converges whenever it is bounded above. If SN is not bounded above, then ∑n=1∞an diverges to +∞.


Another test, called the integral test, studies the terms of a series as if they represent rectangles with upper corner pinned to the graph of a continuous function.

To apply the test, we must convert the integer index variable n in an into a continuous variable x. This is easy when we have a formula for an (provided it doesn’t contain factorials or other elements dependent on integrality).

Integral Test (IT)

Applicability: f(x) must be:

  • Continuous
  • Positive
  • Monotone decreasing

Test Statement:

∑n=1∞anconverges⟺∫1∞f(x)dxconverges

Extra - Integral test: explanation

To show that integral convergence implies series convergence, consider the diagram:

This shows that ∑n=2Nan≤∫1Nf(x)dx for any N. Therefore, if ∫1∞f(x)dx converges, then ∫1Nf(x)dx is bounded (independent of N) and so ∑n=2Nan is bounded by that inequality. But ∑n=2Nan=SN−a1; so by adding a1 to the bound, we see that SN itself is bounded, which implies that ∑n=1∞an converges.

To show that integral divergence implies series divergence, consider a similar diagram:

This shows that ∑n=1N−1an≥∫1Nf(x)dx for any N. Therefore, if ∫1∞f(x)dx diverges, then ∫1Nf(x)dx goes to +∞ as N→∞, and so ∑n=1N−1an goes to +∞ as well. So ∑n=1∞an diverges.

Notice: the picture shows f(x) entirely above (or below) the rectangles. This depends upon f(x) being monotone decreasing, as well as f(x)>0. (This explains the applicability conditions.)


Next we use the integral test to evaluate the family of p-series, and later we can use p-series in comparison tests without repeating the work of the integral test.

p-series

A p-series is a series of this form: ∑n=1∞1np

Convergence properties:

p>1:series convergesp≤1:series diverges

Extra - Proof of p-series convergence

(1) To verify the convergence properties of p-series, apply the integral test:

Applicability: verify it’s continuous, positive, decreasing.

Convert n to x to obtain the function f(x)=1xp.

Indeed 1xp is continuous and positive and decreasing as x increases.


(2) Apply the integral test.

Integrate, assuming p≠1:

∫1∞1xpdx≫≫limR→∞⁡xp−1p−1|1R≫≫limR→∞⁡(R−p+1−p+1−1−p+1−p+1)

When p>1 we have limR→∞⁡R−p+1−p+1=0

When p<1 we have limR→∞⁡R−p+1−p+1=∞

When p=1, integrate a second time:

∫1∞1xdx≫≫limR→∞⁡ln⁡x|1R≫≫limR→∞⁡ln⁡R−ln⁡1≫≫∞

Conclude: the integral converges when p>1 and diverges when p≤1.


We could instead immediately refer to the convergence results for p-integrals instead of reproving them here.

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04 Illustration

Example - p-series examples

p-series examples

By finding p and applying the p-series convergence properties:

We see that ∑n=1∞1n1.1 converges: p=1.1 so p>1

But ∑n=1∞1n diverges: p=1/2 so p≤1

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Example - Integral test - pushing the envelope of convergence

Integral test - pushing the envelope of convergence

Does ∑n=2∞1nln⁡n converge?

Does ∑n=2∞1n(ln⁡n)2 converge?

Notice that ln⁡n grows very slowly with n, so 1nln⁡n is just a little smaller than 1n for large n, and similarly 1n(ln⁡n)2 is just a little smaller still.

Solution

(1) The two series lead to the two functions f(x)=1xln⁡x and g(x)=1x(ln⁡x)2.

Check applicability.

Clearly f(x) and g(x) are both continuous, positive, decreasing functions on x∈[2,∞).


(2) Apply the integral test to f(x).

Integrate f(x):

∫2∞1xln⁡xdx≫≫∫u=ln⁡2∞1udu≫≫limR→∞⁡ln⁡u|ln⁡1R≫≫∞

Conclude: ∑n=2∞1nln⁡n diverges.


(3) Apply the integral test to g(x).

Integrate g(x):

∫2∞1x(ln⁡x)2dx≫≫∫u=ln⁡2∞1u2du≫≫limR→∞⁡−u−1|ln⁡2R≫≫1ln⁡2

Conclude: ∑n=2∞1n(ln⁡n)2 converges.

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05 Theory

Theory 2

Direct Comparison Test (DCT)

Applicability: Both series are positive: an>0 and bn>0.

Test Statement: Suppose an≤bn for large enough n. (Meaning: for n≥N with some given N.) Then:

  • Smaller pushes up bigger:
∑n=1∞andiverges⟹∑n=1∞bndiverges
  • Bigger controls smaller:
∑n=1∞bnconverges⟹∑n=1∞anconverges
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06 Illustration

Example - Direct comparison test: rational functions

Direct comparison test: rational functions

(a) ∑n=1∞1n3n

Choose: an=1n3n and bn=13n

Check: 1n3n≤13n

Observe: ∑13n is a convergent geometric series

Therefore: converges by the DCT.


