# A Proof without Calculus

Now, solve $\sin(x)=x$.

By inspection, $x=0$.

Is this the only solution?

Visually, it is difficult to tell (see Fig. 1 and Fig. 2)

Fig. 1

Fig. 2

However, we can prove that $0$ is the only solution:

Fig. 3

If $0 < x \leq 1 < \frac{\pi}{2}$ then from Fig. 3, we have

Area of triangle OAB < Area of circular sector OAB.

That is,

$\frac{1}{2}\cdot 1 \cdot \sin(x)< \frac{1}{2}\cdot 1 \cdot x$.

Hence,

$\forall 0< x \leq 1, \sin(x) < x\quad\quad\quad(1)$

If $x>1$ then

$\sin(x) \leq 1 \overset{x>1}{\implies} \sin(x) \leq 1< x \implies \forall x>1, \sin(x)

Put (1) and (2) together, we have

$\forall x > 0, \sin(x) < x\quad\quad\quad(3)$

If $x < 0$ then

$-x > 0 \overset{(3)}\implies \sin(-x) < -x \overset{\sin(-x)=-\sin(x)}\implies -\sin(x) < -x \implies \sin(x) > x$

i.e.,

$\forall x < 0, \sin(x) > x\quad\quad\quad(4)$

Therefore by (3) and (4), we conclude that

only when $x = 0, \sin(x)=x$.

# From proof to simpler proof

Solve $\sin(x)=2x$ for $x$.

By mere inspection, we have $x=0$.

Visually, it appears that $0$ is the only solution (see Fig.1 or Fig. 2)

Fig. 1

Fig. 2

To show that $0$ is the only solution of $\sin(x)=2x$ analytically, let

$f(x) = \sin(x)-2x$

$0$ is a solution of $\sin(x)=2x$ means

$f(0) = 0\quad\quad\quad(1)$

Suppose there is a solution

$x^* \neq 0\quad\quad\quad(2)$

then,

$f(x^*)=\sin(x^*)-2x^*=0\quad\quad\quad(3)$

Since $f(x)$ is an function continuous and differentiable on $(-\infty, +\infty)$,

by Lagrange’s Mean-Value Theorem (see “A Sprint to FTC“), there $\exists c \in (0, x^*)$ such that

$f(x^*)-f(0)=f'(c)(x^*-0)$

$\overset{(1), (3)}{\implies} 0 = f'(c) x^*\quad\quad\quad(4)$

$\overset{(2)}{\implies} f'(c)=0\quad\quad\quad(5)$.

We know

$f'(x) = \cos(x)-2$.

From (5), we have

$\cos(c)-2=0$.

i.e.,

$\cos(c)=2$.

This is not possible since $\forall x \in R, -1 \le \cos(x) \le 1$.

A simpler alternative without direct applying Lagrange’s Mean-Value Theorem is:

$f(x) = \sin(x)-2x \implies f'(x) = \cos(x) - 2 \overset{-1 \le \cos(x) \le 1}{\implies} f'(x) < 0\implies$

$f(x) =\sin(x)-2x$ is a strictly decreasing function.

Since $f(0) = 0, \forall x<0, f(x) > 0$ and $\forall x>0, f(x) < 0$.

Therefore, $0$ is the only solution of $f(x)=0$. i.e.,

$0$ is the only solution of $\sin(x) = 2x$.

# Analyzing Heron’s Algorithm

Finding the value of $\sqrt{a}$ for $a>0$ is equivalent to seeking a positive number $x$ whose square yields $a$: $x^2=a$. In other words, the solution to $x^2-a=0$.

We can find $\sqrt{4}$ by solving $x^2-4=0$. It is $2$ by mere inspection. $\sqrt{9}$ can also be obtained by solving $x^2-9=0$, again, by mere inspection. But ‘mere inspection’ can only go so far. For Example, what is $\sqrt{15241578750190521}$?

Method for finding the square root of a positive real number date back to the acient Greek and Babylonian eras. Heron’s algorithm, also known as the Babylonian method, is an algorithm named after the $1^{st}$ – century Greek mathematician Hero of Alexandria, It proceeds as follows:

1. Begin with an arbitrary positive starting value $x_0$.
2. Let $x_{k+1}$ be the average of $x_k$ and $\frac{a}{x_k}$
3. Repeat step 2 until the desired accuracy is achieved.

This post offers an analysis of Heron’s algorithm. Our aim is a better understanding of the algorithm through mathematics.

Let us begin by arbitrarily choosing a number $x_0>0$. If $x_0^2-a=0$, then $x_0$ is $\sqrt{a}$, and we have guessed the exact value of the square root. Otherwise, we are in one of the following cases:

Case 1: $x_0^2-a > 0 \implies x_0^2 > a \implies \sqrt{a} x_0 > a \implies \sqrt{a} > \frac{a}{x_0} \implies \frac{a}{x_0} < \sqrt{a} < x_0$

Case 2: $x_0^2-a <0 \implies x_0 < \sqrt{a} \implies \sqrt{a} x_0 < a \implies \sqrt{a} < \frac{a}{x_0} \implies x_0 < \sqrt{a} < \frac{a}{x_0}$

Both cases indicate that $\sqrt{a}$ lies somewhere between $\frac{a}{x_0}$ and $x_0$.

