The Banach Algebra of Borel Measures on Euclidean Space

This blog post is intended to deliver a quick explanation of the algebra of Borel measures on \(\mathbb{R}^n\). It will be broken into pieces. All complex-valued complex Borel measures \(M(\mathbb{R}^n)\) clearly form a vector space over \(\mathbb{C}\). The main goal of this post is to show that this is a Banach space and also a Banach algebra.

In fact the \(\mathbb{R}^n\) case can be generalised into any locally compact abelian group (see any abstract harmonic analysis books), this is because what really matters here is being locally compact and abelian. But at this moment we stick to Euclidean spaces. Note since \(\mathbb{R}^n\) is \(\sigma\)-compact, all Borel measures are regular.

To read this post you need to be familiar with some basic properties of Banach algebra, complex Borel measures, and the most important, Fubini's theorem.

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Left Shift Semigroup and Its Infinitesimal Generator

Left shift operator

Throughout we consider the Hilbert space \(L^2=L^2(\mathbb{R})\), the space of all complex-valued functions with real variable such that \(f \in L^2\) if and only if \[ \lVert f \rVert_2^2=\int_{-\infty}^{\infty}|f(t)|^2dm(t)<\infty \] where \(m\) denotes the ordinary Lebesgue measure (in fact it's legitimate to consider Riemann integral in this context).

For each \(t \geq 0\), we assign an bounded linear operator \(Q(t)\) such that \[ (Q(t)f)(s)=f(s+t). \] This is indeed bounded since we have \(\lVert Q(t)f \rVert_2 = \lVert f \rVert_2\) as the Lebesgue measure is translate-invariant. This is a left translation operator with a single step \(t\).

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Several ways to prove Hardy's inequality

Suppose \(1 < p < \infty\) and \(f \in L^p((0,\infty))\) (with respect to Lebesgue measure of course) is a nonnegative function, take \[ F(x) = \frac{1}{x}\int_0^x f(t)dt \quad 0 < x <\infty, \] we have Hardy's inequality \(\def\lrVert[#1]{\lVert #1 \rVert}\) \[ \lrVert[F]_p \leq q\lrVert[f]_p \] where \(\frac{1}{p}+\frac{1}{q}=1\) of course.

There are several ways to prove it. I think there are several good reasons to write them down thoroughly since that may be why you find this page. Maybe you are burnt out since it's left as exercise. You are assumed to have enough knowledge of Lebesgue measure and integration.

Minkowski's integral inequality

Let \(S_1,S_2 \subset \mathbb{R}\) be two measurable set, suppose \(F:S_1 \times S_2 \to \mathbb{R}\) is measurable, then \[ \left[\int_{S_2} \left\vert\int_{S_1}F(x,y)dx \right\vert^pdy\right]^{\frac{1}{p}} \leq \int_{S_1} \left[\int_{S_2} |F(x,y)|^p dy\right]^{\frac{1}{p}}dx. \] A proof can be found at here by turning to Example A9. You may need to replace all measures with Lebesgue measure \(m\).

Now let's get into it. For a measurable function in this place we should have \(G(x,t)=\frac{f(t)}{x}\). If we put this function inside this inequality, we see \[ \begin{aligned} \lrVert[F]_p &= \left[\int_0^\infty \left\vert \int_0^x \frac{f(t)}{x}dt \right\vert^p dx\right]^{\frac{1}{p}} \\ &= \left[\int_0^\infty \left\vert \int_0^1 f(ux)du \right\vert^p dx\right]^{\frac{1}{p}} \\ &\leq \int_0^1 \left[\int_0^\infty |f(ux)|^pdx\right]^{\frac{1}{p}}du \\ &= \int_0^1 \left[\int_0^\infty |f(ux)|^pudx\right]^{\frac{1}{p}}u^{-\frac{1}{p}}du \\ &= \lrVert[f]_p \int_0^1 u^{-\frac{1}{p}}du \\ &=q\lrVert[f]_p. \end{aligned} \] Note we have used change-of-variable twice and the inequality once.

A constructive approach

I have no idea how people came up with this solution. Take \(xF(x)=\int_0^x f(t)t^{u}t^{-u}dt\) where \(0<u<1-\frac{1}{p}\). Hölder's inequality gives us \[ \begin{aligned} xF(x) &= \int_0^x f(t)t^ut^{-u}dt \\ &\leq \left[\int_0^x t^{-uq}dt\right]^{\frac{1}{q}}\left[\int_0^xf(t)^pt^{up}dt\right]^{\frac{1}{p}} \\ &=\left(\frac{1}{1-uq}x^{1-uq}\right)^{\frac{1}{q}}\left[\int_0^xf(t)^pt^{up}dt\right]^{\frac{1}{p}} \end{aligned} \] Hence \[ \begin{aligned} F(x)^p & \leq \frac{1}{x^p}\left\{\left(\frac{1}{1-uq}x^{1-uq}\right)^{\frac{1}{q}}\left[\int_0^xf(t)^pt^{up}dt\right]^{\frac{1}{p}}\right\}^{p} \\ &= \left(\frac{1}{1-uq}\right)^{\frac{p}{q}}x^{\frac{p}{q}(1-uq)-p}\int_0^x f(t)^pt^{up}dt \\ &= \left(\frac{1}{1-uq}\right)^{p-1}x^{-up-1}\int_0^x f(t)^pt^{up}dt \end{aligned} \]

Note we have used the fact that \(\frac{1}{p}+\frac{1}{q}=1 \implies p+q=pq\) and \(\frac{p}{q}=p-1\). Fubini's theorem gives us the final answer: \[ \begin{aligned} \int_0^\infty F(x)^pdx &\leq \int_0^\infty\left[\left(\frac{1}{1-uq}\right)^{p-1}x^{-up-1}\int_0^x f(t)^pt^{up}dt\right]dx \\ &=\left(\frac{1}{1-uq}\right)^{p-1}\int_0^\infty dx\int_0^x f(t)^pt^{up}x^{-up-1}dt \\ &=\left(\frac{1}{1-uq}\right)^{p-1}\int_0^\infty dt\int_t^\infty f(t)^pt^{up}x^{-up-1}dx \\ &=\left(\frac{1}{1-uq}\right)^{p-1}\frac{1}{up}\int_0^\infty f(t)^pdt. \end{aligned} \] It remains to find the minimum of \(\varphi(u) = \left(\frac{1}{1-uq}\right)^{p-1}\frac{1}{up}\). This is an elementary calculus problem. By taking its derivative, we see when \(u=\frac{1}{pq}<1-\frac{1}{p}\) it attains its minimum \(\left(\frac{p}{p-1}\right)^p=q^p\). Hence we get \[ \int_0^\infty F(x)^pdx \leq q^p\int_0^\infty f(t)^pdt, \] which is exactly what we want. Note the constant \(q\) cannot be replaced with a smaller one. We simply proved the case when \(f \geq 0\). For the general case, one simply needs to take absolute value.