(b) ∑n=1∞cos2⁡nn3

Choose: an=cos2⁡nn3 and bn=1n3.

Check: cos2⁡nn3≤1n3

Observe: ∑1n3 is a convergent p-series

Therefore: converges by the DCT.


(c) ∑n=1∞nn3+1

Choose: an=nn3+1 and bn=1n2

Check: nn3+1≤1n2 (notice that nn3+1≤nn3)

Observe: ∑1n2 is a convergent p-series

Therefore: converges by the DCT.


(d) ∑n=2∞1n−1

Choose: an=1n and bn=1n−1

Check: 1n≤1n−1

Observe: ∑1n is a divergent p-series

Therefore: diverges by the DCT.

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07 Theory

Theory 3

Some series can be compared using the DCT after applying certain manipulations and tricks.

For example, consider the series ∑n=2∞1n2−1. We suspect convergence because an≈1n2 for large n. But unfortunately, an>1n2 always, so we cannot apply the DCT.

We could make some ad hoc arguments that do use the DCT, eventually:

Trick Method 1:

  • Observe that for n>1 we have 1n2−1≤10n2. (Check it!)
  • But ∑10n2 converges, indeed its value is 10⋅∑1n2, which is 10π26.
  • So the series ∑1n2−1 converges.

Trick Method 2:

  • Observe that we can change the letter n to n+1 by starting the new n at n=1.
  • Then we have:
∑n=2∞1n2−1=∑n=1∞1(n+1)2−1=∑n=1∞1n2+2n
  • This last series has terms smaller than 1n2 so the DCT with bn=1n2 (a convergent p-series) shows that the original series converges too.

These convoluted arguments suggest that a more general version of Comparison is possible.

Indeed, it is sufficient to compare the relative large-n behavior of the two series. We use the termwise ratios to estimate comparative behavior for increasing n.

Limit Comparison Test (LCT)

Applicability: Both series are positive: an>0 and bn>0.

Test Statement: Suppose that limn→∞⁡anbn=L. Then: If 0<L<∞, i.e. finite non-zero, then:

∑anconverges⟺∑bnconverges

Extra - LCT edge cases

If L=0 or L=∞, we can still draw an inference, but only in one direction:

  • If L=0:
∑bnconverges⟹∑anconverges
  • If L=∞:
∑bndiverges⟹∑andiverges

Extra - Limit Comparison Test explanation

Suppose an/bn→L and 0<L<∞. Then for n sufficiently large, we know an/bn<L+1.

Doing some algebra, we get an<(L+1)bn for n large.

If ∑bn converges, then ∑(L+1)bn also converges (constant multiple), and then the DCT implies that ∑an converges.

Conversely: we also know that bn/an→1/L, so bn<(1/L+1)an for all n sufficiently large. Thus if ∑an converges, ∑(1/L+1)an also converges, and by the DCT again ∑bn converges too.

The cases with L=0 or L=∞ are handled similarly.

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08 Illustration

Example - Limit Comparison Test examples

Limit comparison test examples

(a) ∑n=1∞12n−1

Choose: an=12n−1 and bn=12n.

Compare in the limit:

limn→∞⁡anbn≫≫limn→∞⁡2n2n−1≫≫1=:L

Observe: ∑12n is a convergent geometric series

Therefore: converges by the LCT.


(b) ∑n=1∞2n2+3n5+n5

Choose: an=2n2+3n5+n5, bn=1n1/2

Compare in the limit:

limn→∞⁡anbn≫≫limn→∞⁡(2n2+3n)n5+n5 (2n2+3n)n5+n5⟶n→∞2n5/2n5/2→2=:L

Observe: ∑1n1/2 is a divergent p-series

Therefore: diverges by the LCT.


(c) ∑n=2∞n2n4−n−1

Choose: an=n2n4−n−1 and bn=1n2

Compare in the limit:

limn→∞⁡anbn≫≫limn→∞⁡n4n4−n−1≫≫1=:L

Observe: ∑n=2∞1n2 is a converging p-series

Therefore: converges by the LCT.

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Alternating series

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09 Theory

Theory 1

Consider these series:

1+12+13+14+15+16+17+⋯=∞−1−12−13−14−15−16−17−⋯=−∞1−12+13−14+15−16+17−⋯=ln⁡21+12−13+14−15−16+17+⋯=?

The absolute values of terms are the same between these series, only the signs of terms change.

The first is a positive series because there are no negative terms.

The second series is the negation of a positive series – the study of such series is equivalent to that of positive series, just add a negative sign everywhere. These signs can be factored out of the series. (For example ∑−1n=−∑1n.)

The third series is an alternating series because the signs alternate in a strict pattern, every other sign being negative.