Let us define $e_0$ as the relative error of approximating $\sqrt{a}$ by $x_0$:

$e_0 =\frac{x_0-\sqrt{a}}{\sqrt{a}}$

The closer $e_0$ is to $0$, the better $x_0$ is as an approximation of $\sqrt{a}$.

Since $x_0>0, \sqrt{a}>0$,

$e_0 + 1 = \frac{x_0-\sqrt{a}}{\sqrt{a}} + 1 = \frac{x_0}{\sqrt{a}} > 0\quad\quad\quad(1)$

By (1),

$x_0 = \sqrt{a} (e_0+1)\quad\quad\quad(2)$

Let $x_1$ be the mid-point of $x_0$ and $\frac{a}{x_0}$:

$x_1 = \frac{1}{2} (x_0 + \frac{a}{x_0})\quad\quad\quad(3)$

and, $e_1$ the relative error of $x_1$ approximating $\sqrt{a}$,

$e_1 = \frac{x_1-\sqrt{a}}{\sqrt{a}}\quad\quad\quad(4)$

We have

$x_1-\sqrt{a}\overset{(3)}{=}\frac{1}{2}(x_0+\frac{a}{x_0}) - \sqrt{a} \overset{(2)}{=}\frac{1}{2}(\sqrt{a}(e_0+1)+\frac{a}{\sqrt{a}(e_0+1)})-\sqrt{a}=\frac{\sqrt{a}}{2}\frac{e_0^2}{e_0+1}\overset{(1)}{>}0\quad(5)$

$e_1 = \frac{x_1-\sqrt{a}}{\sqrt{a}} \overset{(5)}{=} \frac{\frac{\sqrt{a}}{2}\frac{e_0^2}{e_0+1}}{\sqrt{a}} = \frac{1}{2}\frac{e_0^2}{e_0+1}\overset{(1)}{ > }0\quad\quad\quad(6)$

By (5),

$x_1 > \sqrt{a} \implies \sqrt{a} x_1 > a \implies \sqrt{a} > \frac{a}{x_1}\implies \frac{\sqrt{a}}{x_1} < \sqrt{a} < x_1$

i.e., $\sqrt{a}$ lies between $\frac{a}{x_1}$ and $x_1$.

We can generate more values in stages by

$x_{k+1} = \frac{1}{2}(x_k + \frac{a}{x_k}), \;k=1, 2, 3, ...\quad\quad(8)$

Clearly,

$\forall k, x_k >0$.

Let

$e_k = \frac{x_k-\sqrt{a}}{\sqrt{a}}$.

We have

$x_k = \sqrt{a}(e_k+1)\quad\quad\quad(9)$

and

$e_{k+1} = \frac{x_{k+1}-\sqrt{a}}{\sqrt{a}}=\frac{\frac{1}{2}(\sqrt{a}(e_k+1) + \frac{a}{\sqrt{a}(e_k+1)})-\sqrt{a}}{\sqrt{a}}=\frac{1}{2}\frac{e_k^2}{e_k + 1}\quad\quad\quad(10)$

Consequently, we can prove that

$\forall k \ge 1, e_{k+1} > 0\quad\quad\quad(11)$

by induction:

When $k = 1, e_{1+1} \overset{(10)}{=} \frac{1}{2}\frac{e_1^2}{e_1+1} \overset{(6)} {>} 0$.

Assume when $k = p$,

$e_{p+1} =\frac{1}{2}\frac{e_p^2}{e_p^2+1} >0\quad\quad\quad(12)$

When $k = p + 1, e_{(p+1)+1} \overset{(10)}{=}\frac{1}{2}\frac{e_{p+1}^2}{e_{p+1} + 1}\overset{(12)}{>}0$.

Since (11) implies

$\forall k \ge 2, e_k > 0\quad\quad\quad(13)$

(10) and (13) together implies

$\forall k \ge 1, e_k > 0\quad\quad\quad(14)$

It follows that

$\forall k \ge 1, 0 \overset{(14)}{<} e_{k+1} \overset{(10)}{=} \frac{1}{2}\frac{e_k^2}{e_k+1} \overset{(14)}{<} \frac{1}{2}\frac{e_k^2}{e_k}=\frac{1}{2}e_k\quad\quad\quad(15)$

and

$\forall k \ge 1, 0 \overset{(14)}{<} e_{k+1} \overset{(10)}{=} \frac{1}{2}\frac{e_k^2}{e_k+1}\overset{(14)}{<}\frac{1}{2}\frac{e_k^2}{1}

(15) indicates that the error is cut at least in half at each stage after the first.