Integration by parts

This approach makes use of properties of \(L^p\) space. Still we assume that \(f \geq 0\) but we also assume \(f \in C_c((0,\infty))\), that is, \(f\) is continuous and has compact support. Hence \(F\) is differentiable in this situation. Integration by parts gives \[ \int_0^\infty F^p(x)dx=xF(x)^p\vert_0^\infty- p\int_0^\infty xdF^p = -p\int_0^\infty xF^{p-1}(x)F'(x)dx. \] Note since \(f\) has compact support, there are some \([a,b]\) such that \(f >0\) only if \(0 < a \leq x \leq b < \infty\) and hence \(xF(x)^p\vert_0^\infty=0\). Next it is natural to take a look at \(F'(x)\). Note we have \[ F'(x) = \frac{f(x)}{x}-\frac{\int_0^x f(t)dt}{x^2}, \] hence \(xF'(x)=f(x)-F(x)\). A substitution gives us \[ \int_0^\infty F^p(x)dx = -p\int_0^\infty F^{p-1}(x)[f(x)-F(x)]dx, \] which is equivalent to say \[ \int_0^\infty F^p(x)dx = \frac{p}{p-1}\int_0^\infty F^{p-1}(x)f(x)dx. \] Hölder's inequality gives us \[ \begin{aligned} \int_0^\infty F^{p-1}(x)f(x)dx &\leq \left[\int_0^\infty F^{(p-1)q}(x)dx\right]^{\frac{1}{q}}\left[\int_0^\infty f(x)^pdx\right]^{\frac{1}{p}} \\ &=\left[\int_0^\infty F^{p}(x)dx\right]^{\frac{1}{q}}\left[\int_0^\infty f(x)^pdx\right]^{\frac{1}{p}}. \end{aligned} \] Together with the identity above we get \[ \int_0^\infty F^p(x)dx = q\left[\int_0^\infty F^{p}(x)dx\right]^{\frac{1}{q}}\left[\int_0^\infty f(x)^pdx\right]^{\frac{1}{p}} \] which is exactly what we want since \(1-\frac{1}{q}=\frac{1}{p}\) and all we need to do is divide \(\left[\int_0^\infty F^pdx\right]^{1/q}\) on both sides. So what's next? Note \(C_c((0,\infty))\) is dense in \(L^p((0,\infty))\). For any \(f \in L^p((0,\infty))\), we can take a sequence of functions \(f_n \in C_c((0,\infty))\) such that \(f_n \to f\) with respect to \(L^p\)-norm. Taking \(F=\frac{1}{x}\int_0^x f(t)dt\) and \(F_n = \frac{1}{x}\int_0^x f_n(t)dt\), we need to show that \(F_n \to F\) pointwise, so that we can use Fatou's lemma. For \(\varepsilon>0\), there exists some \(m\) such that \(\lrVert[f_n-f]_p < \frac{1}{n}\). Thus \[ \begin{aligned} |F_n(x)-F(x)| &= \frac{1}{x}\left\vert \int_0^x f_n(t)dt - \int_0^x f(t)dt \right\vert \\ &\leq \frac{1}{x} \int_0^x |f_n(t)-f(t)|dt \\ &\leq \frac{1}{x} \left[\int_0^x|f_n(t)-f(t)|^pdt\right]^{\frac{1}{p}}\left[\int_0^x 1^qdt\right]^{\frac{1}{q}} \\ &=\frac{1}{x^{1/p}}\left[\int_0^x|f_n(t)-f(t)|^pdt\right]^{\frac{1}{p}} \\ &\leq \frac{1}{x^{1/p}}\lrVert[f_n-f]_p <\frac{\varepsilon}{x^{1/p}}. \end{aligned} \] Hence \(F_n \to F\) pointwise, which also implies that \(|F_n|^p \to |F|^p\) pointwise. For \(|F_n|\) we have \[ \begin{aligned} \int_0^\infty |F_n(x)|^pdx &= \int_0^\infty \left\vert\frac{1}{x}\int_0^x f_n(t)dt\right\vert^p dx \\ &\leq \int_0^\infty \left[\frac{1}{x}\int_0^x |f_n(t)|dt\right]^{p}dx \\ &\leq q\int_0^\infty |f_n(t)|^pdt \end{aligned} \] note the third inequality follows since we have already proved it for \(f \geq 0\). By Fatou's lemma, we have \[ \begin{aligned} \int_0^\infty |F(x)|^pdx &= \int_0^\infty \lim_{n \to \infty}|F_n(x)|^pdx \\ &\leq \lim_{n \to \infty} \int_0^\infty |F_n(x)|^pdx \\ &\leq \lim_{n \to \infty}q^p\int_0^\infty |f_n(x)|^pdx \\ &=q^p\int_0^\infty |f(x)|^pdx. \end{aligned} \]

A Continuous Function Sending L^p Functions to L^1

Throughout, let \((X,\mathfrak{M},\mu)\) be a measure space where \(\mu\) is positive.

The question

If \(f\) is of \(L^p(\mu)\), which means \(\lVert f \rVert_p=\left(\int_X |f|^p d\mu\right)^{1/p}<\infty\), or equivalently \(\int_X |f|^p d\mu<\infty\), then we may say \(|f|^p\) is of \(L^1(\mu)\). In other words, we have a function \[ \begin{aligned} \lambda: L^p(\mu) &\to L^1(\mu) \\ f &\mapsto |f|^p. \end{aligned} \] This function does not have to be one to one due to absolute value. But we hope this function to be fine enough, at the very least, we hope it is continuous.

Here, \(f \sim g\) means that \(f-g\) equals to \(0\) almost everywhere with respect to \(\mu\). It can be easily verified that this is a equivalence relation.

Continuity

We still use \(\varepsilon-\delta\) argument but it's in a metric space. Suppose \((X,d_1)\) and \((Y,d_2)\) are two metric spaces and \(f:X \to Y\) is a function. We say \(f\) is continuous at \(x_0 \in X\) if for any \(\varepsilon>0\), there exists some \(\delta>0\) such that \(d_2(f(x_0),f(x))<\varepsilon\) whenever \(d_1(x_0,x)<\delta\). Further, we say \(f\) is continuous on \(X\) if \(f\) is continuous at every point \(x \in X\).

Metrics

For \(1\leq p<\infty\), we already have a metric by \[ d(f,g)=\lVert f-g \rVert_p \] given that \(d(f,g)=0\) if and only if \(f \sim g\). This is complete and makes \(L^p\) a Banach space. But for \(0<p<1\) (yes we are going to cover that), things are much more different, and there is one reason: Minkowski inequality holds reversely! In fact we have \[ \lVert f+g \rVert_p \geq \lVert f \rVert_p + \lVert g \rVert_p \] for \(0<p<1\). In fact, \(L^p\) space has too many weird things when \(0<p<1\). Precisely,

For \(0<p<1\), \(L^p(\mu)\) is locally convex if and only if \(\mu\) assumes finitely many values. (Proof.)

On the other hand, for example, \(X=[0,1]\) and \(\mu=m\) be the Lebesgue measure, then \(L^p(\mu)\) has no open convex subset other than \(\varnothing\) and \(L^p(\mu)\) itself. However,

A topological vector space \(X\) is normable if and only if its origin has a convex bounded neighbourhood. (See Kolmogorov's normability criterion.)

Therefore \(L^p(m)\) is not normable, hence not Banach.

We have gone too far. We need a metric that is fine enough.

Metric of \(L^p\) when \(0<p<1\)

In this subsection we always have \(0<p<1\).

Define \[ \Delta(f)=\int_X |f|^p d\mu \] for \(f \in L^p(\mu)\). We will show that we have a metric by \[ d(f,g)=\Delta(f-g). \] Fix \(y\geq 0\), consider the function \[ f(x)=(x+y)^p-x^p. \] We have \(f(0)=y^p\) and \[ f'(x)=p(x+y)^{p-1}-px^{p-1} \leq px^{p-1}-px^{p-1}=0 \] when \(x > 0\) and hence \(f(x)\) is nonincreasing on \([0,\infty)\), which implies that \[ (x+y)^p \leq x^p+y^p. \] Hence for any \(f\), \(g \in L^p\), we have \[ \Delta(f+g)=\int_X |f+g|^p d\mu \leq \int_X |f|^p d\mu + \int_X |g|^p d\mu=\Delta(f)+\Delta(g). \] This inequality ensures that \[ d(f,g)=\Delta(f-g) \] is a metric. It's immediate that \(d(f,g)=d(g,f) \geq 0\) for all \(f\), \(g \in L^p(\mu)\). For the triangle inequality, note that \[ d(f,h)+d(g,h)=\Delta(f-h)+\Delta(h-g) \geq \Delta((f-h)+(h-g))=\Delta(f-g)=d(f,g). \] This is translate-invariant as well since \[ d(f+h,g+h)=\Delta(f+h-g-h)=\Delta(f-g)=d(f,g) \] The completeness can be verified in the same way as the case when \(p>1\). In fact, this metric makes \(L^p\) a locally bounded F-space.

The continuity of \(\lambda\)

The metric of \(L^1\) is defined by \[ d_1(f,g)=\lVert f-g \rVert_1=\int_X |f-g|d\mu. \] We need to find a relation between \(d_p(f,g)\) and \(d_1(\lambda(f),\lambda(g))\), where \(d_p\) is the metric of the corresponding \(L^p\) space.

\(0<p<1\)

As we have proved, \[ (x+y)^p \leq x^p+y^p. \] Without loss of generality we assume \(x \geq y\) and therefore \[ x^p=(x-y+y)^p \leq (x-y)^p+y^p. \] Hence \[ x^p-y^p \leq (x-y)^p. \] By interchanging \(x\) and \(y\), we get \[ |x^p-y^p| \leq |x-y|^p. \] Replacing \(x\) and \(y\) with \(|f|\) and \(|g|\) where \(f\), \(g \in L^p\), we get \[ \int_{X}\lvert |f|^p-|g|^p \rvert d\mu \leq \int_X |f-g|^p d\mu. \] But \[ d_1(\lambda(f),\lambda(g))=\int_{X}\lvert |f|^p-|g|^p \rvert d\mu \\ d_p(f,g)=\Delta(f-g)= d\mu \leq \int_X |f-g|^p d\mu \] and we therefore have \[ d_1(\lambda(f),\lambda(g)) \leq d_p(f,g). \] Hence \(\lambda\) is continuous (and in fact, Lipschitz continuous and uniformly continuous) when \(0<p<1\).

