An Introduction to Analysis/Sequences of numbers

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The chapter begins with sequences of real and complex numbers and the question of their convergence and connection with continuity and such. Following the classical topics and the discussion of first and second countability we introduce notions of measure and semi-norms.

Groups

A group is a triple (G,,id) consisting of:

  • (1) a set G
  • (2) a composition law such that for every f and g in G, gf in G.
  • (3) the identity idG; i.e., for every f in G, fid=f=idf in G.

subject to the following conditions:

  • For every f in G, f is invertible; i.e., there exists some g in G, called the inverse of f, such that gf=f=fg.
  • The composition law is associative; that is, for every f, g, h in G, h(gf) = (hg)f.

A group is said to be abelian if fg=gf.

2 Theorem Let Sn be the set of all bijections from 1,2,...n into itself. Then Sn forms a group under composition.
Proof: Clear.

The group in the theorem is called a symmetric group and its elements permutations of n objects. The sign of a permutation σ is defined to be 0 if a permutation is identity, even if it can be written as the composition of an even number of permutations and odd otherwise.

We remark that there cannot be a bijection defined on a finite set that is not a permutation at the same time. For one thing, since we define that a function is single-valued, the range of a function defined on any finite set must be finite. If it is injective additionally the number of elements of its range equals to that of elements in the domain.

A ring F is an abelian group ((G,+,0),,1) consisting of an abelian group, the multiplication and the identity with the distribution law; i.e.,

  • h(f+g)=(hf)+(hg).

A field is a ring, all of whose non-zero elements are invertible.

We assume in every field 01. This is an example of a theorem that holds since a field is an abelian group.

A sequence in a set E is a function f:E

2 Theorem Every infinite subset A of a countable set B is countable.
Proof: Since B is countable, we can find a sequence in B. Since AB, there is a subsequence of B which is a bijection from B.

Real and complex numbers

In this chapter, we work with a number of subtleties like completeness, ordering, Archimedian property; those are usually never seriously concerned when Calculus was first conceived. I believe that the significance of those nuances becomes clear only when one starts writing proofs and learning examples that counter one's intuition. For this, I suggest a reader to simply skip materials that she does not find there is need to be concerned with; in particular, the construction of real numbers does not make much sense at first glance, in terms of argument and the need to do so. In short, one should seek rigor only when she sees the need for one. WIthout loss of continuity, one can proceed and hopefully she can find answers for those subtle questions when she search here. They are put here not for pedagogical consideration but mere for the later chapters logically relies on it.

We denote by the set of natural numbers. The set does not form a group, and the introduction of negative numbers fills this deficiency. We leave to the readers details of the construction of integers from natural numbers, as this is not central and menial; to do this, consider the pair of natural numbers and think about how arithmetical properties should be defined. The set of integers is denoted by . It forms an integral domain; thus, we may define the quotient field of , that is, the set of rational numbers, by

={ab:𝑏 are integers and 𝑏 is nonzero }.

As usual, we say for two rationals x and y x<y if xy is positive.

We say E is bounded above if there exists some x in such that for any yE yx, and is bounded below if the reversed relation holds. Notice E is bounded above and below if and only if E is bounded in the definition given in the chapter 1.

The reason for why we want to work on problems in analysis with instead of is a quite simple one:

2 Theorem Fundamental axiom of analysis fails in .
Proof: 1,1.4,1.41,1.414,...21/2.

How can we create the field satisfying this axiom? i.e., the construction of the real field. There are several ways. The quickest is to obtain the real field by completing .

We define the set of complex numbers =/<i2+1> where i is just a symbol. That i2+1 is irreducible says the ideal generated by it is maximal, and the field theory tells that C is a field. Every complex number z has a form:

z=a+bi+<i2+1>.

Though the square root of -1 does not exist, i2 can be thought of as -1 since i2+<i2+1>=1+1+i2+<i2+1>=1. Accordingly, the term <i2+1>is usually omitted.

2 Exercise Prove that there exists an irrational numbers a,b such that ab is rational.

Sequences

2 Theorem Let sn be a sequence of numbers, be they real or complex. Then the following are equivalent:

  • (a) sn converges.
  • (b) There exists a cofinite subsequence in every open ball.