Therefore, we will reach a stage $n$ that

$0 < e_n < 1\quad\quad\quad(17)$

It can be shown that

$\forall k \ge 1, 0< e_{n+k} < e_n^{2^k}\quad\quad\quad(18)$

by induction:

When $k = 1$,

$0 \overset{(14)}{<} e_{n+1} \overset{(10)}{=}\frac{1}{2}\frac{e_n^2}{e_n+1} \overset{}{<} e_n^2=e_n^{2^1}\quad\quad\quad(19)$

Assume when k = p,

$0 < e_{n+p} < e_n^{2^p}\quad\quad\quad(20)$

When $k = p+1$,

$0 \overset{(14)}{<} e_{n+(p+1)} = e_{(n+p)+1} \overset{(16)}{<}e_{n+p}^2\overset{(20)}{<}(e_n^{2^p})^2 = e_n^{2^p\cdot 2} = e_n^{2^{p+1}}$

From (17) and (18), we see that eventually, the error decreases at an exponential rate.

Illustrated below is a script that implements the Heron’s Algorithm:

/*
a   - the positive number whose square root we are seeking
x0  - the initial guess
eps - the desired accuracy
*/
square_root(a, x0, eps) := block(
[xk],
numer:true,
xk:x0,
loop,
if abs(xk^2-a) < eps then return(xk),
xk:(xk+a/xk)/2,
go(loop)
)\$


Suppose we want to find $\sqrt{2}$ and start with $x_0 = 99$. Fig. 2 shows the Heron’s algorithm in action.

Fig. 1

# Déjà vu!

The stages of a two stage rocket have initial masses $m_1$ and $m_2$ respectively and carry a payload of mass $P$. Both stages have equal structure factors and equal relative exhaust speeds. If the rocket mass, $m_1+m_2$, is fixed, show that the condition for maximal final speed is

$m_2^2 + P m_2 = P m_1$.

Find the optimal ratio $\frac{m_1}{m_2}$ when $\frac{P}{m_1+m_2} = b$.

According to multi-stage rocket’s flight equation (see “Viva Rocketry! Part 2“), the final speed of a two stage rocket is

$v = -c \log(1-\frac{e \cdot m_1}{m_1 + m_2 +P})- c\log(1-\frac{e \cdot m_2}{m_2+P})$

Let $m_0 = m_1 + m_2$, we have

$m_1 = m_0-m_2$

and,

$v = -c \log(1-\frac{e \cdot (m_0-m_2)}{m_0 +P})- c\log(1-\frac{e \cdot m_2}{m_2+P})$

Differentiate $v$ with respect to $m_2$ gives

$v' = \frac{c(e-1)e(2m_2 P-m_0 P +m_2^2)}{(P+m_2)(P-e m_2+m_2)(P+e m_2-e m_0+m_0)}= \frac{c(e-1)e(2m_2-m_0 P+m_2^2)}{(P+m_2)(P+(1-e)m_2)(P+e m_2 + (1-e)m_0)}\quad\quad\quad(1)$

It follows that $v'=0$ implies

$2m_2 P-m_0 P + m_2^2 =0$.

That is, $2 m_2 P - (m_1+m_2) P + m_2^2 = (m_2-m_1) P + m_2^2=0$. i.e.,

$m_2^2 + P m_2 = P m_1\quad\quad\quad(2)$

It is the condition for an extreme value of $v$. Specifically, the condition to attain a maximum (see Exercise-2)

When $\frac{P}{m_1+m_2} = b$, solving

$\begin{cases} (m_2-m_1)P+m_2^2=0\\ \frac{P}{m_1+m_2} = b\end{cases}$

yields two pairs:

$\begin{cases} m_1=\frac{(\sqrt{b}\sqrt{b+1}+b+1)P}{b}\\m_2= -\frac{\sqrt{b^2+b}P+bP}{b}\end{cases}$

and

$\begin{cases} m_1= - \frac{(\sqrt{b}\sqrt{b+1}-b-1)P}{b}\\ m_2=\frac{\sqrt{b^2+b}P-bP}{b}\end{cases}\quad\quad\quad(3)$

Only (3) is valid (see Exercise-1)

Hence

$\frac{m_1}{m_2} = -\frac{(\sqrt{b}\sqrt{b+1}-b-1)P}{\sqrt{b^2+b}P-b P} = \frac{\sqrt{b+1}}{\sqrt{b}} = \sqrt{1+\frac{1}{b}}$

The entire process is captured in Fig. 2.

Fig. 2

Exercise-1 Given $b>0, 00$, prove:

1. $- \frac{(\sqrt{b}\sqrt{b+1}-b-1)P}{b}>0$
2. $\frac{\sqrt{b^2+b}P-bP}{b}>0$

Exercise-2 From (1), prove the extreme value attained under (2) is a maximum.

# Boosting rocket flight performance without calculus (Viva Rocketry! Part 2.1)

Fig. 1

Given

$v = c\log(\frac{(a+1)(b+1)((a+1)b+1)}{(1-e+(a+1)b)((1-e)a+(a+1)b+1})$

where $b>0, 00, a>0$, maximize $v$ with appropriate $a$.