\(1 \leq p < \infty\)

It's natural to think about Minkowski's inequality and Hölder's inequality in this case since they are critical inequality enablers. You need to think about some examples of how to create the condition to use them and get a fine result. In this section we need to prove that \[ |x^p-y^p| \leq p|x-y|(x^{p-1}+y^{p-1}). \] This inequality is surprisingly easy to prove however. We will use nothing but the mean value theorem. Without loss of generality we assume that \(x > y \geq 0\) and define \(f(t)=t^p\). Then \[ \frac{f(x)-f(y)}{x-y}=f'(\zeta)=p\zeta^{p-1} \] where \(y < \zeta < x\). But since \(p-1 \geq 0\), we see \(\zeta^{p-1} < x^{p-1} <x^{p-1}+y^{p-1}\). Therefore \[ f(x)-f(y)=x^p-y^p=p(x-y)\zeta^{p-1}<p(x-y)(x^{p-1}-y^{p-1}). \] For \(x=y\) the equality holds.


Therefore \[ \begin{aligned} d_1(\lambda(f),\lambda(g)) &= \int_X \left||f|^p-|g|^p\right|d\mu \\ &\leq \int_Xp\left||f|-|g|\right|(|f|^{p-1}+|g|^{p-1})d\mu \end{aligned} \] By Hölder's inequality, we have \[ \begin{aligned} \int_X ||f|-|g||(|f|^{p-1}+|g|^{p-1})d\mu & \leq \left[\int_X \left||f|-|g|\right|^pd\mu\right]^{1/p}\left[\int_X\left(|f|^{p-1}+|g|^{p-1}\right)^q\right]^{1/q} \\ &\leq \left[\int_X \left|f-g\right|^pd\mu\right]^{1/p}\left[\int_X\left(|f|^{p-1}+|g|^{p-1}\right)^q\right]^{1/q} \\ &=\lVert f-g \rVert_p \left[\int_X\left(|f|^{p-1}+|g|^{p-1}\right)^q\right]^{1/q}. \end{aligned} \] By Minkowski's inequality, we have \[ \left[\int_X\left(|f|^{p-1}+|g|^{p-1}\right)^q\right]^{1/q} \leq \left[\int_X|f|^{(p-1)q}d\mu\right]^{1/q}+\left[\int_X |g|^{(p-1)q}d\mu\right]^{1/q} \] Now things are clear. Since \(1/p+1/q=1\), or equivalently \(1/q=(p-1)/p\), suppose \(\lVert f \rVert_p\), \(\lVert g \rVert_p \leq R\), then \((p-1)q=p\) and therefore \[ \left[\int_X|f|^{(p-1)q}d\mu\right]^{1/q}+\left[\int_X |g|^{(p-1)q}d\mu\right]^{1/q} = \lVert f \rVert_p^{p-1}+\lVert g \rVert_p^{p-1} \leq 2R^{p-1}. \] Summing the inequalities above, we get \[ \begin{aligned} d_1(\lambda(f),\lambda(g)) \leq 2pR^{p-1}\lVert f-g \rVert_p =2pR^{p-1}d_p(f,g) \end{aligned} \] hence \(\lambda\) is continuous.

Conclusion and further

We have proved that \(\lambda\) is continuous, and when \(0<p<1\), we have seen that \(\lambda\) is Lipschitz continuous. It's natural to think about its differentiability afterwards, but the absolute value function is not even differentiable so we may have no chance. But this is still a fine enough result. For example we have no restriction to \((X,\mathfrak{M},\mu)\) other than the positivity of \(\mu\). Therefore we may take \(\mathbb{R}^n\) as the Lebesgue measure space here, or we can take something else.

It's also interesting how we use elementary Calculus to solve some much more abstract problems.

A proof of the ordinary Gleason-Kahane-Żelazko theorem for complex functionals

The Theorem

(Gleason-Kahane-Żelazko) If \(\phi\) is a complex linear functional on a unitary Banach algebra \(A\), such that \(\phi(e)=1\) and \(\phi(x) \neq 0\) for every invertible \(x \in A\), then \[ \phi(xy)=\phi(x)\phi(y) \] Namely, \(\phi\) is a complex homomorphism.

Notations and remarks

Suppose \(A\) is a complex unitary Banach algebra and \(\phi: A \to \mathbb{C}\) is a linear functional which is not identically \(0\) (for convenience), and if \[ \phi(xy)=\phi(x)\phi(y) \] for all \(x \in A\) and \(y \in A\), then \(\phi\) is called a complex homomorphism on \(A\). Note that a unitary Banach algebra (with \(e\) as multiplicative unit) is also a ring, so is \(\mathbb{C}\), we may say in this case \(\phi\) is a ring-homomorphism. For such \(\phi\), we have an instant proposition:

Proposition 0 \(\phi(e)=1\) and \(\phi(x) \neq 0\) for every invertible \(x \in A\).

Proof. Since \(\phi(e)=\phi(ee)=\phi(e)\phi(e)\), we have \(\phi(e)=0\) or \(\phi(e)=1\). If \(\phi(e)=0\) however, for any \(y \in A\), we have \(\phi(y)=\phi(ye)=\phi(y)\phi(e)=0\), which is an excluded case. Hence \(\phi(e)=1\).

For invertible \(x \in A\), note that \(\phi(xx^{-1})=\phi(x)\phi(x^{-1})=\phi(e)=1\). This can't happen if \(\phi(x)=0\). \(\square\)

The theorem reveals that Proposition \(0\) actually characterizes the complex homomorphisms (ring-homomorphisms) among the linear functionals (group-homomorphisms).

This theorem was proved by Andrew M. Gleason in 1967 and later independently by J.-P. Kahane and W. Żelazko in 1968. Both of them worked mainly on commutative Banach algebras, and the non-commutative version, which focused on complex homomorphism, was by W. Żelazko. In this post we will follow the third one.

Unfortunately, one cannot find an educational proof on the Internet with ease, which may be the reason why I write this post and why you read this.

Equivalences

Following definitions of Banach algebra and some logic manipulation, we have several equivalences worth noting.

Subspace and ideal version

(Stated by Gleason) Let \(M\) be a linear subspace of codimension one in a commutative Banach algebra \(A\) having an identity. Suppose no element of \(M\) is invertible, then \(M\) is an ideal.

(Stated by Kahane and Żelazko) A subspace \(X \subset A\) of codimension \(1\) is a maximal ideal if and only if it consists of non-invertible elements.

Spectrum version

(Stated by Kahane and Żelazko) Let \(A\) be a commutative complex Banach algebra with unit element. Then a functional \(f \in A^\ast\) is a multiplicative linear functional if and only if \(f(x)=\sigma(x)\) holds for all \(x \in A\).

Here \(\sigma(x)\) denotes the spectrum of \(x\).

The connection

Clearly any maximal ideal contains no invertible element (if so, then it contains \(e\), then it's the ring itself). So it suffices to show that it has codimension 1, and if it consists of non-invertible elements. Also note that every maximal ideal is the kernel of some complex homomorphism. For such a subspace \(X \subset A\), since \(e \notin X\), we may define \(\phi\) so that \(\phi(e)=1\), and \(\phi(x) \in \sigma(x)\) for all \(x \in A\). Note that \(\phi(e)=1\) holds if and only if \(\phi(x) \in \sigma(x)\). As we will show, \(\phi\) has to be a complex homomorphism.

Tools to prove the theorem

Lemma 0 Suppose \(A\) is a unitary Banach algebra, \(x \in A\), \(\lVert x \rVert<1\), then \(e-x\) is invertible.

This lemma can be found in any functional analysis book introducing Banach algebra.

Lemma 1 Suppose \(f\) is an entire function of one complex variable, \(f(0)=1\), \(f'(0)=0\), and \[ 0<|f(\lambda)| \leq e^{|\lambda|} \] for all complex \(\lambda\), then \(f(\lambda)=1\) for all \(\lambda \in \mathbb{C}\).

Note that there is an entire function \(g\) such that \(f=\exp(g)\). It can be shown that \(g=0\). Indeed, if we put \[ h_r(\lambda) = \frac{r^2g(\lambda)}{\lambda^2[2r-g(\lambda)]} \] then we see \(h_r\) is holomorphic in the open disk centred at \(0\) with radius \(2r\). Besides, \(|h_r(\lambda)| \leq 1\) if \(|\lambda|=r\). By the maximum modulus theorem, we have \[ |h_r(\lambda)| \leq 1 \] whenever \(|\lambda| \leq r\). Fix \(\lambda\) and let \(r \to \infty\), by definition of \(h_r(\lambda)\), we must have \(g(\lambda)=0\).

Jordan homomorphism

A mapping \(\phi\) from one ring \(R\) to another ring \(R'\) is said to be a Jordan homomorphism from \(R\) to \(R'\) if \[ \phi(a+b)=\phi(a)+\phi(b) \] and \[ \phi(ab+ba)=\phi(a)\phi(b)+\phi(b)\phi(a). \] It's of course clear that every homomorphism is Jordan. Note if \(R'\) is not of characteristic \(2\), the second identity is equivalent to \[ \phi(a^2)=\phi(a)^2. \] To show the equivalence, one let \(b=a\) in the first case and puts \(a+b\) in place of \(a\) in the second case.

Since in this case \(R=A\) and \(R'=\mathbb{C}\), the latter of which is commutative, we also write \[ \phi(ab+ba)=2\phi(a)\phi(b). \] As we will show, the \(\phi\) in the theorem is a Jordan homomorphism.