Theorem (Bolzano-Weierstrass) Every infinite bounded set has a non-isolated point.
Proof: Suppose S is discrete. Then S is closed; thus, S is compact by Heine-Borel theorem. Since S is discrete, there exists a collection of disjoint open balls Sx containing x for each xS. Since the collection is an open cover of S, there exists a finite subcover {Sx1,Sx2,...,Sxn}. But

S({Sx1Sx2...Sxn}={x1,x2,...xn}

This contradicts that S is infinite. .

2 Corollary Every bounded sequence has a convergent subsequence. Proof:

The definition of convergence given in Ch 1 is general enough but is usually inconvenient in writing proofs. We thus give the next theorem, which is more convenient in showing the properties of the sequences, which we normally learn in Calculus courses.

In the very first chapter, we discussed sequences of sets and their limits. Now that we have created real numbers in the previous chapter, we are ready to study real and complex-valued sequences. Throughout the chapter, by numbers we always means real or complex numbers. (In fact, we never talk about non-real and non-complex numbers in the book, anyway)

Given a sequence an of numbers, let Ej={aj,aj+1,}. Then we have:

lim supjan =lim supjEj
=j=1k=jEk
=j=1sup{ak,ak+1,...}

The similar case holds for liminf as well.

Theorem Let sj be a sequence. The following are equivalent:

  • (a) The sequence sj converges to s.
  • (b) The sequence sj is Cauchy; i.e., |snsm|0 as n,m.
  • (c) Every convergent subsequence of sj converges to s.
  • (d) lim supjsj=lim infjsj.
  • (e) For each ϵ>0, we can find some real number N (i.e., N is a function of ϵ) so that
    |sjs|<ϵ for jN.

Proof: From the triangular inequality it follows that:

|snsm||snn|+|smm|.

Letting n,m gives that (a) implies (b). Suppose (b). The Bolzano-Weierstrass theorem states that every bounded sequence has a convergent subsequence, say, sk. Thus,

|sns| =|snsm+sms|
|snsm|+|sms|
<ϵ 2+ϵ 2=ϵ
. Therefore, sjs. That (c) implies (d) is obvious by definition.

2 Theorem Let xj and yj converge to x and y, respectively. Then we have:

(a) limj(xj+yj)=x+y.
(b) limj(xjyj)=xy.

Proof: Let ϵ>0 be given. (a) From the triangular inequality it follows:

|(xj+yj)(x+y)|=|xjx|+|yjy|<ϵ2+ϵ2=ϵ

where the convergence of xj and yj tell that we can find some N so that

|xjx|<ϵ2 and |yjy|<ϵ2 for all j>N.

(b) Again from the triangular inequality, it follows:

|xjyjxy| =|(xjx)(yjy+y)+x(yjy)|
|xjx|(1+|y|)+|x||yjy|
0 as j

where we may suppose that |yjy|<1.

Other similar cases follows from the theorem; for example, we can have by letting yj=α

limj(kxj)=klimjxj.

2.7 Theorem Given an in a normed space:

  • (a) an converges.
  • (b) The sequence an1n has the upper limit a*<1. (Archimedean property)
  • (c) (an+1) converges.

Proof: If (b) is true, then we can find a N>0 and b such that for all nN:

an1n<b<1. Thus (a) is true since:,
1an=a+Nana+0bn=a+11b if a=1N1.

Continuity

Let f:Ω{,}. We write {f>a} to mean the set {x:xΩ,f(x)>a}. In the same vein we also use the notations like {f<a},{f=a}, etc.

We say, u is upper semicontinuous if the set {f<c} is open for every c and lower semicontinuous if f is upper semicontinuous.

2 Lemma The following are equivalent.

  • (i) u is upper semicontinuous.
  • (ii) If u(z)<c, then there is a δ>0 such that sup|sz|<δf(s)<c.
  • (iii) lim supszu(s)u(z).

Proof: Suppose u(z)<c. Then we find a c2 such that u(z)<c2<c. If (i) is true, then we can find a δ>0 so that sup|sz|<δu(s)c2<c. Thus, the converse being clear, (i) (ii). Assuming (ii), for each ϵ>0, we can find a δϵ>0 such that sup|sz|<δϵu(s)<u(z)+ϵ. Taking inf over all ϵ gives (ii) (iii), whose converse is clear.

2 Theorem If u is upper semicontinuous and < on a compact set K, then u is bounded from above and attains its maximum.
Proof: Suppose u(x)<supKu for all xK. For each xK, we can find g(x) such that u(x)<g(x)<supKu. Since u is upper semicontinuous, it follows that the sets {u<g(x)}, running over all xK, form an open cover of K. It admits a finite subcover since K is compact. That is to say, there exist x1,x2,...,xnK such that K{u<g(x1)}{u<g(x1)}...{u<g(xn)} and so:

supKumax{g(x1),g(x2),...,g(xn)}<supKu,

which is a contradiction. Hence, there must be some zK such that u(z)=supKu.

