202209300929 Theorem 4.1.3

Prove Theorem 4.1.3.

Let (X,d) and (Y,\rho ) be metric spaces and f:X\to Y be a function. Then, f is continuous at a point x_0 if and only if f(x_n)\to f(x_0), for every sequence \{ x_n\} \subset X with x_n\to x_0.

Extracted from K. J. Pawan & A. Khalil. (2004). Metric Spaces.


Background.

Define, by open spheres, continuity of a function f at a point x_0:

\forall\, x,\exists\, x_0\in X, \forall\, \epsilon >0, \exists\,\delta >0, d(x,x_0)<\delta\Rightarrow \rho (f(x),f(x_0))<\epsilon

in other words,

x\in S_\delta (x_0)\Rightarrow f(x)\in S_\epsilon (f(x_0)),

or the same,

f(S_\delta (x_0))\subset S_\epsilon (f(x_0)).


Proof.

\begin{aligned} & f\textrm{ is continuous at }x_0 \\ \Leftrightarrow\enspace & f(S_\delta (x_0))\subset S_\epsilon (f(x_0))\textrm{ for some } \delta>0\textrm{ and any }\epsilon >0 \\ & \\ & \textrm{for every sequence }\{ x_n\}\subset X\textrm{ with }x_n\textrm{ tends to }x_0 \\ \Leftrightarrow\enspace & \textrm{there exists an }N>0\textrm{ such that }x_n\in S_{\delta}(x_0)\textrm{ for all }n>N \\ \end{aligned}

\begin{aligned} & x_n\in S_\delta (x_0) \Longrightarrow f(x_n)\in f(S_\delta (x_0))\subset S_\epsilon (f(x_0)) \\ \Leftrightarrow\enspace & f(x_n) \to f(x_0) \\ \end{aligned}

202010230028 Problem 2.4.11

Let (X,d) be a metric space, a\in X and 0<r<r'.

Prove that the set

\{ x\in X:\enspace r<d(x,a)<r' \}

is open in (X,d).


Setup.

Understand the definition given to each of the following:

First, what is meant by whether a set is open or not in some metric space?

Definition. (open set) Let (X,d) be a metric space. A set G\subset X is said to be an open set if it is a neighborhood of each of its points. (Equivalently, a set G\subset X is said to be an open set
if for each x\in G, there exists an r>0 such that S_r(x)\subset G.)

Please refer to pg. 20, Jain and Ahmad’s Metric Spaces.

Second, what is referred to as a neighborhood of some point(s)?

Definition. (neighborhood) Let (X,d) be a metric space and x\in X. A set N\subset X is said to be a neighborhood (nbd) of x
if there exists an open sphere centred at x and contained in N,
i.e., if S_r(x)\subset N for some r>0.

Please refer to pg. 19, Jain and Ahmad’s Metric Spaces.

Third, what is an open sphere?

Definition. (open sphere) Let (X,d) be a metric space. Let x\in X and r>0 be a real number. The open sphere with centre x and radius r, denoted by S_r(x), the subset of X given by S_r(x)=\{ y\in X:\enspace d(x,y)<r \} N.b. An open sphere is always non-empty since it contains its centre at least.

Please refer to pg. 16, Jain and Ahmad’s Metric Spaces.

202010022249 Problem 2.1.10

In C[0,1], determine the values of d_{\infty}(x,y) and d_{1}(x,y), when

(a) x(t)=t^3+t+1 and y(t)=t^3+t^2+\frac{1}{2}t+1;

(b) x(t)=\sin t and y(t)=t;
(c) x(t)=\sin t and y(t)=t-\displaystyle{\frac{t^3}{6}};
(d) x(t)=\mathrm{exp}(t) and y(t)=\displaystyle{\sum_{m=0}^n}\frac{t^m}{m!}.


Recall.

Definition. (uniform metric) Let C[a,b] be the set of all real-valued continuous functions defined on [a,b]. For any x,y\in C[a,b], define the uniform metric d_{\infty}:

d_{\infty}(x,y)=\displaystyle{\max_{t\in [a,b]}}|x(t)-y(t)|.

N.b. If we let B[a,b] be the set of all real-valued functions defined and bounded on [a,b], the uniform metric is then defined

d_{\infty}(x,y)=\displaystyle{\sup_{t\in [a,b]}}|x(t)-y(t)|.