The proof

We will follow an unusual approach. By keep 'downgrading' the goal, one will see this algebraic problem be transformed into a pure analysis problem neatly.

To begin with, let \(N\) be the kernel of \(\phi\).

Step 1 - It suffices to prove that \(\phi\) is a Jordan homomorphism

If \(\phi\) is a complex homomorphism, it is immediate that \(\phi\) is a Jordan homomorphism. Conversely, if \(\phi\) is Jordan, we have \[ \phi(xy+yx) =2\phi(x)\phi(y). \] If \(x\in N\), the right hand becomes \(0\), and therefore \[ xy+yx \in N \quad \text{if } x \in N, y \in A. \] Consider the identity \[ (xy-yx)^2+(xy+yx)^2=2[x(yxy)+(yxy)x] \]

Therefore \[ \begin{aligned} \phi((xy-yx)^2+(xy+yx)^2)&=\phi((xy-yx)^2)+\phi((xy+yx)^2) \\ &=\phi(xy-yx)^2+\phi(xy+yx)^2 \\ &= \phi(xy-yx)^2 \\ &=2\phi[x(yxy)+(yxy)x] \\ &=0 \end{aligned} \] Since \(x \in N\) and \(yxy \in A\), we see \(x(yxy)+(yxy)x \in N\). Therefore \(\phi(xy-yx)=0\) and \[ xy-yx \in N \] if \(x \in N\) and \(y \in A\). Further we see \[ xy-yx+xy+yx=2xy \in N \quad \text {and}\quad xy+yx-xy+yx = 2yx \in N, \] which implies that \(N\) is an ideal. This may remind you of this classic diagram (we will not use it since it is additive though):

Ring Homomorphism

For \(x,y \in A\), we have \(x \in \phi(x)e+N\) and \(y \in \phi(y)e+N\). As a result, \(xy \in \phi(x)\phi(y)e+N\), and therefore \[ \phi(xy)=\phi(x)\phi(y)+0. \]

Step 2 - It suffices to prove that \(\phi(a^2)=0\) if \(\phi(a)=0\).

Again, if \(\phi\) is Jordan, we have \(\phi(x^2)=\phi(x)^2\) for all \(x \in A\). Conversely, if \(\phi(a^2)=0\) for all \(a \in N\), we may write \(x\) by \[ x=\phi(x)e+a \] where \(a \in N\) for all \(x \in A\). Therefore \[ \begin{aligned} \phi(x^2)&=\phi((\phi(x)e+a)^2)=\phi(x)^2+2\phi(x)\phi(a)+\phi(a)^2=\phi(x)^2, \end{aligned} \] which also shows that \(\phi\) is Jordan.

Step 3 - It suffices to show that the following function is constant

Fix \(a \in N\), assume \(\lVert a \rVert = 1\) without loss of generality, and define \[ f(\lambda)=\sum_{n=0}^{\infty}\frac{\phi(a^n)}{n!}\lambda^n \] for all complex \(\lambda\). If this function is constant (lemma 1), we immediately have \(f''(0)=\phi(a^2)=0\). This is purely a complex analysis problem however.

Step 4 - It suffices to describe the behaviour of an entire function

Note in the definition of \(f\), we have \[ \lvert \phi(a^n) \rvert \leq \lVert \phi \rVert \lVert a^n \rVert \leq \lVert \phi \rVert \lVert a \rVert^n=\lVert \phi \rVert. \] So we expect the norm of \(\phi\) to be finite, which ensures that \(f\) is entire. By reductio ad absurdum, if \(\lVert e-a \rVert < 1\) for \(a \in N\), by lemma 0, we have \(e-e+a=a\) to be invertible, which is impossible. Hence \(\lVert e-a \rVert \geq 1\) for all \(a \in N\). On the other hand, for \(\lambda \in \mathbb{C}\), we have the following inequality: \[ \begin{aligned} \lVert \lambda e-a \rVert = \lambda\lVert e-\lambda^{-1}a \rVert &\geq|\lambda| \\ &= |\phi(\lambda e)-\phi(a)| \\ &= |\phi(\lambda e-a)| \end{aligned} \] Therefore \(\phi\) is continuous with norm less than \(1\). The continuity of \(\phi\) is not assumed at the beginning but proved here.

For \(f\) we have some immediate facts. Since each coefficient in the series of \(f\) has finite norm, \(f\) is entire with \(f'(0)=\phi(a)=0\). Also, since \(\phi\) has norm \(1\), we also have \[ |f(\lambda)|=\left|\sum_{n=0}^{\infty}\frac{\phi(a^n)}{n!}\lambda^n\right| \leq \sum_{n=0}^{\infty}\frac{|\lambda^n|}{n!}=e^{|\lambda|}. \] All we need in the end is to show that \(f(\lambda) \neq 0\) for all \(\lambda \in \mathbb{C}\).

The series \[ E(\lambda)=\exp(a\lambda)=\sum_{n=0}^{\infty}\frac{(\lambda a)^n}{n!} \] converges since \(\lVert a \rVert=1\). The continuity of \(\phi\) shows now \[ f(\lambda)=\phi(E(\lambda)). \] Note \[ E(-\lambda)E(\lambda)=\left(\sum_{n=0}^{\infty}\frac{(-\lambda a)^n}{n!}\right)\left(\sum_{n=0}^{\infty}\frac{(\lambda a)^n}{n!}\right)=e. \] Hence \(E(\lambda)\) is invertible for all \(\lambda \in C\), hence \(f(\lambda)=\phi(E(\lambda)) \neq 0\). By lemma 1, \(f(\lambda)=1\) is constant. The proof is completed by reversing the steps. \(\square\)

References / Further reading

  • Walter Rudin, Real and Complex Analysis
  • Walter Rudin, Functional Analysis
  • Andrew M. Gleason, A Characterization of Maximal Ideals
  • J.-P. Kahane and W. Żelazko, A Characterization of Maximal Ideals in Commutative Banach Algebras
  • W. Żelazko A Characterization of Multiplicative linear functionals in Complex Banach Algebras
  • I. N. Herstein, Jordan Homomorphisms

The completeness of the quotient space (topological vector space)

The Goal

We are going to show the completeness of \(X/N\) where \(X\) is a TVS and \(N\) a closed subspace. Alongside, a bunch of useful analysis tricks will be demonstrated (and that's why you may find this blog post a little tedious.). But what's more important, the theorem proved here will be used in the future.

The main process

To make it clear, we should give a formal definition of \(F\)-space.

A topological space \(X\) is an \(F\)-space if its topology \(\tau\) is induced by a complete invariant metric \(d\).

A metric \(d\) on a vector space \(X\) will be called invariant if for all \(x,y,z \in X\), we have \[ d(x+z,y+z)=d(x,y). \] By complete we mean every Cauchy sequence of \((X,d)\) converges.

Defining the quotient metric \(\rho\)

The metric can be inherited to the quotient space naturally (we will use this fact latter), that is

If \(X\) is a \(F\)-space, \(N\) is a closed subspace of a topological vector space \(X\), then \(X/N\) is still a \(F\)-space.

Suppose \(d\) is a complete invariant metric compatible with \(\tau_X\). The metric on \(X/N\) is defined by \[ \boxed{\rho(\pi(x),\pi(y))=\inf_{z \in N}d(x-y,z)} \] ### \(\rho\) is a metric

Proof. First, if \(\pi(x)=\pi(y)\), that is, \(x-y \in N\), we see \[ \rho(\pi(x),\pi(y))=\inf_{z \in N}d(x-y,z)=d(x-y,x-y)=0. \] If \(\pi(x) \neq \pi(y)\) however, we shall show that \(\rho(\pi(x),\pi(y))>0\). In this case, we have \(x-y \notin N\). Since \(N\) is closed, \(N^c\) is open, and \(x-y\) is an interior point of \(X-N\). Therefore there exists an open ball \(B_r(x-y)\) centered at \(x-y\) with radius \(r>0\) such that \(B_r(x-y) \cap N = \varnothing\). Notice we have \(d(x-y,z)>r\) since otherwise \(z \in B_r(x-y)\). By putting \[ r_0=\sup\{r:B_r(x-y) \cap N = \varnothing\}, \] we see \(d(x-y,z) \geq r_0\) for all \(z \in N\) and indeed \(r_0=\inf_{z \in N}d(x-y,z)>0\) (the verification can be done by contradiction). In general, \(\inf_z d(x-y,z)=0\) if and only if \(x-y \in \overline{N}\).

Next, we shall show that \(\rho(\pi(x),\pi(y))=\rho(\pi(y),\pi(x))\), and it suffices to assume that \(\pi(x) \neq \pi(y)\). Sgince \(d\) is translate invariant, we get \[ \begin{aligned} d(x-y,z)&=d(x-y-z,0) \\ &=d(0,y-x+z) \\ &=d(-z,y-x) \\ &=d(y-x,-z). \end{aligned} \] Therefore the \(\inf\) of the left hand is equal to the one of the right hand. The identity is proved.