The above optimization problem is solved using calculus (see “Viva Rocketry! Part 2“). However, there is an alternative that requires only high school mathematics with the help of a Computer Algebra System (CAS). This non-calculus approach places more emphasis on problem solving through mathematical thinking, as all symbolic calculations are carried out by the CAS (e.g., see Fig. 2). It also makes a range of interesting problems readily tackled with minimum mathematical prerequisites.

The fact that

$\log$ is a monotonic increasing function $\implies v_{max} = c\log(w_{max})$

where

$w = \frac{(a+1)(b+1)((a+1)b+1)}{(1-e+(a+1)b)((1-e)a+(a+1)b+1)}$

or

$w(1-e+(a+1)b)((1-e)a+(a+1)b+1) - (a+1)(b+1)((a+1)b+1)=0\quad\quad\quad(1)$

(1) can be written as

$A_1 a^2 + B_1 a +C_1= 0$

where

$A_1 = -bew+b^2w+bw-b^2-b$

$B_1 = e^2w-2bew-2ew+2b^2w+3bw+w-2b^2-3b-1$,

$C_1 = -bew-ew+b^2w+2bw+w-b^2-2b-1$.

Since $A_1 = 0$ means

$-b e w + b^2 w + b w - b^2 - b =0$.

That is

$w = -\frac{b+1}{e-b-1}$.

Solve

$\frac{(a+1)(b+1)((a+1)b+1)}{(1-e+(a+1)b)((1-e)a+(a+1)b+1)} = -\frac{b+1}{e-b-1}$

for $a$ gives $a = 0 \implies A_1 \neq 0$ if $a > 0$.

Hence, (1) is a quadratic equation. For it to have solution, its discriminant $B_1^2-4A_1C_1$ must be nonnegative, i.e.,

$(e^2-2be-2e+b+1)^2 w^2-2(b+1)(2be^2+e^2-2be-2e+b+1) w +(b+1)^2 \geq 0\quad(2)$

Consider

$(e^2-2be-2e+b+1)^2 w^2-2(b+1)(2be^2+e^2-2be-2e+b+1)w+(b+1)^2 = 0\quad(3)$

If $e^2-2be-2e+b+1 \neq 0$, (3) is a quadratic equation.

Solving (3) yields two solutions

$w_1 = -\frac{(b+1)(2\sqrt{b(b+1)}e^2-2be^2-e^2-2\sqrt{b(b+1)}e+2be+2e-b-1)}{(e^2-2be-2e+b+1)^2}$,

$w_2=\frac{(b+1)(2\sqrt{b(b+1)}e^2+2be^2+e^2-2\sqrt{b(b+1)}e-2be-2e+b+1)}{(e^2-2be-2e+b+1)^2}$.

Since $0 < e < 1$,

$w_1 - w_2 = -\frac{4(b+1)\sqrt{b(b+1)}(e-1)e}{(e^2-2be-2e+b+1)^2} > 0\quad(4)$

(4) implies

$w_1 > w_2$

and, the solution to (2) is

$w \leq w_2$ or $w \ge w_1$

i.e.,

$w \leq \frac{(b+1)(2\sqrt{b(b+1)}e^2+2be^2+e^2-2\sqrt{b(b+1)}e-2be-2e+b+1)}{(e^2-2be-2e+b+1)^2}\quad\quad\quad(4)$

or

$w \ge -\frac{(b+1)(2\sqrt{b(b+1)}e^2-2be^2-e^2-2\sqrt{b(b+1)}e+2be+2e-b-1)}{(e^2-2be-2e+b+1)^2}\quad\quad\quad(5)$

We prove that (4) is true by showing (5) is false:

Consider $w - w_1= 0$:

$\frac{(b+1)(e-1) e \cdot f(a)}{(1-e+ab+b)(a(1-e)+ab+b+1)(e^2-2be-2e+b+1)^2} = 0\quad\quad\quad(6)$

where

$f(a) = 2a\sqrt{b(b+1)}e^2+a^2be^2+be^2+e^2-2a^2b\sqrt{b(b+1)}$

$-4ab\sqrt{b(b+1)}e - 2b\sqrt{b(b+1)}e - 4a\sqrt{b(b+1)}e$

$-2\sqrt{b(b+1)}e-2a^2b^2e-4ab^2e-2a^2be-4abe-4be$

$-2e+2a^2b^2\sqrt{b(b+1)}+4ab^2\sqrt{b(b+1)} + 2b^2\sqrt{b(b+1)} +2a^2b\sqrt{b(b+1)}$

$+6ab\sqrt{b(b+1)} + 4b\sqrt{b(b+1)} + 2a\sqrt{b(b+1)} + 2\sqrt{b(b+1)}$

$+2a^2b^3 + 4ab^3 + 2b^3 +3a^2b^2 + 8ab^2 +5b^2+a^2b+4ab+4b+1$.

