The squeeze theorem – Serlo

The squeeze theorem is a powerful tool to determine the limit of a complicated sequence. It is based on comparison to simpler sequences, for which the limit is easily determinable.

Motivation

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Convergence proof for a root sequence (video in German)

The intuition behind this theorem is quite simple: We are given a complicated sequence   and want to know whether it converges. Often, one can leave out terms in the complicated sequence   and gets some simpler sequences   and  . If   is a lower bond and   an upper bound, then   is "caught" in the space between both functions. If both sequences converge to the same limit  , they "squeeze" together this space to this single point and   has no other option than to converge towards  , as well.

 
image about the principle of the sandwich lemma
 
A ham & cheese sandwich

You may also visualize this theorem by a "ham & cheese sandwich" (see image on the right). The upper and lower bounds   and   act as two "slices of toast", which confine   , i.e. the "filling". If you squeeze the toast slices together, the filling in between will also squeezed to this point.

The squeeze theorem

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Squueze theorem (video in German, YouTube videofrom the YouTube channel Maths CA).

The theorem reads as follows:

Theorem (Squeeze theorem)

Let   be any sequence. If two sequences   and   exist with   for all   and   for some common limit  , then   will also converge towards this limit  .

Proof (Squeeze theorem)

Let   and   be such that   and   for some  .

We need to prove   , i.e. for each   there is an  , such that   for all   . Let   be arbitrary. By assumption, the convergences   and   hold.

Hence, there are two thresholds   and   with   for all   (lower bounding sequence) and   for all   (upper bounding sequence). Now, there is   for  . For each   we distinguish the two cases   (above the limit) and   (below the limit). In case   there is

 

The other case   is treated analogously:

 

So if the maximum of both thresholds   is exceeded by   ( i.e.   and   hold), then for  ,

 

and for  ,

 

So in either case   for all  , which establishes convergence.

Hint

The inequality   does not need to be satisfied for all   . Assume, a finite amount of sequence elements   is not "caught between   and  ". Then, after some finite   all of these finite elements will have been passed and the further sequence elements   will indeed be caught as  .

Example

We illustrate the squeeze theorem using the root sequence   for an arbitrary but fixed constant  .

Two cases need to be distinguished:

Fall 1:  

Let us choose some  , such that  . Then, for all   with   there is  , so  , i.e. we have an upper bound. A lower bound is given by   for any  .

In the previous chapter, we have established the limit  . So the squeeze theorem yields   for any   standing inside the root.

Fall 2:  

In this case,  , so we are led to the above case, replacing   by   . Hence, also   and the limit theorems (quotient) yield:

 

A useful special case

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We often encounter 0 as a sequence limit (null sequence). Since the squeeze theorem can be used to prove any   to be a limit of a sequence  , it can also be used for  . Especially, for any convergent  , the sequence   must converge to 0:

Theorem (Special case of the squeeze theorem)

Let   be a null sequence and   for all  . Then  .

Proof (Special case of the squeeze theorem)

We set  . For the constant sequence   there is   (absolute values are always positive). Since   the squeeze theorem also implies  . Therefore,   is a null sequence, which is equivalent to the statement that   converges to   .

Hint

This special case of the squeeze theorem is also referred to as direct comparison argument for sequences .

Example

Let us take a look at  . Not a simple case: both   and   converge to   . But we can show that its difference converges to 0, since it is bounded by:

 

Let us prove this inequality. There is   so   and the difference is positive, i.e.   .

Now, we remove the root by squaring it:

 

The last inequality holds for all   and implies

 

But now,   is a null sequence, so the squeeze theorem proves our assertion.

Squeeze theorem: examples and problems

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Squeeze theorem: example & exercise 1

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Example (Squeeze theorem 1)

Let us consider the sequence   with

 
 
The first 6 elements of the sequence  ,   and  

We take a look at the first sequence elements   . They seem to be alternately positive and negative and they tend to zero. We ise as lower or upper bound   and  . Then,   dso we caught   for all  .

In addition  . The squeeze theorem hence implies  .

Question: Which further sequences   and   can be used for catching and squeezing  ?

We could also use   and  . Or any other power between 0 and 2 (not necessarily 1 or 2).

Exercise (Squeeze theorem 1)

Investigate whether the sequence   converges, using the squeeze theorem, where

  with  

Solution (Squeeze theorem 1)

For   there is a   with  . By monotony of the power function,

 

Hence

 

The limit theorems yield

 

and analogously,  . Hence, by means of the squeeze theorem  .


Squeeze theorem: example & exercise 2

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Example (Squeeze theorem 2)

Next, we prove that the sequence   with

 

converges to 0.

 
The first elements of  ,   and  

This sequence is bounded from below as all elements are positive   for all  . An upper bound can be found by multiplying out   and  :

 

Obviously, both bounding sequences converge to zero:   . So the squeeze theorem implies  .

Exercise (Squeeze theorem 2)

Use the squeeze theorem to show that   and   with

  1.  
  2.  

both converge to 0.

Solution (Squeeze theorem 2)

Part 1: The trick is multiplying out again. this time, for   and   :

 

Since   , the squeeze theorem implies  .

Part 2:

Again, the elements are positive, i.e.  . Bounding from above can be done using   for all  . This inequality can be shown by induction:

Induction basis:  .

 

Induction step:  .

 

So for all  ,

 

Since   , the squeeze theorem implies  .

Squeeze theorem: example & exercise 3

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Example (Squeeze theorem 3)

Now, let us consider the root sequences   and   with

 

and

 

First sequence: For   , monotony of the  -th root implies

 

as well as

 

For those two bounding sequences, the limit theorems imply

 

and

 

The squeeze theorem hence implies  .