Finally, we need to verify the triangle inequality. Let \(r,s,t \in X\). For any \(\varepsilon>0\), there exist some \(z_\varepsilon\) and \(z_\varepsilon'\) such that \[ d(r-s,z_\varepsilon)<\rho(\pi(r),\pi(s))+\frac{\varepsilon}{2},\quad d(s-t,z'_\varepsilon)<\rho(\pi(s),\pi(t))+\frac{\varepsilon}{2}. \] Since \(d\) is invariant, we see \[ \begin{aligned} d(r-t,z_\varepsilon+z'_\varepsilon)&=d((r-s)+(s-t)-(z_\varepsilon+z'_\varepsilon),0) \\ &=d([(r-s)-z_\varepsilon]+[(s-t)-z'_\varepsilon],0) \\ &=d(r-s-z_\varepsilon,t-s+z'_\varepsilon) \\ &\leq d(r-s-z_\varepsilon,0)+d(t-s+z'_\varepsilon,0) \\ &=d(r-s,z_\varepsilon)+d(s-t,z'_\varepsilon) \end{aligned} \] (I owe [@LeechLattice](https://onp4.com/@leechlattice) for the inequality above.)

Therefore \[ \begin{aligned} d(r-t,z_\varepsilon+z'_\varepsilon)&\leq d(r-s,z_\varepsilon)+d(s-t,z'_\varepsilon) \\ &<\rho(\pi(r),\pi(s))+\rho(\pi(s),\pi(t))+\varepsilon. \end{aligned} \] (Warning: This does not imply that \(\rho(\pi(r),\pi(s))+\rho(\pi(s),\pi(t))=\inf_z d(r-t,z)\) since we don't know whether it is the lower bound or not.)

If \(\rho(\pi(r),\pi(s))+\rho(\pi(s),\pi(t))<\rho(\pi(r),\pi(t))\) however, let \[ 0<\varepsilon<\rho(\pi(r),\pi(t))-(\rho(\pi(r),\pi(s))+\rho(\pi(s),\pi(t))) \] then there exists some \(z''_\varepsilon=z_\varepsilon+z'_\varepsilon\) such that \[ d(r-t,z''_\varepsilon)<\rho(\pi(r),\pi(t)) \] which is a contradiction since \(\rho(\pi(r),\pi(t)) \leq d(r-t,z)\) for all \(z \in N\).

(We are using the \(\varepsilon\) definition of \(\inf\). See here.)

\(\rho\) is translate invariant

Since \(\pi\) is surjective, we see if \(u \in X/N\), there exists some \(a \in X\) such that \(\pi(a)=u\). Therefore \[ \begin{aligned} \rho(\pi(x)+u,\pi(y)+u) &=\rho(\pi(x)+\pi(a),\pi(y)+\pi(a)) \\ &=\rho(\pi(x+a),\pi(y+a)) \\ &=\inf_{z \in N}d(x+a-y-a,z) \\ &=\rho(\pi(x),\pi(y)). \end{aligned} \]

\(\rho\) is well-defined

If \(\pi(x)=\pi(x')\) and \(\pi(y)=\pi(y')\), we have to show that \(\rho(\pi(x),\pi(y))=\rho(\pi(x'),\pi(y'))\). In fact, \[ \begin{aligned} \rho(\pi(x),\pi(y)) &\leq \rho(\pi(x),\pi(x'))+\rho(\pi(x'),\pi(y'))+\rho(\pi(y'),\pi(y)) \\ &=\rho(\pi(x'),\pi(y')) \end{aligned} \] since \(\rho(\pi(x),\pi(x'))=0\) as \(\pi(x)=\pi(x')\). Meanwhile \[ \begin{aligned} \rho(\pi(x'),\pi(y')) &\leq \rho(\pi(x'),\pi(x)) + \rho(\pi(x),\pi(y)) + \rho(\pi(y),\pi(y')) \\ &= \rho(\pi(x),\pi(y)). \end{aligned} \] therefore \(\rho(\pi(x),\pi(y))=\rho(\pi(x'),\pi(y'))\).

\(\rho\) is compatible with \(\tau_N\)

By proving this, we need to show that a set \(E \subset X/N\) is open with respect to \(\tau_N\) if and only if \(E\) is a union of open balls. But we need to show a generalized version:

If \(\mathscr{B}\) is a local base for \(\tau\), then the collection \(\mathscr{B}_N\), which contains all sets \(\pi(V)\) where \(V \in \mathscr{B}\), forms a local base for \(\tau_N\).

Proof. We already know that \(\pi\) is continuous, linear and open. Therefore \(\pi(V)\) is open for all \(V \in \mathscr{B}\). For any open set around \(E \subset X/N\) containing \(\pi(0)\), we see \(\pi^{-1}(E)\) is open, and we have \[ \pi^{-1}(E)=\bigcup_{V\in\mathscr{B}}V \] and therefore \[ E=\bigcup_{V \in \mathscr{B}}\pi(V). \]


Now consider the local base \(\mathscr{B}\) containing all open balls around \(0 \in X\). Since \[ \pi(\{x:d(x,0)<r\})=\{u:\rho(u,\pi(0))<r\} \] we see \(\rho\) determines \(\mathscr{B}_N\). But we have already proved that \(\rho\) is invariant; hence \(\mathscr{B}_N\) determines \(\tau_N\).

If \(d\) is complete, then \(\rho\) is complete.

Once this is proved, we are able to claim that, if \(X\) is a \(F\)-space, then \(X/N\) is still a \(F\)-space, since its topology is induced by a complete invariant metric \(\rho\).

Proof. Suppose \((x_n)\) is a Cauchy sequence in \(X/N\), relative to \(\rho\). There is a subsequence \((x_{n_k})\) with \(\rho(x_{n_k},x_{n_{k+1}})<2^{-k}\). Since \(\pi\) is surjective, we are able to pick some \(z_k \in X\) such that \(\pi(z_k) = x_{n_k}\) and such that \[ d(z_{k},z_{k+1})<2^{-k}. \] (The existence can be verified by contradiction still.) By the inequality above, we see \((z_k)\) is Cauchy (can you see why?). Since \(X\) is complete, \(z_k \to z\) for some \(z \in X\). By the continuity of \(\pi\), we also see \(x_{n_k} \to \pi(z)\) as \(k \to \infty\). Therefore \((x_{n_k})\) converges. Hence \((x_n)\) converges since it has a convergent subsequence. \(\rho\) is complete.

Remarks

This fact will be used to prove some corollaries in the open mapping theorem. For instance, for any continuous linear map \(\Lambda:X \to Y\), we see \(\ker(\Lambda)\) is closed, therefore if \(X\) is a \(F\)-space, then \(X/\ker(\Lambda)\) is a \(F\)-space as well. We will show in the future that \(X/\ker(\Lambda)\) and \(\Lambda(X)\) are homeomorphic if \(\Lambda(X)\) is of the second category.

There are more properties that can be inherited by \(X/N\) from \(X\). For example, normability, metrizability, local convexity. In particular, if \(X\) is Banach, then \(X/N\) is Banach as well. To do this, it suffices to define the quotient norm by \[ \lVert \pi(x) \rVert = \inf\{\lVert x-z \rVert:z \in N\}. \]

Basic Facts of Semicontinuous Functions

Continuity

We are restricting ourself into \(\mathbb{R}\) endowed with normal topology. Recall that a function is continuous if and only if for any open set \(U \subset \mathbb{R}\), we have \[ \{x:f(x) \in U\}=f^{-1}(U) \]

to be open. One can rewrite this statement using \(\varepsilon-\delta\) language. To say a function \(f: \mathbb{R} \to \mathbb{R}\) continuous at \(f(x)\), we mean for any \(\varepsilon>0\), there exists some \(\delta>0\) such that for \(t \in (x-\delta,x+\delta)\), we have \[ |f(x)-f(t)|<\varepsilon. \] \(f\) is continuous on \(\mathbb{R}\) if and only if \(f\) is continuous at every point of \(\mathbb{R}\).

If \((x-\delta,x+\delta)\) is replaced with \((x-\delta,x)\) or \((x,x+\delta)\), we get left continuous and right continuous, one of which plays an important role in probability theory.

But the problem is, sometimes continuity is too strong for being a restriction, but the 'direction' associated with left/right continuous functions are unnecessary as well. For example the function \[ f(x)=\chi_{(0,1)}(x) \] is neither left nor right continuous (globally), but it is a thing. Left/right continuity is not a perfectly weakened version of continuity. We need something different.

Definition of semicontinuous

Let \(f\) be a real (or extended-real) function on \(\mathbb{R}\). The semicontinuity of \(f\) is defined as follows.

If \[ \{x:f(x)>\alpha\} \] is open for all real \(\alpha\), we say \(f\) is lower semicontinuous.

If \[ \{x:f(x)<\alpha\} \] is open for all real \(\alpha\), we say \(f\) is upper semicontinuous.