(cited from Examples 14 and 15, pg. 13, Pawan K. Jain and Khalil Ahmad’s Metric Spaces (2e) on Introductory Concepts)

Definition. For any x, y\in C[a,b], define

d_1(x,y)=\displaystyle{\int_a^b}|x(t)-y(t)|\,\mathrm{d}t

N.b. d_1(x,y) represents the absolute area between the functions x and y as a measure of the distance between these two functions.

(cited from Example 16, pg. 14, Pawan K. Jain and Khalil Ahmad’s Metric Spaces (2e) on Introductory Concepts)


Solution.

(a)

\begin{aligned} & \quad\, d_{\infty} (x,y) \\ &= \max_{t\in [0,1]} |x(t)-y(t)| \\ \end{aligned}


Roughwork.

\begin{aligned} & |x(t)-y(t)| \\ = & \bigg| (t^3+t+1)-(t^3+t^2+\frac{1}{2}t+1)\bigg| \\ = & \Big|-t^2+\frac{1}{2}t\Big| \\ \stackrel{\textrm{def}}{=} & f(t) \\ \end{aligned}


Approach.

To know the maximum value of |x(t)-y(t)|, apply differentiation to

f(t)=-t^2+\displaystyle{\frac{1}{2}}t

and attain

\begin{aligned} f(t) & = -t^2 + \frac{1}{2}t \\ f'(t) & = -2t+\frac{1}{2} \\ f'(t) & = 0 \Leftrightarrow t=\frac{1}{4} \\ \end{aligned}

If the quadratic function f(t) is plotted in a graph, a parabola admits of no inflexion points, needless to check on f''(x)=0. So,

\begin{array}{c|c|c|c|c|c} & t=0 & 0<t<\frac{1}{4} & t=\frac{1}{4} & \frac{1}{4}<t<1 & t=1 \\ &&&&&\\ \hline &&&&&\\ f(t) & 0 & \dots & \displaystyle{\frac{1}{16}} & \dots & -\displaystyle{\frac{1}{2}} \\ &&&&&\\ \hline &&&&&\\ f'(t) & \displaystyle{\frac{1}{2}}\enspace (>0) & \dots & 0\enspace (=0) & \dots & -\displaystyle{\frac{3}{2}}\enspace (<0) \\ &&&&&\\ \hline &&&&&\\ \textrm{plot} & \diagup & \dots & --- & \dots & \diagdown \\ &&&&&\\ \end{array}

The continuous function f(t) in the closed interval [0,1] attains its maximum value f(\frac{1}{4})=\frac{1}{16} when t=\frac{1}{4}.

\begin{aligned} & d_{\infty} (x,y) \\ = & \max_{t\in [0,1]} |x(t)-y(t)| \\ = & \max_{t\in [0,1]} |f(t)| \\ = & \frac{1}{16} \qquad\qquad\qquad \checkmark\\ \end{aligned}

and should you think of what follows as quite right

\begin{aligned} d_1(x,y) & = \int_0^1 |x(t)-y(t)| \,\mathrm{d}t \\ & = \int_0^1 f(t)\,\mathrm{d}t \\ & = \int_0^1 \bigg| -t^2 + \frac{1}{2}t  \bigg| \, \mathrm{d}t \\ & = \bigg[ -\frac{t^3}{3} + \frac{t^2}{4} \bigg] \bigg|_0^1 \\ & = -\frac{1}{12}\qquad\qquad\qquad \times \\ \end{aligned}

you might have rather mistaken calculus.


Correction.

Get back to the basics,

\begin{aligned} f(t) & = 0 \\ \bigg| -t^2+\frac{1}{2}t \bigg| & = 0 \\ t^2-\frac{1}{2}t &= 0 \\ (t-\frac{1}{2})t & = 0 \\ t & = 0\quad \textrm{\scriptsize{OR}}\quad \frac{1}{2} \\ \end{aligned}

From the previous graph of C[0,1], f(t) is found to be positive when t\in (0,0.5), zero when t\in \{ 0\} \cup\{ 0.5\}, and negative when t\in (0.5,1].