Second sequence: For   the root function is monotonous, as well

 

and

 

We use the limit theorems for the upper and lower bounding sequences

 

and

 

So the squeeze theorem implies  .

Exercise (Squeeze theorem 3)

Use the squeeze theorem to determine the limits of the sequences  ,   and   with

  1.  
  2.  

Solution (Squeeze theorem 3)

Part 1: For   the  -th root is monotonous, so

 

further, for  

 

Hence, there is

 

The limit theorems for the bounding sequences yield

 

and

 

So the squeeze theorem implies  .

Part 2: For   we again have monotony of the  -th root

 

Further,

 

The limit theorems for the bounding sequences render

 

And by means of the squeeze theorem  .

Squeeze theorem: example & exercise 4

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Example (Squeeze theorem 4)

Let   be the sequence of partial sums

 

Then, for all   we bound:

 

and hence

 

For the lower bound,

 

For the upper bound,

 

So the squeeze theorem implies  .

Exercise (Squeeze theorem 4)

Investigate the limits of the two sequences of partial sums   and   defined by

  1.  
  2.  

using the squeeze theorem.

Solution (Squeeze theorem 4)

Part 1: For all   we have:

 

so we have some bounds

 

For the upper bound,

 

For the lower bound,

 

So the squeeze theorem implies  .

Part 2: For all   and   there is:

 

so we have the bounds

 

The lower bound is just 0 and for the upper bound,

 

So the squeeze theorem implies  .

Squeeze theorem: example & exercise 5

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Exercise (Squeeze theorem 5)

Does the series   converge`? If yes, what is its limit? Provide proofs for your claims.

How to get to the proof? (Squeeze theorem 5)

Perhaps, you have already encountered the sequence   which converges to Euler's number  . This is an upper bound for   , so with that knowledge, we may assert that it converges. But what is the limit? Maybe, we should start by computing some initial sequence elements.:

 

It looks as if   converges to   . This gets particularly clear, if we plot the sequence elements in a diagram:

 
The first 20 elements of the sequence (1-n^(-2))^n

What makes determining the limit hard is the exponent of   . Otherwise, we could use some limit theorems of an epsilon-delta proof and would be done quite fast. Luckily, there is a trick to get rid of the exponent: Bernoulli's inequality:

 

The sequence   is hence a lower bound on   and it clearly converges to   . We hence set   as a lower bound for the squeeze theorem.

How to get an upper bound converging to 1? This is quite easy, since   is always smaller than   : The expression   is always smaller than 1 and therefore, also every power of it will be. This includes   being smaller than  .

So for the squeeze theorem, we have for all   the inequality:

 

both the lower and the upper bounding sequence converge to the identical limit   . So by means of the squeeze theorem, there must also be  .

Proof (Squeeze theorem 5)

Bernoulli's inequality implies

 

And there is

 

So we get the bounds

 

Both bounding sequences converge to   , so by means of the squeeze theorem,   which was to be shown.

Hint

If you are allowed to use   and   , then you can also obtain the result directly, using the third binomial formula:

 

Exercise (Squeeze theorem 5)

Does the sequence   converge? If yes, what is its limit? Prove your assertions.

How to get to the proof? (Squeeze theorem 5)

Again, Bernoulli's inequality can be used for getting rid of the exponential  . There is

 

so we have a bound from below converging to  . But   is already a lower bound converging to  . So Bernoulli's inequality doesn't help us in this form. What we need is an upper bound converging to  . This can be done by a little trick: we put expressions from the enumerator in the denominator and apply Bernoulli's inequality afterwards:

 

Hence, we have an upper bound  . Enumerator and denominator of the bound are both dominated by the  , so they cancel out when using the limit theorems and we obtain  . Therefore, we have two bounding sequences   converging to   and by the squeeze theorem   .

Proof (Squeeze theorem 5)

Let   and  . There is  :

 

The bounding sequences converge to   and (by means of the limit theorem):

 

So there is   and the squeeze theorem implies   .

Alternative proof (Squeeze theorem 5)

If the exponential series   is known, one can also use the binomial theorem for a bounding. A lower bound is given by   . For the upper bound, we use the binomial theorem  :

 

This yields a suitable upper bound and we get   . The squeeze theorem then implies that   converges to   .

Alternative proof (Squeeze theorem 5)

We may use   for a direct proof. The sequence   is a subsequence of   and converges to the same limit:

 

Hence, for some  , there is   for all  . The sequence   can be recovered from this by taking the   root   . So for all  :

 

With   , the squeeze theorem again implies that   converges to  .

Examples & exercises: squeeze theorem for null sequences

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Example (Squeeze theorem for null sequences 1)

First, an easy example: we prove  .

We estimate   by the null sequence   as follows:

 

Since   is a null sequence, we can use   as an upper and   as a lower bound for  . The squeeze theorem then implies  .

Example (Squeeze theorem for null sequences 2)

Now, it gets a bit more complicated: We fix any   and prove that  . This can be done by bounding   from above using a null sequence   . the trick is to use Bernoulli's inequality, which will yield us to the upper bound   :

 

Which implies

 

So we have

 

The squeeze theorem for null sequences directly implies  .

Exercise (Squeeze theorem for null sequences)

Prove that  .

Solution (Squeeze theorem for null sequences)

In order to use the squeeze theorem, we need to bound   from above by a null sequence. The binomial theorem can be used for this bounding. In case  :

 

This inequality implies

 

So the desired bound reads

 

And by means of the squeeze theorem  , which was to be shown.