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\begin{theorem}
    A continuous real-valued function on a closed
    bounded interval is uniformly continuous.
\end{theorem}

\begin{proof}
    Let $f : [a, b] \to \RR$ and suppose $f$ is
    continuous but not uniformly continuous. Then
    we can find an $\eps > 0$ such that for all
    $\delta > 0$ there exists $x, y \in [a, b]$
    with $|x - y| < \delta$ but $|f(x) - f(y)| \ge
    \eps$. In particular taking $\delta =
    \frac{1}{n}$ for $n = 1, 2, 3, \dots$, we can
    find sequences $(x_n)$, $(y_n)$ in $[a, b]$
    with for each $n$, $|x_n - y_n| < \frac{1}{n}$
    but $|f(x_n) - f(y_n)| \ge \eps$. The sequence
    $(x_n)$ is bounded so by Bolzano Weierstrass
    it has a convergent subsequence $x_{n_j} \to
    x$ say. And $[a, b]$ is a closed interval so
    $x \in [a, b]$. Then $x_{n_j} - y_{n_j} \to 0$
    so also $y_{n_j} \to x$. But $f$ is continuous
    at $x$, so there exists $\delta > 0$ such that
    for all $y \in [a, b]$, $|y - x| < \delta$
    implies that $|f(y) - f(x)| < \frac{\eps}{2}$.
    Take such a $\delta$. As $x_{n_j} \to x$ we
    can find $J_1$ such that $j \ge J_1$ implies
    that $|x_{n_j} - x| < \delta.$ Similarly can
    find $J_2$ such that $j \ge J_2$ implies
    $|y_{n_j} - x| < \delta$. Now let $j =
    \max\{J_1, J_2\}$. Then $|x_{n_J} - x| <
    \delta$ and $|y_{n_j} - x| < \delta$ so we
    have $|f(x_{n_J}) - f(x)| < \frac{\eps}{2}$
    and $|f(y_{n_j}) - f(x)| < \frac{\eps}{2}$.
    Then
    \[ |f(x_{n_j}) - f(y_{n_j})| \le |f(x_{n_j}) -
    f(x)| + |f(x) - f(y_{n_j})| < \frac{\eps}{2} +
    \frac{\eps}{2} = \eps \]
    contradiction.
\end{proof}

\begin{corollary}
    A continuous real-valued function on a closed
    bounded interval is bounded.
\end{corollary}

\begin{proof}
    Let $f : [a, b] \to \RR$ be continuous, and so
    uniformly continuous by Theorem 12. Then can
    find $\delta > 0$ such that
    \[ \forall x, y \in [a, b] \quad |x - y| <
    \delta \implies |f(x) - f(y)| < 1 \]
    Let $M = \left\lceil \frac{b - a}{\delta}
    \right\rceil$. Now let $x \in [a, b]$. We can
    find $a = x_0 \le x_1 \le \cdots \le x_m = x$,
    with $|x_i - x_{i - 1}| < \delta$ for each
    $i$. Hence
    \begin{align*}
        |f(x)| = |f(a) + \sum_{i = 1}^M f(x_i) -
        f(x_{i - 1})| \\
        &\le |f(a)| + \sum_{i = 1}^M |f(x_i) -
        f(x_{i - 1})| \\
        &< |f(a)| + \sum_{i = 1}^M 1 \\
        &= M + f(a)
    \end{align*}
\end{proof}

\begin{corollary}
    A continuous real-valued function on a closed
    bounded interval is integrable.
\end{corollary}

\begin{proof}
    Let $f : [a, b] \to \RR$ be continuous and so
    uniformly continuous by Theorem 12. Let $\eps
    > 0$. Then can find $\delta > 0$ such that for
    all $x, y \in [a, b]$, $|x - y| < \delta
    \implies |f(x) - f(y)| < \eps$. Let
    $\mathcal{D} = \{x_0 < x_1 < \cdots < x_n\}$
    be a dissection such that for each $i$ we have
    $x_i - x_{i - 1} < \delta$. Let $i \in \{1,
    \dots, n\}$. Then for any $u, v \in [x_{i -
    1}, x_i]$ we have $|u - v| < \delta$ so $|f(u)
    - f(v)| < \eps$. Hence
    \[ \sup_{x \in [x_{i - 1}, x_i]} f(x) -
    \inf_{x \in [x_{i - 1}, x_i]} f(x) \le \eps \]
    Hence:
    \begin{align*}
        S(f, \mathcal{D}) - s(f, \mathcal{D})
        &= \sum_{i = 1}^n (x_i - x_{i - 1})
        (\sup_{x \in [x_{i - 1}, x_i]} f(x) -
        \inf_{x \in [x_{i - 1}, x_i]} f(x)) \\
        &\le \sum_{i = 1}^n (x_i - x_{i - 1}) \eps
        \\
        &= \eps \sum_{i = 1}^n (x_i - x_{i - 1})
        \\
        &= \eps(b - a)
    \end{align*}
    But $\eps(b - a)$ can be made arbitrarily
    small by taking $\eps$ small. So by Riemann's
    criterion $f$ is integrable over $[a, b]$.
\end{proof}