The fourth series is not alternating, nor is it positive, nor negative: it has a mysterious or unknown pattern of signs.

A series with any negative signs present, call it ∑n=1∞an, converges absolutely when the positive series of absolute values of terms, namely ∑n=1∞|an|, converges.

THEOREM: Absolute implies ordinary

If a series ∑n=1∞an converges absolutely, then it also converges as it stands.

A series might converge due to the presence of negative terms and yet not converge absolutely:

A series ∑n=1∞an is said to be converge conditionally when the series converges as it stands, but the series produced by inserting absolute values, namely ∑n=1∞|an|, diverges.

The alternating harmonic series above, 1−12+13−14+⋯=ln⁡2, is therefore conditionally convergent. Let us see why it converges. We can group the terms to create new sequences of pairs, each pair being a positive term. This can be done in two ways. The first creates an increasing sequence, the second a decreasing sequence:

increasing from 0:(1−12)+(13−14)+(15−16)+(17−18)+⋯decreasing from 1:1−(12−13)−(14−15)−(16−17)−⋯

Suppose SN gives the sequence of partial sums of the original series. Then S2N gives the first sequence of pairs, namely S2, S4, S6, … . And S2N−1 gives the second sequence of pairs, namely S1, S3, S5, … .

The second sequence shows that SN is bounded above by 1, so S2N is monotone increasing and bounded above, so it converges. Similarly S2N−1 is monotone decreasing and bounded below, so it converges too, and of course they must converge to the same thing.

The fact that the terms were decreasing in magnitude was an essential ingredient of the argument for convergence. This fact ensured that the parenthetical pairs were positive numbers.

Alternating Series Test (AST) - “Leibniz Test”

Applicability: Alternating series only: ∑n=1∞(−1)n−1an with an>0

Test Statement: If:

  1. an→0 as n→∞ (i.e. it passes the SDT: if this fails, conclude diverges)
  2. an are decreasing, so a1>a2>a3>a4>⋯>0

Then:

∑n=1∞(−1)n−1anconverges

“Next Term Bound” rule for error of the partial sums:

|S−SN|<aN+1

Extra - Alternating Series Test: Theory

Just as for the alternating harmonic series, we can form positive paired-up series because the terms are decreasing:

(a1−a2)+(a3−a4)+(a5−a6)+⋯a1−(a2−a3)−(a4−a5)−(a6−a7)−⋯

The first sequence S2N is monotone increasing from 0, and the second S2N−1 is decreasing from a1. The first is therefore also bounded above by a1. So it converges. Similarly, the second converges. Their difference at any point is S2N−S2N−1 which is equal to −a2N, and this goes to zero. So the two sequences must converge to the same thing.

By considering these paired-up sequences and the effect of adding each new term one after the other, we obtain the following order relations:

0<S2<S4<S6<⋯<S<⋯<S5<S3<S1=a1

Thus, for any even 2N and any odd 2M−1:

S2N<S<S2M−1

Now set M=N and subtract S2N−1 from both sides:

S2N−S2N−1<S−S2N−1<0≫≫−a2N<S−S2N−1<0

Now set M=N+1 and subtract S2N from both sides:

0<S−S2N<S2N+1−S2N≫≫0<S−S2N<a2N+1

This covers both even cases (n=2N) and odd cases (n=2N−1). In either case, we have:

|S−Sn|<an+1
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10 Illustration

Example - Alternating Series Test: Basic illustration

Alternating series test: basic illustration

(a) ∑n=1∞(−1)n−1n converges by the AST.

Notice that ∑1n diverges as a p-series with p=1/2<1.

Therefore the first series converges conditionally.


(b) ∑n=1∞cos⁡nπn2 converges by the AST.

Notice the funny notation: cos⁡nπ=(−1)n.

This series converges absolutely because |cos⁡nπn2|=1n2, which is a p-series with p=2>1.

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Example - Approximating π

Approximating pi

The Taylor series for tan−1⁡x is given by:

tan−1⁡x=x−x33+x55−x77+⋯

Using the AST “next term bound,” how many terms are needed to approximate π to within 0.001?

Solution

(1) The main idea is to use tan⁡π4=1 and thus tan−1⁡1=π4. Therefore:

π4=1−13+15−17+⋯

and thus:

π=4−43+45−47+⋯

(2) Write En for the error of the approximation, meaning En=S−Sn.

By the AST error formula, we have |En|<an+1.

We desire n such that |En|<0.001. Therefore, calculate n such that an+1<0.001, and then we will know:

|En|<an+1<0.001

(3) The general term is an=42n−1. Plug in n+1 in place of n to find an+1=42n+1. Now solve:

an+1=42n+1<0.001≫≫40.001<2n+1≫≫3999<2n≫≫2000≤n

We conclude that at least 2000 terms are necessary to be confident (by the error formula) that the approximation of π is accurate to within 0.001.

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