From ca43e5660ccbd3f4beaaa2073ce7dda05a80eff6 Mon Sep 17 00:00:00 2001 From: Nao Pross Date: Mon, 1 Aug 2022 19:58:09 +0200 Subject: Kugel: Introduction and preliminaries (not spherical harmonics, yet) --- buch/papers/kugel/preliminaries.tex | 346 ++++++++++++++++++++++++++++++++++++ 1 file changed, 346 insertions(+) create mode 100644 buch/papers/kugel/preliminaries.tex (limited to 'buch/papers/kugel/preliminaries.tex') diff --git a/buch/papers/kugel/preliminaries.tex b/buch/papers/kugel/preliminaries.tex new file mode 100644 index 0000000..03cd421 --- /dev/null +++ b/buch/papers/kugel/preliminaries.tex @@ -0,0 +1,346 @@ +% vim:ts=2 sw=2 et spell tw=78: + +\section{Preliminaries} + +The purpose of this section is to dust off some concepts that will become +important later on. This will enable us to be able to get a richer and more +general view of the topic than just liming ourselves to a specific example. + +\subsection{Vectors and inner product spaces} + +We shall start with a few fundamentals of linear algebra. We will mostly work +with complex numbers, but for the sake of generality we will do what most +textbook do, and write \(\mathbb{K}\) instead of \(\mathbb{C}\) since the +theory works the same when we replace \(\mathbb{K}\) with the real +numbers \(\mathbb{R}\). + +\begin{definition}[Vector space] + \label{kugel:def:vector-space} \nocite{axler_linear_2014} + A \emph{vector space} over a field \(\mathbb{K}\) is a set \(V\) with an + addition on \(V\) and a multiplication on \(V\) such that the following + properties hold: + \begin{enumerate}[(a)] + \item (Commutativity) \(u + v = v + u\) for all \(u, v \in V\); + \item (Associativity) \((u + v) + w = u + (v + w)\) and \((ab)v = a(bv)\) + for all \(u, v, w \in V\) and \(a, b \in \mathbb{K}\); + \item (Additive identity) There exists an element \(0 \in V\) such that + \(v + 0 = v\) for all \(v \in V\); + \item (Additive inverse) For every \(v \in V\), there exists a \(w \in V\) + such that \(v + w = 0\); + \item (Multiplicative identity) \(1 v = v\) for all \(v \in V\); + \item (Distributive properties) \(a(u + v) = au + av\) and \((a + b)v = av + + bv\) for all \(a, b \in \mathbb{K}\) and all \(u,v \in V\). + \end{enumerate} +\end{definition} + +\begin{definition}[Dot product] + \label{kugel:def:dot-product} + In the vector field \(\mathbb{K}^n\) the scalar or dot product between two + vectors \(u, v \in \mathbb{K}^n\) is + \( + u \cdot v + = u_1 \overline{v}_1 + u_2 \overline{v}_2 + \cdots + u_n \overline{v}_n + = \sum_{i=1}^n u_i \overline{v}_i. + \) +\end{definition} + +\texttt{TODO: Text here.} + +\begin{definition}[Span] +\end{definition} + +\texttt{TODO: Text here.} + +\begin{definition}[Linear independence] +\end{definition} + + +\texttt{TODO: Text here.} + +\begin{definition}[Basis] +\end{definition} + +\texttt{TODO: Text here.} + +\begin{definition}[Inner product] + \label{kugel:def:inner-product} \nocite{axler_linear_2014} + The \emph{inner product} on \(V\) is a function that takes each ordered pair + \((u, v)\) of elements of \(V\) to a number \(\langle u, v \rangle \in + \mathbb{K}\) and has the following properties: + \begin{enumerate}[(a)] + \item (Positivity) \(\langle v, v \rangle \geq 0\) for all \(v \in V\); + \item (Definiteness) \(\langle v, v \rangle = 0\) iff \(v = 0\); + \item (Additivity) \( + \langle u + v, w \rangle = + \langle u, w \rangle + \langle v, w \rangle + \) for all \(u, v, w \in V\); + \item (Homogeneity) \( + \langle \lambda u, v \rangle = + \lambda \langle u, v \rangle + \) for all \(\lambda \in \mathbb{K}\) and all \(u, v \in V\); + \item (Conjugate symmetry) + \(\langle u, v \rangle = \overline{\langle v, u \rangle}\) for all + \(u, v \in V\). + \end{enumerate} +\end{definition} + +This newly introduced inner product is thus a generalization of the scalar +product that does not explicitly depend