If f is continuous, then both f1((α,)) and f1((,β)) for all α,β are open. Thus, the continuity of a real-valued function is the same as its semicontinuity from above and below.

2 Lemma A decreasing sequence of semicontinuous functions has the limit which is upper semicontinuous. Conversely, let f be upper semicontinuous. Then there exists a decreasing sequence fj of Lipschitz continuous functions that converges to f.
Proof: The first part is obvious. To show the second part, let fj(x)=infyEf(x)+j1|yx|. Then fj has the desired properties since the identity |fj(x)fj(y)|=j1|xy|.

We say a function is uniform continuous on E if for each ϵ>0 there exists a δ>0 such that for all x,yE with |xy|<δ we have |f(x)f(y)|.

Example: The function f(x)=x is uniformly continuous on since

|f(x)f(y)|=|xy|<δ=ϵ

while the function f(x)=x2 is not uniformly continuous on since

|f(x)f(y)|=|x2y2|=|xy||x+y|

and |x+y| can be made arbitrary large.

2 Theorem Let f:K for K compact. If f is continuous on K, then f is uniformly continuous on K.
Proof:

The theorem has this interpretation; an uniform continuity is a global property in contrast to continuity, which is a local property.

2 Corollary A function is uniform continuous on a bounded set E if and only if it has a continuos extension to E.
Proof:

2 Theorem if the sequence {fj} of continuos functions converges uniformly on a compact set K, then the sequence {fj} is equicontinuous.
Let ϵ>0 be given. Since fjf uniformly, we can find a N so that:

|fj(x)f(x)|<ϵ/3 for any j>N and any xK.

Also, since K is compact, f and each fj are uniformly continuous on K and so, we find a finite sequence {δ0,δ1,...δN} so that:

|f(x)f(y)|<ϵ/3 whenever |xy|<δ0 and x,yE,

and for i=1,2,...N,

|fi(x)fi(y)|<ϵ whenever |xy|<δi and x,yE.

Let δ=min{δ0,δ1,...δn}, and let x,yK be given. If jN, then

|fj(x)fj(y)|<ϵ whenever |xy|<δ.

If j>N, then

|fj(x)fj(y)| |fj(x)f(x)|+|f(x)f(y)|+|f(y)fj(y)|
<ϵ

whenever |xy|<δ.

2 Theorem A sequence fj of complex-valued functions converges uniformly on E if and only if supE|fnfm|0as n,m
Proof: Let =supE||. The direct part follows holds since the limit exists by assumption. To show the converse, let f(x)=limjfj(x) for each xE, which exists since the completeness of . Moreover, let ϵ>0 be given. Since from the hypothesis it follows that the sequence fj of real numbers is Cauchy, we can find some N so that:

fnfm<ϵ/2 for all n,m>N.

The theorem follows. Indeed, suppose j>N. For each x, since the pointwise convergence, we can find some M so that:

fk(x)f(x)<ϵ/2 for all k>M.

Thus, if n=max{N,M}+1,

|fj(x)f(x)||fj(x)fn(x)|+|fn(x)f(x)|<ϵ.

2 Corollary A numerical sequence aj converges if and only if |anam|0 as n,m.
Proof: Let fj in the theorem be constant functions. .

2 Theorem (Arzela) Let K be compact and metric. If a family of real-valued functions on K bestowed with =supK|| is pointwise bounded and equicontinuous on a compact set K, then:

  • (i) is uniformly bounded.
  • (ii) is totally bounded.

Proof: Let ϵ>0 be given. Then by equicontinuity we find a δ>0 so that: for any x,yK with xB(δ,y)

|f(x)f(y)|<ϵ/3

Since K is compact, it admits a finite subset K2 such that:

KxK2B(δ,x).

Since the finite union of totally and uniformly bounded sets is again totally and uniformly bounded, we may suppose that K2 is a singleton {y}. Since the pointwise boundedness we have:

|f(x)||f(x)f(y)|+|f(y)|ϵ/3+|f(y)|< for any xK and f,

showing that (i) holds. To show (ii) let A=f{f(y)}. Then since A is compact, A is totally bounded; hence, it admits a finite subset A2 such that: for each f, we can find some g(y)A2 so that:

|f(y)g(y)|<ϵ/3.

