% !TeX root = ../../script.tex \documentclass[../../script.tex]{subfiles} \begin{document} \section{Linear Operators} Throughout this chapter $X$ and $Y$ will denote vector spaces over the same scalar field $\field$. Also, I want to quickly recap some normed vector spaces that we will use from here on out. \begin{enumerate}[(i)] \item The real numbers \begin{align*} X &= \realn \\ \norm{x} &= \abs{x}, \quad x \in \realn \end{align*} \item The euclidian space \begin{align*} X &= \realn^n \\ \norm{x} &= \left(\sum_{k=1}^n \xi_k^2\right)^{\rec{2}}, \quad x = (\xi_1, \cdots, \xi_n) \in \realn^n \end{align*} \item The space of bounded sequences $l^{\infty}$ \begin{align*} X &= l^{\infty} := \set[(\xi_k) \text{ bounded}]{(\xi_k) \subset \realn} \\ \norm{x}_{l^{\infty}} &= \sup_{k \in \natn} \abs{\xi_k}, \quad x = (\xi_n) \in l^{\infty} \end{align*} \item The space of converging sequences $c$ \begin{align*} X &= c = \set[(\xi_k) \text{ is convergent}]{(\xi_k) \subset \realn} \\ \norm{x}_c &= \sup_{k \in \natn} \abs{\xi_k}, \quad x = (\xi_n) \in c \end{align*} $c$ can be considered a subspace of $l^{\infty}$ because it is a subset of $l^{\infty}$ and its norm is just a restriction of $\dnorm_{l^{\infty}}$. \item The space of bounded functions $B(A)$ \begin{align*} X &= B(A) = \set[f \text{ bounded}]{f: A \subset \realn \rightarrow \realn} \\ \norm{x}_{\infty} &= \sup_{t \in A} \abs{f(t)}, \quad f \in B(A) \end{align*} \item The space of continuous functions $C(A)$ \begin{align*} X &= C(A) = \set[f \text{ continuous}]{f: A \rightarrow \realn} \\ \norm{x}_C &= \max_{t \in A} \abs{f(t)}, \quad f \in C(A) \end{align*} \item Sequence spaces $l^p, ~p \ge 1$ \begin{align*} X &= l^p = \set[\sum_{k=1}^{\infty} \abs{\xi_k}^p < \infty]{(\xi_n) \subset \realn} \\ \norm{x}_{l^p} &= \left( \sum_{k=1}^{\infty} \abs{\xi_k}^p \right)^{\rec{p}}, \quad x = (\xi_n) \in l^p \end{align*} \item The space of Lebesgue measurable functions $L^p(A), ~p \ge 1$ \begin{align*} X &= L^p(A) = \set[\int_A \abs{f(t)}^p \dd{t} < \infty]{f: A \rightarrow \realn} \\ \norm{x}_{L^p} &= \left(\int_A \abs{f(t)}^p \dd{t} \right)^{\rec{p}}, \quad f \in L^p(A) \end{align*} \end{enumerate} \begin{defi} A linear operator $T$ is a mapping \[ T: \domain(T) \subset X \longrightarrow Y \] such that \begin{enumerate}[(i)] \item The domain $\domain(T)$ is a subspace of $X$ \item $\forall x, y \in \domain(T), ~\forall \alpha \in \field: \quad T(x + \alpha y) = Tx + \alpha Ty$ \end{enumerate} If $Y = \field$, then $T$ is said to be a linear functional. \end{defi} \begin{eg} \begin{enumerate}[(i)] \item Let $X = \realn^n$ and $Y = \realn^m$. If $A \in \realn^{m \times n}$ then we can define \[ Tx = Ax, \quad x \in \realn^n \] such that for $x = (\xi_1, \cdots, \xi_n)$ we have \[ Tx = \begin{pmatrix} a_{11} & \cdots & a_{1n} \\ \vdots & \ddots & \vdots \\ a_{m1} & \cdots & a_{mn} \end{pmatrix} \begin{pmatrix} \xi_1 \\ \vdots \\ \xi_n \end{pmatrix} = \begin{pmatrix} \eta_1 \\ \vdots \\ \eta_m \end{pmatrix} \] Then $\domain(T) = \realn^n$ and $T$ is a linear operator. \item Let $X = C([a, b])$ and $Y = C([a, b])$. Then \[ (Tx)(t) = \int_{\alpha}^t x(s) \dd{s}, \quad t \in [a, b] \] defines