on rows or columns of vectors. This +has the interesting consequence that anything that behaves according to the +rules given in definition \ref{kugel:def:inner-product} \emph{is} an inner +product. For example if we say that the vector space \(V = \mathbb{R}^n\), +then the dot product defined in definition \ref{kugel:def:dot-product} +\( + u \cdot v = u_1 \overline{v}_1 + u_2 \overline{v}_2 + \cdots + u_n \overline{v}_n +\) +is an inner product in \(V\), and the two are said to form an \emph{inner +product space}. + +\begin{definition}[Inner product space] + \nocite{axler_linear_2014} + An inner product space is a vector space \(V\) equipped with an inner + product on \(V\). +\end{definition} + +How about a more interesting example: the set of continuous complex valued +functions on the interval \([0; 1]\) can behave like vectors. Functions can +be added, subtracted, multiplied with scalars, are associative and there is +even the identity element (zero function \(f(x) = 0\)), so we can create an +inner product +\[ + \langle f, g \rangle = \int_0^1 f(x) \overline{g(x)} \, dx, +\] +which will indeed satisfy all of the rules for an inner product (in fact this +is called the Hermitian inner product\nocite{allard_mathematics_2009}). If +this last step sounds too good to be true, you are right, because it is not +quite so simple. The problem that we have swept under the rug here is +convergence, which any student who took an analysis class will know is a +rather hairy question. We will not need to go too much into the details since +formally discussing convergence is definitely beyond the scope of this text, +however, for our purposes we will still need to dig a little deeper for a few +more paragraph. + +\subsection{Convergence} + +In the last section we hinted that we can create ``infinite-dimensional'' +vector spaces using functions as vectors, and inner product spaces by +integrating the product of two functions of said vector space. However, there +is a problem with convergence which twofold: the obvious problem is that the +integral of the inner product may not always converge, while the second is a +bit more subtle and will be discussed later. The inner product that does +not converge is a problem because we want a \emph{norm}. + +\begin{definition}[\(L^2\) Norm] + \nocite{axler_linear_2014} + The norm of a vector \(v\) of an inner product space is a number + denoted as \(\| v \|\) that is computed by \(\| v \| = \sqrt{\langle v, v + \rangle}\). +\end{definition} + +In \(\mathbb{R}^n\) with the dot product (Euclidian space) the norm is the +geometric length of a vector, while in a more general inner product space the +norm can be thought of as a more abstract measure of ``length''. In any case +it is rather important that the expression \(\sqrt{\langle v, v \rangle}\), +which when using functions \(f: \mathbb{R} \to \mathbb{C}\) becomes +\[ + \sqrt{\langle f, f \rangle} = + \sqrt{\int_\mathbb{R} f(x) \overline{f(x)} \, dx} = + \sqrt{\int_\mathbb{R} |f(x)|^2 \, dx}, +\] +always exists. So, to fix this problems we do what mathematicians do best: +make up the solution. Since the integrand under the square root is always the +square of the magnitude, we can just specify that the functions must be +\emph{absolutely square integrable}. To be more compact it is common to just +write \(f \in L^2\), where \(L^2\) denotes the set of absolutely square +integrable functions. + +Now we can tackle the second (much more difficult) problem of convergence +mentioned at the beginning. Using the technical jargon, we need that our inner +product space is what is called a \emph{complete metric space}, which just +means that we can measure distances. For the more motivated readers although +not really necessary we can also give a more formal definition, the others can +skip to the next section. + +\begin{definition}[Metric