\newpage
\section{Metric Spaces}

\subsection{Definitions and Examples}

Can we think about convergence in a more general
setting? What do we really need? - A notion of
distance. \myskip
In $\RR$: distance $x$ to $y$ is $|x - y|$. \\
In $\RR^2$: distance $x$ to $y$ is $\|x - y\|$. \\
For functions distance $x$ to $y$ is
\[ \sup \sup_{x \in X} |f(x) - g(x)| \]
(where this exists, i.e. if $f - g$ is bounded).
\myskip
Triangle inequality was often important.

\begin{flashcard}
    \begin{definition*}[Metric space]
        A \emph{metric space} is a set $X$ endowed
        with a \emph{metric} $d$, i.e. a function
        $d : X^2 \to \RR$ satisfying:
        \begin{enumerate}[(i)]
            \item \cloze{$d(x, y) \ge 0$ for all $x, y
                \in X$ with equality if and only
                if $x = y$};
            \item \cloze{$d(x, y) = d(y, x)$ for all $x,
                y \in X$};
            \item \cloze{$d(x, z) \le d(x, y) + d(y, z)$
                for all $x, y, z \in X$}.
        \end{enumerate}
    \end{definition*}
\end{flashcard}

\noindent
Could define a metric space as an ordered pair
$(X, d)$. If it is obvious what $d$ is, sometimes
write ``The metric space $X$\dots''

\subsubsection*{Examples}

\begin{enumerate}[(1)]
    \item $X = \RR$, $d(x, y) = |x - y|$ ``The
        \emph{usual metric} on $\RR$''.
    \item $X = \RR^n$ with the \emph{Euclidean
        metric}
        \[ d(x, y) = \|x - y\| = \sqrt{\sum_{i =
        1}^n (x_i - y_i)^2} \]
    \item Let $Y \subset \RR$. Take
        \[ X = B(Y) = \{f : Y \to \RR | \text{$f$
        is bounded}\} \]
        now we can use the \emph{uniform metric}
        \[ d(f, g) = \sup_{x \in Y} |f(x) - g(x)| \]
        (we need the bounded condition for this
        supremum to necessarily exist). Check
        triangle inequality: let $f, g, h \in
        B(Y)$. Let $x \in Y$. Then
        \begin{align*}
            |f(x) - h(x)|
            &\le |f(x) - g(x)| + |g(x) - h(x)| \\
            &\le d(f, g) + d(g, h)
        \end{align*}
        Take sup over all $x \in Y$ we get
        \[ d(f, h) \le d(f, g) + d(g, h) \]
        \begin{remark*}
            Suppose $(X, d)$ is a metric space and
            $Y \subset X$. Then $d \mid_{Y^2}$ is
            a metric on $Y$. We say $Y$ with this
            metric is a \emph{subspace} of $X$.
        \end{remark*}
    \item Subspaces of $\RR$: any of $\QQ, \ZZ,
        \NN, [0, 1], \dots$ with the usual metric
        of $d(x, y) = |x - y|$.
    \item Recall that a continuous function on a
        closed bounded interval is bounded. Define
        $C([a, b]) = \{f : [a, b] \to \RR |
        \text{$f$ is continuous}\}$. This is a
        subspace of $B([a, b])$.
    \item The empty metric space $X = \empty$ with
        the empty metric.
    \item Can define different metrics on the same
        set, for example the $l_1$ metric on
        $\RR^n$:
        \begin{flashcard}[l1-metric-Rn]
        \prompt{$l_1$ metric on $\RR^n$? \\}
        \[ \cloze{d(x, y) = \sum_{i = 1}^n |x_i -
        y_i|} \]
        \end{flashcard}
    \item
        \begin{flashcard}[linf-metric-Rn]
        The $l_\infty$ metric on $\RR^n$:
        \[ \cloze{d(x, y) = \max_i |x_i - y_i|} \]
        \end{flashcard}
        (proof of triangle inequality is the same
        as for uniform metric in example 3).
    \item
        \begin{flashcard}[L1-metric-Cab]
        On $C([a, b])$ we can define the $L_1$
        metric
        \[ \cloze{d(f, g) = \int_a^b |f - g|} \]
        \end{flashcard}
    \item $X = \CC$ with
        \[ d(z, w) = \begin{cases}
            0 & \text{if $z = w$} \\
            |z| + |w| & \text{if $z \neq w$}
        \end{cases} \]
        triangle inequality? Need $d(u, w) \le
        d(u, v) + d(v, w)$
        \begin{itemize}
            \item if $u = w$, $LHS = 0$
            \item If $u = v$ or $v = w$ then $LHS
                = RHS$
            \item If $u, v, w$ all distinct:
                \[ |u| + |w| < |u| + |w| + 2|w| \]
                ``British rail metric'' or ``SNCF
                metric'':
                \begin{center}
                    \includegraphics[width=0.6\linewidth]
                    {images/5f17847e4c7811ed.png}
                \end{center}
        \end{itemize}
\end{enumerate}