a linear operator with $\domain(T) = C([a, b])$. \item Consider $X = C([a, b])$ and $Y = C([a, b])$. We can define \[ (Tx)(t) = x'(t), \quad t \in [a, b] \] $T$ is a linear operator with $C([a, b]) \supset \domain(T) = C^1([a, b])$. \item Let $X = L^p([a, b])$ and $Y = L^q([a, b])$. Choose a fixed measurable function $\phi: [a, b] \rightarrow \realn$. Then \[ (Tx)(t) = \phi(t)x(t), \quad t \in[a, b] \] defines a linear operator. The domain in this case is \[ \domain(T) = \set[\int_a^b \abs{\phi(t) x(t)}^q \dd{t} < \infty]{x \in L^p([a, b])} \] \item Consider $X = l^{\infty}$ and $Y = \realn$. Then \[ Tx = \lim_{k \rightarrow \infty} \xi_k, \quad x = (\xi_k) \in l^{\infty} \] is a linear functional with $\domain(T) = c$. \end{enumerate} \end{eg} \begin{defi} Let $T: \domain(T) \rightarrow Y, ~\domain(T) \subset X$ be a linear operator. If $\exists C > 0$ such that \[ \norm{Tx} \le C\norm{x} \] then $T$ is said to be bounded. The number \[ \norm{T} = \sup_{\stackrel{x \in \domain(T)}{x \ne 0}} \frac{\norm{Tx}}{\norm{x}} \] is the operator norm of $T$. \end{defi} \begin{eg} Consider $X = Y = C([0, 1])$. We can define the operator $T$ as \[ (Tf)(t) = \int_0^t f(s) \dd{s}, \quad f \in C([0, 1]) = \domain(T) \] $T$ is a bounded operator. This can be shown by explicitely calculating the norm \begin{align*} \norm{Tf} &= \max_{t \in [0, 1]} \abs{\int_0^t f(s) \dd{s}} \\ &\le \max_{t \in [0, 1]} \int_0^t \abs{f(s)} \dd{s} \\ &\le \max_{t \in [0, 1]} \int_0^t \max_{s \in [0, 1]} \abs{f(s)} \dd{s} \\ &= \norm{f} \max_{t \in [0, 1]} \int_0^t \dd{s} = \norm{f} \max_{t \in [0, 1]} t = \norm{f} \end{align*} Thus we have shown that $\norm{T} \le 1$. We can further show that $\norm{T} = 1$. To do that, assume $f = 1$. Trivially, this results in $\norm{f} = 1$ and further \[ (Tf)(t) = \int_0^t 1 \dd{s} = t \] This gives us \[ \norm{Tf} = 1 \implies \norm{T} \ge \frac{\norm{Tf}}{\norm{f}} = 1 \] This implies $\norm{T} = 1$. \end{eg} \begin{eg} Again, consider $X = Y = C([0, 1])$. This time we look at \[ (Tf)(t) = f'(t), \quad \domain(T) = C^1([0, 1]) \] $T$ is an unbounded operator. To prove this take $f_n(t) = t^n \in C([0, 1]), ~n \ge 1$. We compute \[ \norm{f_n} = \max_{t \in [0, 1]} \abs{t^n} = 1, \quad \norm{Tf_n} = \max_{t \in [0, 1]} \abs{n t^{n-1}} = n \] Then \[ \norm{T} \ge \frac{\norm{Tf_n}}{\norm{f_n}} = n, \quad \forall n \ge 1 \] So there doesn't exist a $C > 0$ such that $n \le C$, thus $T$ cannot be bounded. \end{eg} \begin{thm} Let $X$ be a finite-dimensional normed space. If $T$ is a linear operator on $X$, then $T$ is bounded. \end{thm} \begin{proof} \noproof \end{proof} \begin{defi} Let $T: \domain(T) \rightarrow Y$ be a linear operator. $T$ is said to be continuous in $x_0 \in \domain(T)$ if \[ \forall \epsilon > 0, ~\exists \delta > 0: \quad \norm{x - x_0} < \delta \implies \norm{Tx - Tx_0} < \epsilon, \quad \forall x \in \domain(T) \] \end{defi} \begin{thm} Let $T: \domain(T) \rightarrow Y$ be a linear operator. Then \begin{enumerate}[(i)] \item $T$ is continuous $\iff$ $T$ is bounded \item If $T$ is continuous in a single point, then it is continuous everwhere \end{enumerate} \end{thm} \begin{proof} To prove the