space] + \nocite{tao_analysis_2016} + A metric space \((X, d)\) is a space \(X\) of objects (called points), + together with a distance function or metric \(d: X \times X \to [0, + +\infty)\), which associates to each pair \(x, y\) of points in \(X\) a + non-negative real number \(d(x, y) \geq 0\). Furthermore, the metric must + satisfy the following four axioms: + \begin{enumerate}[(a)] + \item For any \(x\in X\), we have \(d(x, x) = 0\). + \item (Positivity) For any \emph{distinct} \(x, y \in X\), we have + \(d(x,y) > 0\). + \item (Symmetry) For any \(x,y \in X\), we have \(d(x, y) = d(y, x)\). + \item (Triangle inequality) For any \(x, y, z \in X\) we have + \(d(x, z) \leq d(x, y) + d(y, z)\). + \end{enumerate} +\end{definition} + +As is seen in the definition metric spaces are a very abstract concept and +rely on rather weak statements, which makes them very general. Now, the more +intimidating part is the \emph{completeness} which is defined as follows. + +\begin{definition}[Complete metric space] + \label{kugel:def:complete-metric-space} + A metric space \((X, d)\) is said to be \emph{complete} iff every Cauchy + sequence in \((X, d)\) is convergent in \((X, d)\). +\end{definition} + +To fully explain definition \ref{kugel:def:complete-metric-space} it would +take a few more pages, which would get a bit too heavy. So instead we will +give an informal explanation through an counterexample to get a feeling of +what is actually happening. Cauchy sequences is a rather fancy name for a +sequence for example of numbers that keep changing, but in a such a way that +at some point the change keeps getting smaller (the infamous +\(\varepsilon-\delta\) definition). For example consider the sequence of +numbers +\[ + 1, + 1.4, + 1.41, + 1.414, + 1.4142, + 1.41421, + \ldots +\] +in the metric space \((\mathbb{Q}, d)\) with \(d(x, y) = |x - y|\). Each +element of this sequence can be written with some fraction in \(\mathbb{Q}\), +but in \(\mathbb{R}\) the sequence is converging towards the number +\(\sqrt{2}\). However, \(\sqrt{2} \notin \mathbb{Q}\). Since we can find a +sequence of fractions whose distance's limit is not in \(\mathbb{Q}\), the +metric space \((\mathbb{Q}, d)\) is \emph{not} complete. Conversely, +\((\mathbb{R}, d)\) is a complete metric space since \(\sqrt{2} \in +\mathbb{R}\). + +Of course the analogy above also applies to vectors, i.e. if in an inner +product space \(V\) over a field \(\mathbb{K}\) all sequences of vectors have +a distance that is always in \(\mathbb{K}\), then \(V\) is also a complete +metric space. In the jargon, this particular case is what is known as a +Hilbert space, after the incredibly influential German mathematician David +Hilbert. + +\begin{definition}[Hilbert space] + A Hilbert space is a vector space \(H\) with an inner product \(\langle f, g + \rangle\) and a norm \(\sqrt{\langle f, f \rangle}\) defined such that \(H\) + turns into a complete metric space. +\end{definition} + +\subsection{Orthogonal basis and Fourier series} + +Now we finally have almost everything we need to get into the domain of +Fourier theory from the perspective of linear algebra. However, we still need +to briefly discuss the matter of orthogonality\footnote{See chapter +\ref{buch:chapter:orthogonalitaet} for more on orthogonality.