Doing it step-by-step,

\begin{aligned} d_1(x,y) & = \int_0^1 \bigg| -t^2+\frac{1}{2}t \bigg| \,\mathrm{d}t \\ & = \int_{0}^{0.5}\mathrm{d}t\enspace \bigg| -t^2+\frac{1}{2}t \bigg| + \int_{0.5}^{1}\mathrm{d}t \enspace \bigg| -t^2+\frac{1}{2}t \bigg| \\ & = \int_{0}^{0.5}\mathrm{d}t\enspace \bigg( - t^2 + \frac{1}{2}t \bigg) + \int_{0.5}^{1}\mathrm{d}t \enspace \bigg( t^2-\frac{1}{2}t\bigg) \\ \end{aligned}

Evaluating term-by-term, the first term being

\begin{aligned} & \int_{0}^{0.5} \bigg( -t^2 + \frac{1}{2}t \bigg) \,\mathrm{d}t \\ = & \bigg[ -\frac{t^3}{3} + \frac{t^2}{4} \bigg] \bigg|_{0}^{0.5} \\ = & \bigg[ -\frac{(0.5)^3}{3} + \frac{(0.5)^2}{4} \bigg] - \bigg[ -\frac{(0)^3}{3} + \frac{(0)^2}{4} \bigg] \\ \dots & \enspace \textrm{by arithmetic}\enspace \dots \\ = & \frac{1}{48} \\ \end{aligned}

and the second term being

\begin{aligned} & \int_{0.5}^{1}\bigg( t^2-\frac{1}{2}t\bigg) \,\mathrm{d}t \\ = & \bigg[ \frac{t^3}{3} - \frac{t^2}{4} \bigg]\bigg|_{0.5}^{1} \\ = & \bigg[ \frac{(1)^3}{3} - \frac{(1)^2}{4} \bigg] - \bigg[ \frac{(0.5)^3}{3} - \frac{(0.5)^2}{4} \bigg] \\ \dots & \enspace \textrm{by arithmetic}\enspace \dots \\ = & \bigg( \frac{1}{12} \bigg) - \bigg( -\frac{1}{48} \bigg) \\ = & \frac{5}{48} \\ \end{aligned}

In sum,

d_{1}(x,y)=\displaystyle{\frac{1}{48}+\frac{5}{48}=\frac{6}{48}=\frac{1}{8}}=0.125.


Part (b), (c), and (d) are not chosen.

202010020718 Problem 2.1.2

Let (X,d) be a metric space and let k be a fixed positive real number. For x,\, y\in X, define

d^{*}=kd(x,y).

Prove that d^{*} is a metric on X.


Recall.

Definition. (metric) Let X be a non-empty set. A metric on X is a real-valued function d:\enspace X\times X\rightarrow \mathbb{R} satisfying the following conditions iiv:

i. d(x,y)\ge 0;
ii. d(x,y)=0\Leftrightarrow x=y;
iii. (Symmetry) d(x,y)=d(y,x);
iv. (Triangle Inequality) d(x,y)\le d(x,z)+d(z,y)for any x,\, y,\, z\in X.

N.b. Given x,\,y\in X, d(x,y) is sometimes called the distance between x and y with respect to d.


Proof.

i.

WTS (wish to show)

d^{*}(x,y)\ge 0

By definition d^{*}(x,y)=kd(x,y) and in that the metric d is let clear (\therefore d(x,y)\ge 0) and k a fixed positive real number (\therefore k>0),

one can see

\begin{aligned} d^{*}(x,y) & = kd(x,y) \\ \textrm{\dots because\enspace} & k>0 \enspace \textrm{and}\enspace d(x,y)\ge 0\textrm{\enspace \dots}\\ d^{*}(x,y) & \geqslant 0 \\ \end{aligned}

\therefore Condition i. is made.

ii.

\begin{aligned} d^{*}(x,y) & = 0 \\ \Leftrightarrow kd(x,y) & = 0 \\ \dots\enspace \textrm{as}\enspace k>0 \enspace & \textrm{so}\enspace k\neq 0\enspace \dots \\ \Leftrightarrow d(x,y) & = 0 \\ \dots\enspace \textrm{as}\enspace d \enspace \textrm{was} &\enspace\textrm{foretold to be a metric}\enspace \dots \\ \Leftrightarrow x & = y \\ \end{aligned}

\therefore Condition ii. is made.

iii.