Is it possible to rewrite these definition à la \(\varepsilon-\delta\)? The answer is yes if we restrict ourself in metric space.

\(f: \mathbb{R} \to \mathbb{R}\) is upper semicontinuous at \(x\) if for every \(\varepsilon>0\), there exists some \(\delta>0\) such that for \(t \in (x-\delta,x+\delta)\), we have \[ f(t)<f(x)+\varepsilon \]

\(f: \mathbb{R} \to \mathbb{R}\) is lower semicontinuous at \(x\) if for every \(\varepsilon>0\), there exists some \(\delta>0\) such that for \(t \in (x-\delta,x+\delta)\), we have \[ f(t)>f(x)-\varepsilon \]

Of course, \(f\) is upper/lower semicontinuous on \(\mathbb{R}\) if and only if it is so on every point of \(\mathbb{R}\). One shall find no difference between the definitions in different styles.

Relation with continuous functions

Here is another way to see it. For the continuity of \(f\), we are looking for arbitrary open subsets \(V\) of \(\mathbb{R}\), and \(f^{-1}(V)\) is expected to be open. For the lower/upper semicontinuity of \(f\), however, the open sets are restricted to be like \((\alpha,+\infty]\) and \([-\infty,\alpha)\). Since all open sets of \(\mathbb{R}\) can be generated by the union or intersection of sets like \([-\infty,\alpha)\) and \((\beta,+\infty]\), we immediately get

\(f\) is continuous if and only if \(f\) is both upper semicontinuous and lower semicontinuous.

Proof. If \(f\) is continuous, then for any \(\alpha \in \mathbb{R}\), we see \([-\infty,\alpha)\) is open, and therefore \[ f^{-1}([-\infty,\alpha)) \] has to be open. The upper semicontinuity is proved. The lower semicontinuity of \(f\) is proved in the same manner.

If \(f\) is both upper and lower semicontinuous, we see \[ f^{-1}((\alpha,\beta))=f^{-1}([-\infty,\beta)) \cap f^{-1}((\alpha,+\infty]) \] is open. Since every open subset of \(\mathbb{R}\) can be written as a countable union of segments of the above types, we see for any open subset \(V\) of \(\mathbb{R}\), \(f^{-1}(V)\) is open. (If you have trouble with this part, it is recommended to review the definition of topology.) \(\square\)

Examples

There are two important examples.

  1. If \(E \subset \mathbb{R}\) is open, then \(\chi_E\) is lower semicontinuous.
  2. If \(F \subset \mathbb{R}\) is closed, then \(\chi_F\) is upper semicontinuous.

We will prove the first one. The second one follows in the same manner of course. For \(\alpha<0\), the set \(A=\chi_E^{-1}((\alpha,+\infty])\) is equal to \(\mathbb{R}\), which is open. For \(\alpha \geq 1\), since \(\chi_E \leq 1\), we see \(A=\varnothing\). For \(0 \leq \alpha < 1\) however, the set of \(x\) where \(\chi_E>\alpha\) has to be \(E\), which is still open.

When checking the semicontinuity of a function, we check from bottom to top or top to bottom. The function \(\chi_E\) is defined by \[ \chi_E(x)=\begin{cases} 1 \quad x \in E \\ 0 \quad x \notin E \end{cases}. \]

Addition of semicontinuous functions

If \(f_1\) and \(f_2\) are upper/lower semicontinuous, then so is \(f_1+f_2\).

Proof. We are going to prove this using different tools. Suppose now both \(f_1\) and \(f_2\) are upper semicontinuous. For \(\varepsilon>0\), there exists some \(\delta_1>0\) and \(\delta_2>0\) such that \[ f_1(t) < f_1(x)+\varepsilon/2 \quad t \in (x-\delta_1,x+\delta_1), \\ f_2(t) < f_2(x) + \varepsilon/2 \quad t \in (x-\delta_2,x+\delta_2). \] Proof. If we pick \(\delta=\min(\delta_1,\delta_2)\), then we see for all \(t \in (x-\delta,x+\delta)\), we have \[ f_1(t)+f_2(t)<f_1(x)+f_2(x)+\varepsilon. \] The upper semicontinuity of \(f_1+f_2\) is proved by considering all \(x \in \mathbb{R}\).

Now suppose both \(f_1\) and \(f_2\) are lower semicontinuous. We have a identity by \[ \{x:f_1+f_2>\alpha\}=\bigcup_{\beta\in\mathbb{R}}\{x:f_1>\beta\}\cap\{x:f_2>\alpha-\beta\}. \] The set on the right side is always open. Hence \(f_1+f_2\) is lower semicontinuous. \(\square\)


However, when there are infinite many semicontinuous functions, things are different.

Let \(\\{f_n\\}\) be a sequence of nonnegative functions on \(\mathbb{R}\), then

  • If each \(f_n\) is lower semicontinuous, then so is \(\sum_{1}^{\infty}f_n\).
  • If each \(f_n\) is upper semicontinuous, then \(\sum_{1}^{\infty}f_n\) is not necessarily upper semicontinuous.

Proof. To prove this we are still using the properties of open sets. Put \(g_n=\sum_{1}^{n}f_k\). Now suppose all \(f_k\) are lower. Since \(g_n\) is a finite sum of lower functions, we see each \(g_n\) is lower. Let \(f=\sum_{n}f_n\). As \(f_k\) are non-negative, we see \(f(x)>\alpha\) if and only if there exists some \(n_0\) such that \(g_{n_0}(x)>\alpha\). Therefore \[ \{x:f(x)>\alpha\}=\bigcup_{n \geq n_0}\{x:g_n>\alpha\}. \] The set on the right hand is open already.

For the upper semicontinuity, it suffices to give an counterexample, but before that, we shall give the motivation.

As said, the characteristic function of a closed set is upper semicontinuous. Suppose \(\\{E_n\\}\) is a sequence of almost disjoint closed set, then \(E=\cup_{n\geq 1}E_n\) is not necessarily closed, therefore \(\chi_E=\sum\chi_{E_n}\) (a.e.) is not necessarily upper semicontinuous. Now we give a concrete example. Put \(f_0=\chi_{[1,+\infty]}\) and \(f_n=\chi_{E_n}\) for \(n \geq 1\) where \[ E_n=\{x:\frac{1}{1+n} \leq x \leq \frac{1}{n}\}. \] For \(x > 0\), we have \(f=\sum_nf_n \geq 1\). Meanwhile, \(f^{-1}([-\infty,1))=[-\infty,0]\), which is not open. \(\square\)

Notice that \(f\) can be defined on any topological space here.

Maximum and minimum

There is one fact we already know about continuous functions.

If \(X\) is compact, \(f: X \to \mathbb{R}\) is continuous, then there exists some \(a,b \in X\) such that \(f(a)=\min f(X)\), \(f(b)=\max f(X)\).

In fact, \(f(X)\) is compact still. But for semicontinuous functions, things will be different but reasonable. For upper semicontinuous functions, we have the following fact.

If \(X\) is compact and \(f: X \to (-\infty,+\infty)\) is upper semicontinuous, then there exists some \(a \in X\) such that \(f(a)=\max f(X)\).

Notice that \(X\) is not assumed to hold any other topological property. It can be Hausdorff or Lindelöf, but we are not asking for restrictions like this. The only property we will be using is that every open cover of \(X\) has a finite subcover. Of course, one can replace \(X\) with any compact subset of \(\mathbb{R}\), for example, \([a,b]\).

Proof. Put \(\alpha=\sup f(X)\), and define \[ E_n=\{x:f(x)<\alpha-\frac{1}{n}\}. \] If \(f\) attains no maximum, then for any \(x \in X\), there exists some \(n \geq 1\) such that \(f(x)<\alpha-\frac{1}{n}\). That is, \(x \in E_n\) for some \(n\). Therefore \(\bigcup_{n \geq 1}E_n\) covers \(X\). But this cover has no finite subcover of \(X\). A contradiction since \(X\) is compact. \(\square\)

Approximating integrable functions

This is a comprehensive application of several properties of semicontinuity.