Now suppose xK and f. Finding some g(y) in A2 we have:

|f(x)g(x)||f(x)f(y)|+|f(y)g(y)|+|g(y)g(x)|<ϵ/3.

Since there can be finitely many such g, this shows (ii).

2 Corollary (Ascoli's theorem) If a sequence fj of real-valued functions is equicontinuous and pointwise bounded on every compact subset of Ω, then fj admits a subsequence converging normally on Ω.
Proof: Let KΩ be compact. By Arzela's theorem the sequence fj is totally bounded with =supK||. It follows that it has an accumulation point and has a subsequence converging to it on K. The application of Cantor's diagonal process on an exhaustion by compact subsets of Ω yields the desired subsequence.

2 Corollary If a sequence fj of real-valued 𝒞1 functions on Ω obeys: for some cΩ,

supj|fj(c)|< and supxΩ,j|fj(x)|<,

then fj has a uniformly convergent subsequence.
Proof: We want to show that the sequence satisfies the conditions in Ascoli's theorem. Let M be the second sup in the condition. Since by the mean value theorem we have:

|fj(x)||fj(x)fj(c)|+|fj(c)||xc|M+|fj(c)|,

and the hypothesis, the sup taken all over the sequence fj is finite. Using the mean value theorem again we also have: for any j and x,yΩ,

|fj(x)fj(y)||xy|M

showing the equicontinuity.

First and second countability

2 Theorem (first countability) Let E have a countable base at each point of E. Then we have the following:

  • (i) For AE, xA if and only if there is a sequence xjA such that xjx.
  • (ii) A function is continuous on E if and only if f(xj)f(x) whenever xjx.

Proof: (i) Let {Bj} be a base at x such that Bj+1Bj. If xA, then every Bj intersects A</math>. Let xjBjE. It now follows: if xG, then we find some BNG. Then xkBkBN for kN. Hence, xjx. Conversely, if x∉A, then EE is an open set containing x and no xjE. Hence, no sequence in A converges to x. That (ii) is valid follows since f(xj) is the composition of continuous functions f and x, which is again continuous. In other words, (ii) is essentially the same as (i). .

2. 2 Theorem Ω has a countable basis consisting of open sets with compact closure.
Proof: Suppose the collection of interior of closed balls Gα in Ω for a rational coordinate α. It is countable since is. It is also a basis of Ω; since if not, there exists an interior point that is isolated, and this is absurd.

2.5 Theorem In k there exists a set consisting of uncountably many components.

Usual properties we expect from calculus courses for numerical sequences to have are met like the uniqueness of limit.

2 Theorem (uncountability of the reals) The set of all real numbers is never a sequence.
Proof: See [1].

Example: Let f(x) = 0 if x is rational, f(x) = x^2 if x is irrational. Then f is continuous at 0 and nowhere else but f' exists at 0.

2 Theorem (continuous extension) Let f,g:E be continuous. If f=g on E, then f=gonE.
Proof: Let u=fg. Then clearly u is continuous on E. Since 0 is closed in , u1(0) is also closed. Hence, u=0 on E.

, and for each j, Bj={xk:kj}. Since for any subindex j(1),j(2),...,j(n), we have:

xj(n)Bj(1)Bj(2)...Bj(n).

That is to say the sequence Bj has the finite intersection property. Since E is coun

Since E is first countable, E is also sequentially countable. Hence, (a) (b).

Suppose E is not bounded, then there exists some ϵ>0 such that: for any finite set {x1,x2,...xn}E,

E⊄B(ϵ,x1)...B(ϵ,xn).

Let recursively x1E and xjE1j1B(ϵ,xk). Since d(xn,xm)>ϵ for any n, m, xj is not Cauchy. Hence, (a) implies that E is totally bounded. Also,

separated by neighborhoods

3 Theorem the following are equivalent:

  • (a) x=0 implies that x=0.
  • (b) Two points can be separated by disjoint open sets.
  • (c) Every limit is unique.

3 Theorem The separation by neighborhoods implies that a compact set K is closed in E.
Proof [2]: If K=E, then K is closed. If not, there exists a xEK. For each yK, the hypothesis says there exists two disjoint open sets A(y) containing x and B(y) containing y. The collection {B(y):yE} is an open cover of K. Since the compactness, there exists a finite subcover {B(y1),B(y2),...B(yn)}. Let A(x) = A(y1)A(y2)...A(yn). If zK, then zB(yk) for some k. Hence, z∉A(yk)A. Hence, A(x)EK. Since the finite intersection of open sets is again open, A(x) is open. Since the union of open sets is open,

EK=x∉KA(x) is open.