} and +periodicity. Both should be very straightforward and already well known. + +\begin{definition}[Orthogonality and orthonormality] + \label{kugel:def:orthogonality} + In an inner product space \(V\) two vectors \(u, v \in V\) are said to be + \emph{orthogonal} if \(\langle u, v \rangle = 0\). Further, if both \(u\) + and \(v\) are of unit length, i.e. \(\| u \| = 1\) and \(\| v \| = 1\), then + they are said to be ortho\emph{normal}. +\end{definition} + +\begin{definition}[1-periodic function and \(C(\mathbb{R}/\mathbb{Z}; \mathbb{C})\)] + A function is said to be 1-periodic if \(f(x + 1) = f(x)\). The set of + 1-periodic function from the real to the complex + numbers is denoted by \(C(\mathbb{R}/\mathbb{Z}; \mathbb{C})\). +\end{definition} + +In the definition above the notation \(\mathbb{R}/\mathbb{Z}\) was borrowed +from group theory, and is what is known as a quotient group; Not really +relevant for our discussion but still a ``good to know''. More importantly, it +is worth noting that we could have also defined more generally \(L\)-periodic +functions with \(L\in\mathbb{R}\), however, this would introduce a few ugly +\(L\)'s everywhere which are not really necessary (it will always be possible +to extend the theorems to \(\mathbb{R} / L\mathbb{Z}\)). Thus, we will +continue without the \(L\)'s, and to simplify the language unless specified +otherwise ``periodic'' will mean 1-periodic. Having said that, we can +officially begin with the Fourier theory. + +\begin{lemma} + The subset of absolutely square integrable functions in + \(C(\mathbb{R}/\mathbb{Z}; \mathbb{C})\) together with the Hermitian inner + product + \[ + \langle f, g \rangle = \int_{[0; 1)} f(x) \overline{g(x)} \, dx + \] + form a Hilbert space. +\end{lemma} +\begin{proof} + It is not too difficult to show that the functions in \(C(\mathbb{R} / + \mathbb{Z}; \mathbb{C})\) are well behaved and form a vector space. Thus, + what remains is that the norm needs to form a complete metric space. + However, this follows from the fact that we defined the functions to be + absolutely square integrable\footnote{For the curious on why, it is because + \(L^2\) is what is known as a \emph{compact metric space}, and compact + metric spaces are always complete (see \cite{eck_metric_2022, + tao_analysis_2016}). To explain compactness and the relationship between + compactness and completeness is definitely beyond the goals of this text.}. +\end{proof} + +This was probably not a very satisfactory proof since we brushed off a lot of +details by referencing other theorems. However, the main takeaway should be +that we have ``constructed'' this new Hilbert space of functions in a such a +way that from now on we will not have to worry about the details of +convergence. + +\begin{lemma} + \label{kugel:lemma:exp-1d} + The set of functions \(E_n(x) = e^{i2\pi nx}\) on the interval + \([0; 1)\) with \(n \in \mathbb{Z} \) are orthonormal. +\end{lemma} +\begin{proof} + We need to show that \(\langle E_m, E_n \rangle\) equals 1 when \(m = n\) + and zero otherwise. This is a straightforward computation: We start by + unpacking the notation to get + \[ + \langle E_m, E_n \rangle + = \int_0^1 e^{i2\pi mx} e^{- i2\pi nx} \, dx + = \int_0^1 e^{i2\pi (m - n)x} \, dx, + \] + then inside the integrand we can see that when \(m = n\) we have \(e^0 = 1\) and + thus \( \int_0^1 dx = 1, \) while when \(m \neq n\) we can just say that we + have a new non-zero integer + \(k := m - n\) and + \[ + \int_0^1 e^{i2\pi kx} \, dx + = \frac{e^{i2\pi k} - e^{0}}{i2\pi k} + = \frac{1 - 1}{i2\pi k} + = 0 + \] + as desired. \qedhere +\end{proof} + +\begin{definition}[Spectrum] +\end{definition} + +\begin{theorem}[Fourier Theorem] + \[ + \lim_{N \to \infty} \left \| + f(x) - \sum_{n = -N}^N \hat{f}(n) E_n(x) + \right \|_2 = 0 + \] +\end{theorem} + +\begin{lemma} + The set of functions \(E_{m, n}(\xi, \eta) = e^{i2\pi m\xi}e^{i2\pi n\eta}\) + on the square \([0; 1)^2\) with \(m, n \in \mathbb{Z} \) are orthonormal. +\end{lemma} +\begin{proof} + The proof is almost identical to lemma \ref{kugel:lemma:exp-1d}, with the + only difference that the inner product is given by + \[ + \langle E_{m,n}, E_{m', n'} \rangle + = \iint_{[0;1)^2} + E_{m, n}(\xi, \eta) \overline{E_{m', n'} (\xi, \eta)} + \, d\xi d\eta + .\qedhere + \] +\end{proof} + +\subsection{Laplacian operator} + +\subsection{Eigenvalue Problem} -- cgit v1.2.1