(Vitali–Carathéodory theorem) Suppose \(f \in L^1(\mathbb{R})\), where \(f\) is real-valued function. For \(\varepsilon>0\), there exists some functions \(u\) and \(v\) on \(\mathbb{R}\) such that \(u \leq f \leq v\), \(u\) is a upper semicontinuous functions bounded above, and \(v\) is lower semicontinuous bounded below, and \[ \boxed{\int_{\mathbb{R}}(v-u)dm<\varepsilon} \]

It suffice to prove this theorem for \(f \geq 0\) (of course \(f\) is not identically equal to \(0\) since this case is trivial). Since \(f\) is the pointwise limit of an increasing sequence of simple functions \(s_n\), we are able to write \(f\) as \[ f=s_1+\sum_{n=2}^{\infty}(s_n-s_{n-1}). \] By putting \(t_1=s_1\), \(t_n=s_n-s_{n-1}\) for \(n \geq 2\), we get \(f=\sum_n t_n\). We are able to write \(f\) as \[ f=\sum_{k=1}^{\infty}c_k\chi_{E_k} \] where \(E_k\) is measurable for all \(k\). Also we have \[ \int_X f d\mu = \sum_{k=1}^{\infty}c_km(E_k), \] and the series on the right hand converges(since \(f \in L^1\). By the properties of Lebesgue measure, there exists a compact set \(F_k\) and a open set \(V_k\) such that \(F_k \subset E_k \subset V_k\) and \(c_km(V_k-F_k)<\frac{\varepsilon}{2^{k+1}}\). Put \[ v=\sum_{k=1}^{\infty}c_k\chi_{V_k},\quad u=\sum_{k=1}^{N}c_k\chi_{F_k} \] (now you can see \(v\) is lower semicontinuous and \(u\) is upper semicontinuous). The \(N\) is chosen in such a way that \[ \sum_{k=N+1}^{\infty}c_km(E_K)<\frac{\varepsilon}{2}. \] Since \(V_k \supset E_k\), we have \(\chi_{V_k} \geq \chi_{E_k}\). Therefore \(v \geq f\). Similarly, \(f \geq u\). Now we need to check the desired integral inequality. A simple recombination shows that \[ \begin{aligned} v-u&=\sum_{k=1}^{\infty}c_k\chi_{V_k}-\sum_{k=1}^{N}c_k\chi_{F_k} \\ &\leq \sum_{k=1}^{\infty}c_k\chi_{V_k}-\sum_{k=1}^{N}c_k\chi_{F_k}+\sum_{k=N+1}^{\infty}c_k(\chi_{E_k}-\chi_{F_k}) \\ &=\sum_{k=1}^{\infty}c_k(\chi_{V_k}-\chi_{F_k})+\sum_{k=N+1}^{\infty}c_k\chi_{E_k}. \end{aligned}. \] If we integrate the function above, we get \[ \begin{aligned} \int_{\mathbb{R}}(v-u)dm &\leq \sum_{k=1}^{\infty}c_k\mu(V_k-E_k)+\sum_{k=N+1}^{\infty}c_k\chi_{E_k} \\ &< \sum_{k=1}^{\infty}\frac{\varepsilon}{2^{k+1}}+\frac{\varepsilon}{2} \\ &=\varepsilon. \end{aligned} \] This proved the case when \(f \geq 0\). In the general case, we write \(f=f^{+}-f^{-}\). Attach the semicontinuous functions to \(f^{+}\) and \(f^{-}\) respectively by \(u_1 \leq f^{+} \leq v_1\) and \(u_2 \leq f^{-} \leq v_2\). Put \(u=u_1-v_2\), \(v=v_1-u_2\). As we can see, \(u\) is upper semicontinuous and \(v\) is lower semicontinuous. Also, \(u \leq f \leq v\) with the desired property since \[ \int_\mathbb{R}(v-u)dm=\int_\mathbb{R}(v_1-u_1)dm+\int_\mathbb{R}(v_2-u_2)dm<2\varepsilon, \] and the theorem follows. \(\square\)

Generalization

Indeed, the only unique property about measure used is the existence of \(F_k\) and \(V_k\). The domain \(\mathbb{R}\) here can be replaced with \(\mathbb{R}^k\) for \(1 \leq k < \infty\), and \(m\) be replaced with the respective \(m_k\). Much more generally, the domain can be replaced by any locally compact Hausdorff space \(X\), and the measure by any measure associated with Riesz-Markov-Kakutani representation theorem on \(C_c(X)\).

Is the reverse approximation always possible?

The answer is no. Consider the fat Cantor set \(K\), which has Lebesgue measure \(\frac{1}{2}\). We shall show that \(\chi_K\) can not be approximated below by a lower semicontinuous function.

If \(v\) is a lower semicontinuous function such that \(v \leq \chi_K\), then \(v \leq 0\).

Proof. Consider the set \(V=v^{-1}((0,1])=v^{-1}((0,+\infty))\). Since \(v \leq \chi_K\), we have \(V \subset K\). We will show that \(V\) has to be empty.

Pick \(t \in V\). Since \(V\) is open, there exists some neighborhood \(U\) containing \(t\) such that \(U \subset V\). But \(U=\varnothing\) since \(U \subset K\) and \(K\) has empty interior. Therefore \(V = \varnothing\). That is, \(v \leq 0\) for all \(x\). \(\square\)

Suppose \(u\) is any upper semicontinuous function such that \(u \geq f\). For \(\varepsilon=\frac{1}{2}\), we have \[ \int_{\mathbb{R}}(u-v)dm \geq \int_\mathbb{R}(f-v)dm \geq \frac{1}{2}. \] This example shows that there exists some integrable functions that are not able to reversely approximated in the sense of the Vitali–Carathéodory theorem.

An Introduction to Quotient Space

I'm assuming the reader has some abstract algebra and functional analysis background. You may have learned this already in your linear algebra class, but we are making our way to functional analysis problems.

Motivation

Trouble with \(L^p\) spaces

Fix \(p\) with \(1 \leq p \leq \infty\). It's easy to see that \(L^p(\mu)\) is a topological vector space. But it is not a metric space if we define \[ d(f,g)=\lVert f-g \rVert_p. \] The reason is, if \(d(f,g)=0\), we can only get \(f=g\) a.e., but they are not strictly equal. With that being said, this function \(d\) is actually a pseudo metric. This is unnatural. However, the relation \(\sim\) by \(f \sim g \mathbb{R}ightarrow d(f,g)=0\) is a equivalence relation. This inspires us to take quotient set into consideration.

Vector spaces are groups anyway

For a vector space \(V\), every subspace of \(V\) is a normal subgroup. There is no reason to prevent ourselves from considering quotient group and looking for some interesting properties. Further, a vector space is a abelian group, therefore any subspace is automatically normal.

Definition

Let \(N\) be a subspace of a vector space \(X\). For every \(x \in X\), let \(\pi(x)\) be the coset of \(N\) that contains \(x\), that is \[ \pi(x)=x+N. \] Trivially, \(\pi(x)=\pi(y)\) if and only if \(x-y \in N\) (say, \(\pi\) is well-defined since \(N\) is a vector space). This is a linear function since we also have the addition and multiplication by \[ \pi(x)+\pi(y)=\pi(x+y) \quad \alpha\pi(x)=\pi(\alpha{x}). \] These cosets are the elements of a vector space \(X/N\), which reads, the quotient space of \(X\) modulo \(N\). The map \(\pi\) is called the canonical map as we all know.

Examples

R^2-quotient

First we shall treat \(\mathbb{R}^2\) as a vector space, and the subspace \(\mathbb{R}\), which is graphically represented by \(x\)-axis, as a subspace (we will write it as \(X\)). For a vector \(v=(2,3)\), which is represented by \(AB\), we see the coset \(v+X\) has something special. Pick any \(u \in X\), for example \(AE\), \(AC\), or \(AG\). We see \(v+u\) has the same \(y\) value. The reason is simple, since we have \(v+u=(2+x,3)\), where the \(y\) value remain fixed however \(u\) may vary.

With that being said, the set \(v+X\), which is not a vector space, can be represented by \(\overrightarrow{AD}\). This proceed can be generalized to \(\mathbb{R}^n\) with \(\mathbb{R}^m\) as a subspace with ease.


We now consider some fancy example. Consider all rational Cauchy sequences, that is \[ (a_n)=(a_1,a_2,\cdots) \] where \(a_k\in\mathbb{Q}\) for all \(k\). In analysis class we learned two facts.

  1. Any Cauchy sequence is bounded.
  2. If \((a_n)\) converges, then \((a_n)\) is Cauchy.

However, the reverse of 2 does not hold in \(\mathbb{Q}\). For example, if we put \(a_k=(1+\frac{1}{k})^k\), we should have the limit to be \(e\), but \(e \notin \mathbb{Q}\).

If we define the addition and multiplication term by term, namely \[ (a_n)+(b_n)=(a_1+b_1,a_2+b_2,\cdots) \] and \[ (\alpha a_n)=(\alpha a_1,\alpha a_2,\cdots) \] where \(\alpha \in \mathbb{Q}\), we get a vector space (the verification is easy). The zero vector is defined by \[ (0)=(0,0,\cdots). \] This vector space is denoted by \(\overline{\mathbb{Q}}\). The subspace containing all sequences converges to \(0\) will be denoted by \(\overline{\mathbb{O}}\). Again, \((a_n)+\overline{\mathbb{O}}=(b_n)+\overline{\mathbb{O}}\) if and only if \((a_n-b_n) \in \overline{\mathbb{O}}\). Using the language of equivalence relation, we also say \((a_n)\) and \((b_n)\) are equivalent if \((a_n-b_n) \in \overline{\mathbb{O}}\). For example, the two following sequences are equivalent: \[ (1,1,1,\cdots,1,\cdots)\quad\quad (0.9,0.99,0.999,\cdots). \] Actually we will get \(\mathbb{R} \simeq \overline{\mathbb{Q}}/\overline{\mathbb{O}}\) in the end. But to make sure that this quotient space is exactly the one we meet in our analysis class, there are a lot of verification should be done.