Seminorm

Illustrations of unit circles in different norms.

Let

G

be a linear space. The seminorm of an element in

G

is a nonnegative number, denoted by

, such that: for any

x,yG

,

  1. x0.
  2. αx=|α|x.
  3. x+yx+y. (triangular inequality)

Clearly, an absolute value is an example of a norm. Most of the times, the verification of the first two properties while whether or not the triangular inequality holds may not be so obvious. If every element in a linear space has the defined norm which is scalar in the space, then the space is said to be "normed linear space".

A liner space is a pure algebraic concept; defining norms, hence, induces topology with which we can work on problems in analysis. (In case you haven't noticed this is a book about analysis not linear algebra per se.) In fact, a normed linear space is one of the simplest and most important topological space. See the addendum for the remark on semi-norms.

An example of a normed linear space that we will be using quite often is a sequence space lp, the set of sequences xj such that

For 1p<, 1|xj|p<
For p=, xj is a bounded sequence.

In particular, a subspace of lp is said to have dimension n if in every sequence the terms after nth are all zero, and if n<, then the subspace is said to be an Euclidean space.

An open ball centered at a of radius r is the set {z:za<r}. An open set is then the union of open balls.

Continuity and convergence has close and reveling connection.

3 Theorem Let L be a seminormed space and f:ΩL. The following are equivalent:

  • (a) f is continuous.
  • (b) Let xΩ. If f(x)G, then there exists a ωΩ such that xω and f(ω)G.
  • (c) Let xΩ. For each ϵ>0, there exists a δ>0 such that
|f(y)f(x)|<ϵ whenever yΩ and |yx|<δ
  • (e) Let

Proof: Suppose (c). Since continuity, there exists a δ>0 such that:

|f(xj)f(x)|<ϵ whenever |xjx|<δ

The following special case is often useful; in particular, showing the sequence's failure to converge.

2 Theorem Let xjΩ be a sequence that converges to xΩ. Then a function f is continuous at x if and only if the sequence f(xj) converges to f(x).
Proof: The function x of j is continuous on . The theorem then follows from Theorem 1.4, which says that the composition of continuous functions is again continuous. .

2 Theorem Let uj be continuous and suppose uj converges uniformly to a function u (i.e., the sequence uju0 as j. Then u is continuous.
Proof: In short, the theorem holds since

limyxu(y)=limyxlimjuj(y)=limjlimyxuj(y)=limjuj(x)=u(x).

But more rigorously, let ϵ>0. Since the convergence is uniform, we find a N so that

uN(x)u(x)uNu<ϵ/3 for any x.

Also, since uN is continuous, we find a δ>0 so that

uN(y)uN(x)<ϵ/3 whenever |yx|<δ

It then follows that u is continuous since:

u(y)u(x)u(y)uN(y)+uN(y)uN(x)+uN(x)u(x)<ϵ

whenever |yx|<δ

2 Theorem The set of all continuous linear operators from A to B is complete if and only if B is complete.
Proof: Let uj be a Cauchy sequence of continuous linear operators from A to B. That is, if xA and x=1, then

un(x)um(x)=(unum)(x)0 as n,m

Thus, uj(x) is a Cauchy sequence in B. Since B is complete, let

u(x)=limjuj(x) for each xA.

Then u is linear since the limit operator is and also u is continuous since the sequence of continuous functions converges, if it does, to a continuous function. (FIXME: the converse needs to be shown.)

Metric spaces

We say a topological space is metric or metrizable if every open set in it is the union of open balls; that is, the set of the form {x;d(x,y)<r} with radius r and center y where d, called metric, is a real-valued function satisfying the axioms: for all x,y,z

  1. d(x,y)=d(y,x)
  2. d(x,y)+d(y,z)d(x,z)
  3. d(x,y)=0 if and only if x=y. (Identity of indiscernibles)

It follows immediately that |d(x,y)d(z,y)|d(x,y) for every x,y,z. In particular, d is never negative. While it is clear that every subset of a metric space is again a metric space with the metric restricted to the set, the converse is not necessarily true. For a counterexample, see the next chapter.