NTS (need to show)

d^{*}(x,y)=d^{*}(y,x)

\begin{aligned} \textrm{LHS} & = d^{*}(x,y) \\ & = kd(x,y) \\ \dots \enspace & \textrm{by the symmetric property of }d\enspace \dots \\ & =kd(y,x) \\ & = d^{*}(y,x) \\ & = \textrm{RHS} \\ \end{aligned}

\therefore Condition iii. is made.

iv.

RTP (required to prove)

d^{*}(x,y)\leqslant d^{*}(x,z)+d^{*}(z,y)

One starts with the left hand side,

\begin{aligned} \textrm{LHS} & = d^{*}(x,y) \\ & = kd(x,y) \\ \dots \textrm{as does}\enspace & d(x,y)\le d(x,z)+d(z,y)\enspace\textrm{the metric}\enspace d\enspace \textrm{do} \dots \\ & \le k\Big( d(x,z) + d(z,y) \Big) \\ & = kd(x,z) + kd(z,y) \\ & = d^{*}(x,z) + d^{*}(z,y) \\ & = \textrm{RHS} \\ \end{aligned}

Condition iv. is made.


In conclusion, (X,d^{*}) is a metric space metered by a well-defined metric d^{*}. This metric space shall simply be called X hence.

202009240421 Problem 2.1.1

For x,\, y\in \mathbb{K} (\mathbb{R} or \mathbb{C}), define

d(x,y)=\mathrm{min}\big\{ 1,\,  |x-y| \big\}.

Prove that d is a metric on \mathbb{K}.


Motivation.

A ruler is marked by rules for the sake of measuring things. Not so much common to a ruler, but well worth the rule for the general, that a metric must measure its metric space, with the metric defined below:

Definition. (metric) Let X be a non-empty set. A function d: X\times X \rightarrow \mathbb{R} is said to be a metric on X if it satisfies the following conditions:

i. d(x,y)\ge 0\qquad \forall\, x,\, y\in X;
ii. d(x,y)=0 \Leftrightarrow x=y\qquad x,\, y\in X;
iii. d(x,y)=d(y,x) \qquad \forall\, x,\, y\in X;
iv. d(x,y)\le  d(x,z)+d(z,y) \qquad \forall\, x,\, y,\, z\in X.


Remark.

i. As is known, distance should be either positive or zero (i.e., non-negative); ii. We are in one only by discrimination; iii. Fair and just from a symmetric point of view; and iv. The straighter the path, the shorter the distance (also known as the Triangle Inequality).


Proof.

Assume x\, ,y\in\mathbb{C} for convenience. (Provided \mathbb{R}\subset \mathbb{C}, the assumption is ready for reduction or extension.)

i.

If the minimum \mathrm{min}\big\{ 1, |x-y|\big\} = 1, condition (i) d(x,y)=1 \ge 0 is seen. If the minimum \mathrm{min}\big\{ 1,|x-y|\big\} = |x-y|, be it called the absolute value, the magnitude, the norm, the modulus, or whatsoever, a complex number is non-negative in norm.


Recall. (Norm of complex conjugate)

A complex number z=x+\textrm{i}\, y contains two parts, the real part x and the imaginary part y. The norm |z| (and the norm |\bar{z}| of its complex conjugate \bar{z}=x-\textrm{i}\, y)
is defined by the formula:

|z|=|x+\textrm{i}\, y|=\sqrt{\big[\textrm{Re}(z)\big]^2+\big[\textrm{Im}(z)\big]^2}=\sqrt{x^2+y^2}.

\dagger It turns out that |z|=\sqrt{(x)^2+(y)^2}=\sqrt{(x)^2+(-y)^2}=|\bar{z}| (where x,\, y\in\mathbb{R}) is positive iff x\enspace\textrm{\scriptsize OR}\enspace y\neq 0, and zero iff x=y=0, but never negative.

\ddagger The sum, the difference, the product, and the quotient of two complex numbers is one another complex number for the complex number field is algebraically closed.


ii.