We shall give more definitions for calculation. The multiplication of two Cauchy sequences is defined term by term à la the addition. For \(\overline{\mathbb{Q}}/\overline{\mathbb{O}}\) we have \[ ((a_n)+\overline{\mathbb{O}})+((b_n)+\overline{\mathbb{O}})=(a_n+b_n) + \overline{\mathbb{O}} \] and \[ ((a_n)+\overline{\mathbb{O}})((b_n)+\overline{\mathbb{O}})=(a_nb_n)+\overline{\mathbb{O}}. \] As for inequality, a partial order has to be defined. We say \((a_n) > (0)\) if there exists some \(N>0\) such that \(a_n>0\) for all \(n \geq N\). By \((a_n) > (b_n)\) we mean \((a_n-b_n)>(0)\) of course. For cosets, we say \((a_n)+\overline{\mathbb{O}}>\overline{\mathbb{O}}\) if \((x_n) > (0)\) for some \((x_n) \in (a_n)+\overline{\mathbb{O}}\). This is well defined. That is, if \((x_n)>(0)\), then \((y_n)>(0)\) for all \((y_n) \in (a_n)+\overline{\mathbb{O}}\).

With these operations being defined, it can be verified that \(\overline{\mathbb{Q}}/\overline{\mathbb{O}}\) has the desired properties, for example, least-upper-bound property. But this goes too far from the topic, we are not proving it here. If you are interested, you may visit here for more details.


Finally, we are trying to make \(L^p\) a Banach space. Fix \(p\) with \(1 \leq p < \infty\). There is a seminorm defined for all Lebesgue measurable functions on \([0,1]\) by \[ p(f)=\lVert f \rVert_p=\left\{\int_{0}^{1}|f(t)|^pdt\right\}^{1/p} \] \(L^p\) is a vector space containing all functions \(f\) with \(p(f)<\infty\). But it's not a normed space by \(p\), since \(p(f)=0\) only implies \(f=0\) almost everywhere. However, the set \(N\) which contains all functions that equals to \(0\) is also a vector space. Now consider the quotient space by \[ \tilde{p}(\pi(f))=p(f), \] where \(\pi\) is the canonical map of \(L^p\) into \(L^p/N\). We shall prove that \(\tilde{p}\) is well-defined here. If \(\pi(f)=\pi(g)\), we have \(f-g \in N\), therefore \[ 0=p(f-g)\geq |p(f)-p(g)|, \] which forces \(p(f)=p(g)\). Therefore in this case we also have \(\tilde{p}(\pi(f))=\tilde{p}(\pi(g))\). This indeed ensures that \(\tilde{p}\) is a norm, and \(L^p/N\) a Banach space. There are some topological facts required to prove this, we are going to cover a few of them.

Topology of quotient space

Definition

We know if \(X\) is a topological vector space with a topology \(\tau\), then the addition and scalar multiplication is continuous. Suppose now \(N\) is a closed subspace of \(X\). Define \(\tau_N\) by \[ \tau_N=\{E \subset X/N:\pi^{-1}(E)\in \tau\}. \] We are expecting \(\tau_N\) to be properly-defined. And fortunately it is. Some interesting techniques will be used in the following section.

\(\tau_N\) is a vector topology

There will be two steps to get this done.

\(\tau_N\) is a topology.

It is trivial that \(\varnothing\) and \(X/N\) are elements of \(\tau_N\). Other properties are immediate as well since we have \[ \pi^{-1}(A \cap B) = \pi^{-1}(A) \cap \pi^{-1}(B) \] and \[ \pi^{-1}(\cup A_\alpha)=\cup\pi^{-1}( A_{\alpha}). \] That said, if we have \(A,B\in \tau_N\), then \(A \cap B \in \tau_N\) since \(\pi^{-1}(A \cap B)=\pi^{-1}(A) \cap \pi^{-1}(B) \in \tau\).

Similarly, if \(A_\alpha \in \tau_N\) for all \(\alpha\), we have \(\cup A_\alpha \in \tau_N\). Also, by definition of \(\tau_N\), \(\pi\) is continuous.

\(\tau_N\) is a vector topology.

First, we show that a point in \(X/N\), which can be written as \(\pi(x)\), is closed. Notice that \(N\) is assumed to be closed, and \[ \pi^{-1}(\pi(x))=x+N \] therefore has to be closed.

In fact, \(F \subset X/N\) is \(\tau_N\)-closed if and only if \(\pi^{-1}(F)\) is \(\tau\)-closed. To prove this, one needs to notice that \(\pi^{-1}(F^c)=(\pi^{-1}(F))^{c}\).

Suppose \(V\) is open, then \[ \pi^{-1}(\pi(V))=N+V \] is open. By definition of \(\tau_N\), we have \(\pi(V) \in \tau_N\). Therefore \(\pi\) is an open mapping.

If now \(W\) is a neighborhood of \(0\) in \(X/N\), there exists a neighborhood \(V\) of \(0\) in \(X\) such that \[ V + V \subset \pi^{-1}(W). \] Hence \(\pi(V)+\pi(V) \subset W\). Since \(\pi\) is open, \(\pi(V)\) is a neighborhood of \(0\) in \(X/N\), this shows that the addition is continuous.

The continuity of scalar multiplication will be shown in a direct way (so can the addition, but the proof above is intended to offer some special technique). We already know, the scalar multiplication on \(X\) by \[ \begin{aligned} \varphi:\Phi \times X &\to X \\ (\alpha,x) &\mapsto \alpha{x} \end{aligned} \] is continuous, where \(\Phi\) is the scalar field (usually \(\mathbb{R}\) or \(\mathbb{C}\). Now the scalar multiplication on \(X/N\) is by \[ \begin{aligned} \psi: \Phi \times X/N &\to X/N \\ (\alpha,x+N) &\mapsto \alpha{x}+N. \end{aligned} \] We see \(\psi(\alpha,x+N)=\pi(\varphi(\alpha,x))\). But the composition of two continuous functions are continuous, therefore \(\psi\) is continuous.

A commutative diagram by quotient space

We are going to talk about a classic commutative diagram that you already see in algebra class.

diagram-000001

There are some assumptions.

  1. \(X\) and \(Y\) are topological vector spaces.
  2. \(\Lambda\) is linear.
  3. \(\pi\) is the canonical map.
  4. \(N\) is a closed subspace of \(X\) and \(N \subset \ker\Lambda\).

Algebraically, there exists a unique map \(f: X/N \to Y\) by \(x+N \mapsto \Lambda(x)\). Namely, the diagram above is commutative. But now we are interested in some analysis facts.

\(f\) is linear.

This is obvious. Since \(\pi\) is surjective, for \(u,v \in X/N\), we are able to find some \(x,y \in X\) such that \(\pi(x)=u\) and \(\pi(y)=v\). Therefore we have \[ \begin{aligned} f(u+v)=f(\pi(x)+\pi(y))&=f(\pi(x+y)) \\ &=\Lambda(x+y) \\ &=\Lambda(x)+\Lambda(y) \\ &= f(\pi(x))+f(\pi(y)) \\ &=f(u)+f(v) \end{aligned} \] and \[ \begin{aligned} f(\alpha{u})=f(\alpha\pi(x))&=f(\pi(\alpha{x})) \\ &= \Lambda(\alpha{x}) \\ &= \alpha\Lambda(x) \\ &= \alpha{f(\pi(x))} \\ &= \alpha{f(u)}. \end{aligned} \]

\(\Lambda\) is open if and only if \(f\) is open.

If \(f\) is open, then for any open set \(U \subset X\), we have \[ \Lambda(U)=f(\pi(U)) \] to be a open set since \(\pi\) is open, and \(\pi(U)\) is a open set.

If \(f\) is not open, then there exists some \(V \subset X/N\) such that \(f(V)\) is closed. However, since \(\pi\) is continuous, we have \(\pi^{-1}(V)\) to be open. In this case we have \[ f(\pi(\pi^{-1}(V)))=f(V)=\Lambda(\pi^{-1}(V)) \] to be closed. \(\Lambda\) is therefore not open. This shows that if \(\Lambda\) is open, then \(f\) is open.

\(\Lambda\) is continuous if and only if \(f\) is continuous.

If \(f\) is continuous, for any open set \(W \subset Y\), we have \(\pi^{-1}(f^{-1}(W))=\Lambda^{-1}(W)\) to be open. Therefore \(\Lambda\) is continuous.

Conversely, if \(\Lambda\) is continuous, for any open set \(W \subset Y\), we have \(\Lambda^{-1}(W)\) to be open. Therefore \(f^{-1}(W)=\pi(\Lambda^{-1}(W))\) has to be open since \(\pi\) is open.