2 Theorem Let K,d) be a compact metric space. If Γ is an open cover of K, then there exists a δ>0 such that for any EK with supx,yEd(x,y)<δ E some member of Γ
Proof: Let x,y be in K, and suppose x is in some member S of Γ and y is in the complement of S. If we define a function δ by for every x

δ(x)=inf{d(x,z);zAΓ,xA},

then

δ(x)d(x,y)

The theorem thus follows if we show the inf of δ over K is positive.

2 Lemma Let K be a metric space. Then every open cover of K admits a countable subcover if and only if K is separable.
Proof: To show the direct part, fix n=1,2,... and let Γ be the collection of all open balls of radius 1n. Then Γ is an open cover of K and admits a countable subcover γ. Let En be the centers of the members of γ. Then 1En is a countable dense subset. Conversely, let Γ be an open cover of K and E be a countable dense subset of K. Since open balls of radii 1,12,13,..., the centers lying in E, form a countable base for K, each member of Γ is the union of some subsets of countably many open sets B1,B2,.... Since we may suppose that for each n Bn is contained in some GnΓ (if not remove it from the sequence) the sequence G1,... is a countable subcover of Γ of K.

Remark: For more of this with relation to cardinality see [3].

2 Theorem Let K be a metric space. Then the following are equivalent:

  • (i) K is compact.
  • (ii) Every sequence of K admits a convergent subsequence. (sequentially compact)
  • (iii) Every countable open cover of K admits a finite subcover. (countably compact)

Proof (from [4]): Throughout the proof we may suppose that K is infinite. Lemma 1.somthing says that every sequence of a infinite compact set has a limit point. Thus, the first countability shows (i) (ii). Supposing that (iii) is false, let G1,G2,... be an open cover of K such that for each n=1,2,... we can find a point xn(1nGj)c=1nGjc. It follows that xn does not have a convergent subsequence, proving (ii) (iii). Indeed, suppose it does. Then the sequence xn has a limit point p. Thus, p1Gjc, a contradiction. To show (iii) (i), in view of the preceding lemma, it suffices to show that K is separable. But this follows since if K is not separable, we can find a countable discrete subset of K, violating (iii).

Remark: The proof for (ii) (iii) does not use the fact that the space is metric.

2 Theorem A subset E of a metric space is precompact (i.e., its completion is compact) if and only if there exists a δ>0 such that E is contained in the union of finitely many open balls of radius δ with the centers lying in E.

Thus, the notion of relatively compactness and precompactness coincides for complete metric spaces.

2 Theorem (Heine-Borel) If E is a subset of n, then the following are equivalent.

  • (i) E is closed and bounded.
  • (ii) E is compact.
  • (iii) Every infinite subset of E contains some of its limit points.

2 Theorem Every metric space is perfectly and fully normal.

2 Corollary Every metric space is normal and Hausdorff.

2 Theorem If xjx in a metric space X implies f(xn)f(x), then f is continuous.
Proof: Let EX and x the closure of E. Then there exists a xjEx as j as j. Thus, by assumption f(xj)f(x), and this means that f(x) is in the closure of f(E). We conclude: f(E)f(E).

2 Theorem Suppose that f:(X1,d1)(X2,d2) is continuous and satisfies

d2(f(x),f(y))d1(x,y) for all x,y

If FX2 and (F,d2) is a complete metric space, then f(F) is closed.
Proof: Let xn be a sequence in F such that limn,md2(f(xn),f(xm))=0. Then by hypothesis limn,md1(xn,xm)=0. Since F is complete in d2, the limit y=limnxn exists. Finally, since f is continuous, limnd2(f(xn),f(y))=0, completing the proof.

Measure

Cantor set
Cantor set

A measure, which we shall denote by μ throughout this section, is a function from a delta-ring to the set of nonnegative real numbers and such that μ is countably additive; i.e., μ(1Ej)=1μ(Ej) for Ej distinct. We shall define an integral in terms of a measure.

4.1 Theorem A measure μ has the following properties: given measurable sets A and B such that AB,

  • (1) μ(BA)=μ(B)μ(A).
  • (2) μ()=0.
  • (3) μ(A)μ(B) where the equality holds for A=B. (monotonicity)

Proof: (1) μ(B)μ(A)=μ(BAA)μ(A)=μ(BA)+μ(A)μ(A). (2) μ()=μ(AA)=μ(A)μ(A).(3)μ(B)μ(A)=μ(BA)0 since measures are always nonnegative. Also, μ(B)μ(A)=0 if A=B since (2).