It can be written as

$A_2a^2 + B_2a + C_2\quad\quad\quad(7)$

where

$A_2 = be^2-2b\sqrt{b(b+1)}e-2b^2e-2be+2b^2\sqrt{b(b+1)}+2b\sqrt{b(b+1)}$

$+2b^3+3b^2+b$,

$B_2 = 2\sqrt{b(b+1)}e^2-4b\sqrt{b(b+1)}e -4\sqrt{b(b+1)}e$

$-4b^2e-4be+4b^2\sqrt{b(b+1)}+6b\sqrt{b(b+1)}+2\sqrt{b(b+1)}+4b^3+8b^2+4b$,

$C_2=be^2+e^2-2b\sqrt{b(b+1)}e-2\sqrt{b(b+1)}e-2b^2e-4be$

$-2e+2b^2\sqrt{b(b+1)}+4b\sqrt{b(b+1)}+2\sqrt{b(b+1)}+2b^3+5b^2+4b+1$.

Since $A_2 > 0$ (see Exercise 1) and,

solve (7) for $a$ yields

$a = -\sqrt{1+\frac{1}{b}}$.

It follows that for $a > 0, f(a) > 0$.

Consequently, $w-w_1$ is a negative quantity. i.e.,

$w-w1 < 0$

which tells that (5) is false.

Hence, when $e^2-2be-2e+b+1 \neq 0$, the global maximum $w_{max}$ is $w_2$.

Solving $w = w_2$ for $a$:

$\frac{(a+1)(b+1)((a+1)b+1}{(1-e+(a+1)b)((1-e)a+(a+1)b+1)} = \frac{(b+1)(2\sqrt{b(b+1)}e^2+2be^2+e^2-2\sqrt{b(b+1)}e-2be-2e+b+1)}{(e^2-2be-2e+b+1)^2}$,

we have

$a = \sqrt{1+ \frac{1}{b}}$.

Therefore,

$e^2-2be-2e+b+1 \neq 0 \implies w$ attains maximum at $a = \sqrt{1+ \frac{1}{b}}$.

In fact, $w$ attains maxima at $a = \sqrt{1+\frac{1}{b}}$ even when $e^2-2be-2e+b+1 = 0$, as shown below:

Solving $e^2-2be-2e+b+1 = 0$ for $e$, we have

$e_1= -\sqrt{b(b+1)}+b+1$ or $e_2 = \sqrt{b(b+1)} + b + 1$.

Only $e_1$ is valid (see Exercise-2),

When $e = e_1$,

$w(\sqrt{1+\frac{1}{b}} )- w(a) = - \frac{(b+1)g(a)}{4 \sqrt{b(b+1)} (\sqrt{b(b+1)}+ab) (a\sqrt{b(b+1)}+b+1)}\quad(8)$

where

$g(a) = (2a^2b+4ab+2b+2a+2)\sqrt{b(b+1)}-2a^2b^2-4ab^2-2b^2-a^2b-4ab-3b-1$

Solve quadratic equation $g(a) = 0$ for $a$ yields

$a = \sqrt{1+\frac{1}{b}}$.

The coefficient of $a^2$ in $g(a)$ is $2b\sqrt{b(b+1)}-2b^2-b$, a negative quantity (see Exercise-3).

The implication is that $g(a)$ is a negative quantity when $a \neq \sqrt{1 + \frac{1}{b}}$.

Hence, (8) is a positive quantity, i.e.,

$e^2-2be-2e+b+1 = 0, a \neq \sqrt{1+\frac{1}{b}} \implies w(\sqrt{1+\frac{1}{b}})-w(a) > 0$

We therefore conclude

$\forall 0 < e < 1, b > 0, w$ attains its maximum at $a = \sqrt{1+\frac{1}{b}}$.

Fig. 2

Exercise-1 Prove:$00 \implies$

$be^2-2b\sqrt{b(b+1)}e-2b^2e-2be+2b^2\sqrt{b(b+1)}+2b\sqrt{b(b+1)}+2b^3+3b^2+b > 0$

Exercise-2 Prove: $b > 0 \implies 0 <-\sqrt{b(b+1)} + b +1 <1$

Exercise-3 Prove: $b > 0 \implies 2b\sqrt{b(b+1)}-2b^2-b < 0$

# Piece of Pi

A while back, we deemed that it is utterly impractical to calculate the value of $\pi$ using the partial sum of Leibniz’s series due to its slow convergence (see “Pumpkin Pi“)

Fig. 1

As illustrated in Fig. 1, in order to determine each additional correct digit of $\pi$, the number of terms in the summation must increase by a factor of 10.

What we need is a fast converging series whose partial sum yields given number of correct digits with far fewer terms.