first statement, we want to consider $T \ne 0$ (since $T = 0$ is trivial). This implies that $\norm{T} \ne 0$. Assume $T$ is bounded, and take $x_0 \in \domain(T)$. Now let $\epsilon > 0$ and $\delta = \frac{\epsilon}{\norm{T}}$ such that $\norm{x - x_0} < \delta, ~x \in \domain(T)$. Then \begin{equation} \norm{Tx - Tx_0} = \norm{T(x - x_0)} \le \norm{T}\norm{x - x_0} < \norm{T} \delta = \norm{T} \frac{\epsilon}{\norm{T}} = \epsilon \end{equation} Thus proving that $T$ is continuous. Now inversely, let $T$ be continuous in $x_0 \in \domain(T)$. If we choose $\epsilon = 1$, then we can find a $\delta$ such that \begin{equation} \norm{x - x_0} < \delta \implies \norm{Tx - Tx_0} < \epsilon = 1 \end{equation} If we now take any $y \ne 0$ from $\domain(T)$ and set $x = x_0 + \frac{\delta}{2 \norm{y}} y$, then we can show \begin{equation} \norm{x - x_0} = \frac{\delta}{2} < \delta \implies \norm{Tx - Tx_0} < \epsilon = 1 \end{equation} Therefore we have \begin{equation} 1 > \norm{Tx - Tx_0} = \norm{T(x - x_0)} = \norm{T \frac{\delta}{2\norm{y}} y} = \frac{\delta}{2\norm{y}} \norm{Ty} \end{equation} Thus \begin{equation} \frac{\delta}{2\norm{y}} \norm{Ty} < 1 \implies \norm{Ty} < \frac{2}{\delta} \norm{y} \end{equation} Since $y \in \domain(T)$ was chosen arbitrarily, this implies that $T$ is bounded. The second statement follows trivially from the first one, as we have shown that if $T$ is continuous in one point $x_0$, it is bounded and if it is bounded then it is continuous everywhere. \end{proof} \begin{cor} Let $T$ be a bounded linear operator. Then \begin{enumerate}[(i)] \item For $x_n, x \in \domain(T)$ we have $x_n \rightarrow x \implies Tx_n \rightarrow Tx$ \item The set $\ker(T) = \set[Tx = 0]{x \in \domain(T)}$ is a null set and closed in $X$ \end{enumerate} \end{cor} \begin{proof} \reader \end{proof} \begin{thm} Let $T: \domain(T) \rightarrow Y$ be a bounded linear operator, with $Y$ a Banach space. Then $T$ has an extension $\tilde{T}: \closure{\domain(T)} \rightarrow Y$ where $\tilde{T}$ is a bounded linear operator and $\norm*{\tilde{T}} = \norm{T}$. \end{thm} \begin{proof} In this proof we only want to show how such a $\tilde{T}$ can be constructed. Let $x \in \closure{\domain(T)}$. Then there is a sequence $x_n \in \domain(T)$ such that $x_n \rightarrow x$. Since $T$ is linear and bounded, we can find \begin{equation} \norm{Tx_n - Tx_m} \le \norm{T(x_n - x_m)} \le \norm{T} \norm{x_n - x_m} \conv{n, m \rightarrow \infty} 0 \end{equation} So $(Tx_n)_{n \in \natn}$ is a Cauchy sequence in $Y$. Because $Y$ is a Banach space there exists some $y \in Y$ such that $Tx_n$ converges to $y$. Now we define $\tilde{T}x := y$, and show that $\tilde{T}x$ is well-defined. If $(z_n)_{n \in \natn} \subset \domain(T)$ is another sequence converging to $x$, then $Tz_n \rightarrow y'$. Now consider the sequence \begin{equation} (v_n)_{n \in \natn} = (x_1, z_1, x_2, z_2, x_3, z_3, \cdots) \end{equation} This sequence also converges to $x$, and $T v_n \rightarrow y''$. However we can also find \begin{align} T v_{2k+1} \longrightarrow y = y'' && T v_{2k} \longrightarrow y' = y'' \end{align} Thus $y = y'$. \end{proof} \end{document}