One example would be a counting measure; i.e., μE = the number of elements in a finite set E. Indeed, every finite set is measurable since the countable union and countable intersection of finite sets is again finite. To give another example, let x0 be fixed, and μ(E)=1 if x0 is in E and 0 otherwise. The measure μ is indeed countably additive since for the sequence Ej arbitrary, μ(0Ej)=1=μ(Ej) for some j if x0Ej and 0 otherwise.

We now study the notion of geometric convexity.

2 Lemma

x+y2=a(ax+y2+(1a)y)+(1a)(ax+(1a)x+y2) for any x,y.

2 Theorem Let D be a convex subset of a vector space. If 0<a<1 and f(ax+(1a)y)af(x)+(1a)f(y) for x,yD, then

f(x+y2)f(x)+f(y)2 for x,yD

Proof: From the lemma we have:

2a(1a)f(x+y2)a(1a)(f(x)+f(y))

Let be a collection of subsets of a set G. We say is a delta-ring if has the empty set, G and every countable union and countable intersection of members of . a member of is called a measurable set and G a measure space, by analogy to a topological space and open sets in it.

2 Theorem (Hölder's inequality) For 1p, if 1/p+1/q=1, then

|fg|dμ(|f|pdμ)1/p(|g|qdμ)1/q

Proof: By replacing f and g with |f| and |g|, respectively, we may assume that f and g are non-negative. Let C=gqdμ. If C=0, then the inequality is obvious. Suppose not, and let dν=gqdμC. Then, since xp is increasing and convex for x0, by Jensen's inequality,

fgdμ=fg(1q)dνC(fpgqdν)(1/p)C.

Since 1/q=11/p, this is the desired inequality.

4 Corollary (Minkowski's inequality) Let p1 and p=(||p)1/p. If f0 and f(x,y)dxdy<, then

f(,y)dypf(,y)pdy

Proof (from [5]): If p=1, then the inequality is the same as Fubini's theorem. If p>1,

|f(x,y)dy|pdx =|f(x,y)dy|p1f(x,z)dzdx
(Fubini) =|f(x,y)dy|p1f(x,z)dxdz
(Hölder) (|f(x,y)dy|(p1)qdx)1/qf(,z)pdz

By division we get the desired inequality, noting that (p1)q=p and 11q=1p.

TODO: We can probably simplify the proof by appealing to Jensen's inequality instead of Hölder's.

Remark: by replacing dy by a counting measure we get:

f+gpfp+gp

under the same assumption and notation.

2 Lemma For 1p and a,b>0, if 1/p+1/q=1,

a1pb1q=inft>0(1pt1qa+1qt1pb)

Proof: Let f(t)=1pt1qa+1qt1pb for t>0. Then the derivative

f(t)=1pq(t1pa+t1qb)

becomes zero only when t=a/b. Thus, the minimum of f is attained there and is equal to a1pb1q.

When t is taken to 1, the inequality is known as Young's inequality.

2 Theorem (Hölder's inequality) For 1p, if 1/p+1/q=1, then

|fg|(|f|p)1/p(|f|q)1/q

Proof: If p=, the inequality is clear. By the application of the preceding lemma we have: for any t>0

|f|1p|g|1q1pt1q|f|+1qt1p|g|

Taking the infimum we see that the right-hand side becomes:

(|f|)1/p(|g|)1/q

The convex hull in Ω of a compact set K, denoted by K^ is:

{zΩ:|f(z)|supK|f| for f analytic in Ω}.

When f is linear (besides being analytic), we say the K^ is geometrically convex hull. That K is compact ensures that the definition is meaningful.

Theorem The closure of the convex hull of E in G is the intersection if all half-spaces containing E.
Proof: Let F = the collection of half-spaces containing E. Then co(E)F since each-half space in F is closed and convex. Yet, if x∉co(E), then there exists a half-space H containing E which however does not contain x. Hence, co(E)=F.

Let Ωn. A function f:Ωn is said to be convex if

supK|fh|=supbK|fh|.

for some KΩ compact and any h harmonic on K and continuous on K.

Theorem The following are equivalent:

  • (a) f is convex on some K compact.
  • (b) if [a,b]K, then
    f(λa+(1λ)b)λf(a)+(1λ)f(b) for λ[0,1].
  • (c) The difference quotient
    f(x+h)f(x)h increases as h does.
  • (d) f is measurable and we have:
    f(a+b2)a+b2 for any [a,b]K.
  • (e) The set {(x,y):x[a,b],f(x)y} is convex for [a,b]K.