Looking back, we see that the origin of Leibniz’s series is the definite integral

$\frac{\pi}{4} = \int\limits_{0}^{1}\frac{1}{1+x^2} dx\quad\quad\quad(1)$

To find the needed new series, we consider a variation of (1), namely,

$\frac{\pi}{6} = \int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{1}{1+x^2} dx\quad\quad\quad(2)$

Given the fact (see “Pumpkin Pi“) that

$\frac{1}{1+x^2} = \sum\limits_{k=1}^{n}(-1)^{k+1}x^{2k-2}+\frac{(-1)^n x^{2n}}{1+x^2}\quad\quad\quad(3)$

We proceed to integrate (3) with respect to $x$ from $0$ to $\frac{1}{\sqrt{3}}$,

$\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{1}{1+x^2} dx=\int\limits_{0}^{\frac{1}{\sqrt{3}}}\sum\limits_{k=1}^{n}(-1)^{k+1}x^{2k-2} dx + \int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{(-1)^n x^{2n}}{1+x^2}dx$

$= \sum\limits_{k=1}^{n}(-1)^{k+1}\int\limits_{0}^{\frac{1}{\sqrt{3}}}x^{2k-2} dx + (-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx$

$= \sum\limits_{k=1}^{n}(-1)^{k+1}\frac{x^{2k-1}}{2k-1}\bigg|_{0}^{\frac{1}{\sqrt{3}}}+ (-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx$

As result, (2) becomes

$\frac{\pi}{6}=\sum\limits_{k=1}^{n}(-1)^{k+1}\frac{(\frac{1}{\sqrt{3}})^{2k-1}}{2k-1}+(-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx$

$= \sum\limits_{k=1}^{n}(-1)^{k+1}\frac{{(\frac{1}{3}})^k}{(2k-1)\frac{1}{\sqrt{3}}} + (-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx$

$= \sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)} + (-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx$

i.e.,

$\frac{\pi}{6} - \sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)} = (-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx\quad\quad\quad(4)$

By (4),

$|\frac{\pi}{6} - \sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)}| = |(-1)^n\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx|=\int\limits_{0}^{\frac{1}{\sqrt{3}}}\frac{x^{2n}}{1+x^2}dx<\int\limits_{0}^{\frac{1}{\sqrt{3}}}x^{2n}dx$

$=\frac{x^{2n+1}}{2n+1}\bigg|_0^{\frac{1}{\sqrt{3}}}$

$=\frac{1}{3^n \sqrt{3} (2n+1)}$

which gives

$|\sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)} - \frac{\pi}{6}| < \frac{1}{3^n \sqrt{3} (2n+1)}$.

And so

$-\frac{1}{3^n \sqrt{3} (2n+1)}<\sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)}-\frac{\pi}{6}<\frac{1}{3^n \sqrt{3} (2n+1)}\quad\quad\quad(5)$

Since $\lim\limits_{n\rightarrow \infty}\frac{1}{3^n \sqrt{3} (2n+1)}=0$, (5) implies

$\lim\limits_{n\rightarrow \infty} \sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)}-\frac{\pi}{6}= 0$.

Hence,

$\lim\limits_{n\rightarrow \infty} \sqrt{3}\sum\limits_{k=1}^{n}\frac{(-1)^{k+1}}{3^k(2k-1)}=\frac{\pi}{6}$.

It follows that the value of $\pi$ can be approximated by the partial sum of a new series

$6\sqrt{3}\sum\limits_{k=1}^{\infty}\frac{(-1)^{k+1}}{3^k(2k-1)}$

Let’s compute it with Omega CAS Explorer (see Fig. 2, 3)

Fig. 2

Fig. 2 shows the series converges quickly. The sum of the first 10 terms yields the first 6 digits!

Fig. 3

Totaling the first 100 terms of the series gives the first 49 digits of $\pi$ (see Fig. 3)

Exercise 1. Show that $\lim\limits_{n\rightarrow \infty}\frac{1}{3^n \sqrt{3} (2n+1)}=0$.

Exercise 2. Can we use $\frac{\pi}{3} = \int\limits_{0}^{\sqrt{3}}\frac{1}{1+x^2}dx$ to compute the value of $\pi$ in a similar fashion? Explain.

# Viva Rocketry! Part 2

Fig. 1

A rocket with $n$ stages is a composition of $n$ single stage rocket (see Fig. 1) Each stage has its own casing, instruments and fuel. The $n$th stage houses the payload.

Fig. 2

The model is illustrated in Fig. 2, the $i^{th}$ stage having initial total mass $m_i$ and containing fuel $\epsilon_i m_i (0 < \epsilon_i <1, 1 \leq i \leq n)$. The exhaust speed of the $i^{th}$ stage is $c_i$.

The flight of multi-stage rocket starts with the $1^{st}$ stage fires its engine and the rocket is lifted. When all the fuel in the $1^{st}$ stage has been burnt, the $1^{st}$ stage’s casing and instruments are detached. The remaining stages of the rocket continue the flight with $2^{nd}$ stage’s engine ignited.