(only-if) Giving a try straightforth:

\begin{aligned} d(x,y) & = 0 \\ \textrm{min}\big\{ 1, |x-y| \big\} & = 0 \\ |x-y| & = 0\qquad \textrm{\scriptsize OR\qquad\quad } 1 = 0 \qquad \textrm{(rejected for\enspace }1\neq 0) \\ |x-y| & = 0 \\ x-y & = 0 \\ x & = y \end{aligned}

(if) In reverse from backward:

\begin{aligned} x & = y \\ x-y & = 0 \\ |x-y| & = 0 \\ \textrm{min}\big\{ 1, |x-y| \big\} & = 0 \\ d(x,y) & = 0 \end{aligned}

If provided with appropriate explanation, the proof can be shortened by use of the two-way if-and-only-if.

iii.

Suffice it to check whether d(x,y)=d(y,x) is true or not.

\begin{aligned} \textrm{LHS} & = d(x,y) \\ & = \textrm{min}\big\{ 1,\, |x-y| \big\} \\ & = \textrm{min}\big\{ 1,\, |y-x| \big\} \\ & = d(y,x) \\ & = \textrm{RHS} \end{aligned}


Roughwork.

\forall\, x,\,y \in\mathbb{C}, let x=a+\textrm{i}\, b and y=c+\textrm{i}\, d where a, b, c, and d are real numbers. Then,
\begin{aligned} x-y & = (a+\textrm{i}\, b)-(c+\textrm{i}\, d) \\ x-y & = (a-c) + \textrm{i}\, (b-d) \\ |x-y| & = \sqrt{(a-c)^2+(b-d)^2} \\ |x-y| & = \sqrt{(c-a)^2+(d-b)^2} \\ |x-y| & = |(c-a) + \textrm{i}\, (d-b)| \\ |x-y| & = |(c+\textrm{i}\, d) - (a+\textrm{i}\, b)| \\ |x-y| & = |y-x| \\ \end{aligned}


iv.

Given here are some equations, I write out all them lest I might forget any:

d(x,y)=\textrm{min}\big\{ 1, |x-y| \big\},
d(x,z)=\textrm{min}\big\{ 1, |x-z| \big\},
d(z,y)=\textrm{min}\big\{ 1, |z-y| \big\}.

RTP (i.e. required to prove):

d(x,y)\le d(x,z) + d(z,y)

\begin{aligned} \textrm{RHS} & = d(x,z) + d(z,y) \\ & = \textrm{min}\big\{ 1,|x-z|\big\} + \textrm{min}\big\{ 1,|z-y|\big\} \\ & = \textrm{min}\big\{ 2,\, 1+|z-y|,\, 1+|x-z|,\, |x-z|+|z-y|\big\} \\ \end{aligned}

If d(x,y)=\textrm{min}\big\{ 1, |x-y|\big\} \stackrel{?}{=}1, so what have I done with?

WTS (i.e. wish to show):

1\le \textrm{min}\big\{ 2, 1+|z-y|, 1+|x-z|, |x-z|+|z-y|\big\}

The following inequalities hold evidently:

\begin{aligned} 1 & \le 2 \\ 1 & \le 1+|z-y| \\ 1 & \le 1+|x-z| \\ \end{aligned}

But 1\le |x-z|+|z-y| has not yet been ascertained.

The Argand diagram above replaces the usual x– and y-axes of the Cartesian plane with the real and the imaginary axes of Argand plane.

Owing to my giving too raw and rude maybe a proof, the problem should have otherwise been treated case-by-case. I.e., they are in either case:

1\le |x-y| \qquad \qquad \textrm{\scriptsize OR}\qquad\qquad 1> |x-y|


Just do it by rote:

\begin{aligned} & \quad\, d(x,y) \\ & =\textrm{min}\big\{  1, |x-y| \big\} \\ & =\textrm{min}\big\{  1, |x-z+z-y| \big\} \\ \dots \, & \textrm{by the Triangle Inequality}\, \dots \\ & \leqslant \textrm{min} \big\{ 1, |x-z| + |z-y|  \big\}  \\ & \leqslant \textrm{min} \big\{ 1, |x-z| \big\} + \textrm{min} \big\{ 1, |z-y| \big\} \\ & \leqslant d(x,z) + d(z,y) \\ \end{aligned}

d(x,y) \le d(x,z) + d(z,y)

QED