Proof: Suppose (a). For each x[a,b], there exists some λ[0,1] such that x=λa+(1λ)b. Let A(x)=A(λa+(1λ)b)=λf(a)+(1λ)f(b), and then since b[a,b]={a,b} and (fA)(a)=0=(fA)(b),

|f(x)|=|f(x)A(x)+A(x)| sup{|f(a)A(a)|,|f(b)A(b)|}+A(x)
=λf(a)+(1λ)f(b)

Thus, (a) (b). Now suppose (b). Since λ=xaba, for λ(0,1), (b) says:

(bx)f(x)+(xa)f(x) (bx)f(a)+(xa)f(b)
f(a)f(x)ax f(b)f(x)bx

Since ax<bx, we conclude (b) (c). Suppose (c). The continuity follows since we have:

limh>0,h0f(x+h)=f(x)+limh>0,h0hf(x+h)f(x)h.

Also, let x=21(a+b) such that a<b, for a,bK. Then we have:

f(a)f(x)ax f(b)f(x)bx
f(x)f(a) f(b)f(x)
f(a+b2) f(a)+f(b)2

Thus, (c) (d). Now suppose (d), and let E={(x,y):x[a,b],yf(x)}. First we want to show

f(12n12nxj)12n12nf(xj).

If n=0, then the inequality holds trivially. if the inequality holds for some n1, then

f(12n12nxj) =f(12(12n112n1xj+12n112n1x2n1+j))
12f(12n112n1xj)+12f(12n112n1x2n1+j)
12n12n1f(xj)+12n12n1f(x2n1+j)
=12n12nf(xj)

Let x1,x2[a,b] and λ[0,1]. There exists a sequence of rationals number such that:

limjpj2qj=λ.

It then follows that:

f(λx1+(1λ)x2) limjpj2qjf(x1)+(1pj2qj)f(x2)
=λf(x1)+(1λ)f(x2)
λy1+(1λ)y2.

Thus, (d) (e). Finally, suppose (e); that is, E is convex. Also suppose K is an interval for a moment. Then

f(λa+(1λ)b)λf(a)+(1λ)f(b).

4. Corollary (inequality between geometric and arithmetic means)

1najλj1nλjaj if aj0, λj0 and 1nλj=1.

Proof: If some aj=0, then the inequality holds trivially; so, suppose aj>0. The function ex is convex since its second derivative, again ex, is >0. It thus follows:

1najλj=e1nλjlog(aj)1nλjelog(aj)=1nλjaj.

The convex hull of a finite set E is said to be a convex polyhedron. Clearly, the set of the extremely points is the subset of E.

4 Theorem (general Hölder's inequality) If ajk>0 and pj>1 for j=1,...nj and k=1,...nk and 1njpj=1, then:

k=1nkj=1njajkj=1nk(k=1nkajkpj)1/p (Hörmander 11)

Proof: Let Aj=(kajkpj)1/pj.

4. Theorem A convex polyhedron is the intersection of a finite number of closed half-spaces.
Proof: Use induction.

4. Theorem The convex hull of a compact set is compact.
Proof: Let f(λ1,λ2,...λn,x1,x2,...xn)=1nλjxj. Then f is continuous since it is the finite sum of continuous functions λjxj. Since the intersection of compact sets is compact and

co(K)=n=1,2,...f(λ1,λ2,...λn,x1,x2,...xn),

co(K) is compact.

Example: Let p(z)=(zs1)(zs2)...(zsn). Then the derivative of p has zeros in co({s1,s2,...sn}).

Addendum

  1. Show the following are equivalent with assuming that K is second countable.
    • (1) K is compact.
    • (2) Every countable open cover of K admits a finite subcover (countably compact)
    • (3) K is sequentially compact.

Nets are, so to speak, generalized sequences.

  1. Show the following are equivalent with assuming Axiom of Choice:
    • (1) K is compact; i.e, every open cover admits a finite subcover.
    • (2) In K every net has a convergent subnet.
    • (3) In K every ultrafilter has an accumulation point (Bourbaki compact)
    • (4) There is a subbase τ for K such that every open cover that is a subcollection of τ admits a finite subcover. (subbase compact)
    • (5) Every nest of non-empty closed sets has a non-empty intersection (linearly compact) (Hint: use the contrapositive of this instead)
    • (6) Every infinite subset of K has a complete accumulation point (Alexandroff-Urysohn compact)

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