Generally, the rocket starts its $i^{th}$ stage of flight with final velocity achieved at the end of previous stage of flight. The entire rocket is propelled by the fuel in the $i^{th}$ casing of the rocket. When all the fuel for this stage has been burnt, the $i^{th}$ casing is separated  from the rest of the stages. The flight of the rocket is completed if $i=n$. Otherwise, it enters the next stage of flight.

When all external forces are omitted, the governing equation of rocket’s $i^{th}$ stage flight (see “Viva Rocketry! Part 1” or “An alternate derivation of ideal rocket’s flight equation (Viva Rocketry! Part 1.3)“) is

$0 = m_i(t) \frac{dv_i(t)}{dt} + c_i \frac{dm_i(t)}{dt}$

It can be written as

$\frac{dv_i(t)}{dt} = -\frac{c_i}{m_i(t)}\frac{dm_i(t)}{dt}\quad\quad\quad(1)$

Integrate (1) from $t_0$ to $t$,

$\int\limits_{t_0}^{t}\frac{dv_i(t)}{dt}\;dt = -c_i \int\limits_{t_0}^{t}\frac{1}{m_i(t)}\frac{dm_(t)}{dt}\;dt$

gives

$v_i(t) - v_i(t_0) = -c_i(\log(m_i(t))-\log(m_i(t_0)))=-c_i\log(\frac{m_i(t)}{m_i(t_0)})$.

At $t=t^*$ when $i^{th}$ stage’s fuel has been burnt, we have

$v_i(t^*) - v_i(t_0) = -c_i\log(\frac{m_i(t^*)}{m_i(t_0)})\quad\quad\quad(2)$

where

$m_i(t_0) = \sum\limits_{k=i}^{n} m_k +P$

and,

$m_i(t^*) = \sum\limits_{k=i}^n m_k - \epsilon_i m_i + P$.

Let $v^*_i = v_i(t^*), \; v^*_{i-1}$ the velocity of rocket at the end of ${i-1}^{th}$ stage of flight.

Since $v_i(t_0) = v^*_{i-1}$, (2) becomes

$v^*_i - v^*_{i-1} = -c_i \log(\frac{ \sum\limits_{k=i}^n m_k - \epsilon_i m_i+P}{\sum\limits_{k = i}^{n} m_k + P}) = -c_i\log(1-\frac{\epsilon_i m_i}{\sum\limits_{k = i}^{n} m_k + P})$

i.e.,

$v^*_i = v^*_{i-1}-c_i\log(1-\frac{\epsilon_i m_i}{\sum\limits_{k = i}^{n} m_k + P}), \quad\quad 1\leq i \leq n, v_0=0\quad\quad\quad(3)$

For a single stage rocket ($n=1$), (3) is

$v_1^*=-c_1\log(1-\frac{\epsilon_1}{1+\beta})\quad\quad\quad(4)$

In my previous post “Viva Rocketry! Part 1“, it shows that given $c_1=3.0\;km\;s^{-1}, \epsilon_1=0.8$ and $\beta=\frac{1}{100}$, (4) yields $4.7 \;km\;s^{-1}$, a value far below $7.8\;km\;s^{-1}$, the required speed of an earth orbiting  satellite.

But is there a value of $\beta$ that will enable the single stage rocket to produce the speed a satellite needs?

Let’s find out.

Differentiate (4) with respect to $\beta$ gives

$\frac{dv_1^*}{d\beta} = - \frac{c_1 \epsilon_1}{(\beta+1)^2 (1-\frac{\epsilon_1}{\beta+1})} = - \frac{c_1\epsilon_1}{(\beta+1)^2(\frac{1-\epsilon_1+\beta}{\beta+1})} < 0$

since $c_1, \beta$ are positive quantities and $0< \epsilon_1 < 1$.

It means $v_1^*$ is a monotonically decreasing function of $\beta$.

Moreover,

$\lim\limits_{\beta \rightarrow 0}v_1^*=\lim\limits_{\beta \rightarrow 0}-c_1\log(1-\frac{\epsilon_1}{1+\beta}) = - c_1 \log(1-\epsilon_1)\quad\quad\quad(5)$

Given $c_1=3.0\;km\;s^{-1}, \epsilon_1=0.8$, (5) yields approximately

$4.8\;km\;s^{-1}$

Fig. 3

This upper limit implies that for the given $c_1$ and $\epsilon_1$, no value of $\beta$ will produce a speed beyond (see Fig. 4)

Let’s now turn to a two stage rocket ($n=2$)

From (3), we have

$v_2^* = -c_1\log(1-\frac{\epsilon_1 m_1}{m_1+m_2+P}) - c_2\log(1-\frac{\epsilon_2 m_2}{m_2+P})\quad\quad\quad(6)$

If $c_1=c_2=c, \epsilon_1 = \epsilon_2 = \epsilon, m_1=m_2$ and $\frac{P}{m_1+m_2} = \beta$, then

$P = \beta (m_1+m_2) = 2m_1\beta = 2m_2\beta$.

Consequently,

$v_2^*=-c \log(1-\frac{\epsilon}{2(1+\beta)}) - c\log(1-\frac{\epsilon}{1+2\beta})\quad\quad\quad(7)$

When $c=3.0\;km\;s^{-1}, \epsilon=0.8$ and $\beta = \frac{1}{100}$

$v_2^* \approx 6.1\;km\;s^{-1}$

Fig. 5

This is a considerable improvement over the single stage rocket ($v^*=4.7\; km\;s^{-1}$). Nevertheless, it is still short of producing the orbiting speed a satellite needs.

In fact,

$\frac{dv_2^*}{d\beta} = -\frac{2c\epsilon}{(2\beta+2)^2(1-\frac{\epsilon}{2\epsilon+2})}-\frac{2c\epsilon}{(2\beta+1)^2(1-\frac{\epsilon}{2\beta+1})}= -\frac{2c\epsilon}{(2\beta+2)^2\frac{2\beta+2-\epsilon}{2\beta+2}}-\frac{2c\epsilon}{(2\beta+1)^2\frac{2\beta+1-\epsilon}{2\beta+1}} < 0$

indicates that $v_2^*$ is a monotonically decreasing function of $\beta$.

$\lim\limits_{\beta \rightarrow 0} v_2^*=\lim\limits_{\beta \rightarrow 0 }-c\log(1-\frac{\epsilon}{2+2\beta})-c\log(1-\frac{\epsilon}{1+2\beta})=-c\log(1-\frac{\epsilon}{2})-c\log(1-\epsilon)$.

Therefore, there is an upper limit to the speed a two stage rocket can produce. When $c=3.0\;km\;s^{-1}, \epsilon=0.8$, the limit is approximately

$6.4\;km\;s^{-1}$

Fig. 7

In the value used above, we have taken equal stage masses, $m_1 = m_2$. i.e., the ratio of $m_1 : m_2 = 1 : 1$.

Is there a better choice for the ratio of $m_1:m_2$ such that a better $v_2^*$ can be obtained?

To answer this question, let $\frac{m_1}{m_2} = \alpha$, we have

$m_1 = \alpha m_2\quad\quad\quad(8)$

Since $P = \beta (m_1+m_2)$, by (8),

$P = \beta (\alpha m_2 + m_2)\quad\quad\quad(9)$

Substituting (8), (9) into (6),

$v_2^* = -c\log(1-\frac{\epsilon \alpha m_2}{\alpha m_2+m_2+\beta(\alpha m_2+m_2))}) - c\log(1-\frac{\epsilon m_2}{m_2 + \beta(\alpha m_2 + m_2)})$

$= -c\log(1-\frac{\epsilon \alpha}{\alpha+1+\beta(\alpha+1)})-c\log(1-\frac{\epsilon}{1+\beta(\alpha+1)})$

$= c\log\frac{(\alpha+1)(\beta+1)((\alpha+1)\beta+1)}{(1-\epsilon+(\alpha+1)\beta)((1-\epsilon)\alpha + (\alpha+1)\beta+1)}\quad\quad\quad(10)$

a function of $\alpha$. It can be written as

$v_2^*(\alpha) = c\log(w(\alpha))$

where

$w(\alpha) = \frac{(\alpha+1)(\beta+1)((\alpha+1)\beta+1)}{(1-\epsilon+(\alpha+1)\beta)((1-\epsilon)\alpha + (\alpha+1)\beta+1)}$

This is a composite function of $\log$ and $w$.

Since $\log$ is a monotonic increasing function (see “Introducing Lady L“),

$(v_2^*)_{max} = c\log(w_{max})$

Here, $(v_2^*)_{max}, w_{max}$ denote the maximum of $v_2^*$ and $w$ respectively.

To find $w_{max}$, we differentiate $w$,

$\frac{dw}{d\alpha} = \frac{(\beta+1)(\alpha^2\beta-\beta-1)(\epsilon-1)\epsilon}{(\epsilon-\alpha \beta-\beta-1)^2(\alpha\epsilon-\alpha \beta-\beta-\alpha-1)^2}=\frac{(\beta+1)(\alpha^2\beta-\beta-1)(\epsilon-1)\epsilon}{(1-\epsilon+\alpha \beta+\beta)^2(\alpha(1-\epsilon)+\alpha \beta+\beta)^2}\quad\quad\quad(11)$

Solving $\frac{dw}{d\alpha} = 0$ for $\alpha$ gives

$\alpha = - \sqrt{1+\frac{1}{\beta}}$ or $\sqrt{1+\frac{1}{\beta}}$.

Fig. 8

By (8), the valid solution is

$\alpha = \sqrt{1+\frac{1}{\beta}}$.

It shows that $w$ attains an extreme value at $\sqrt{1+\frac{1}{\beta}}$.

Moreover, we observe from (11) that

$\alpha < \sqrt{1+\frac{1}{\beta}} \implies \frac{dw}{d\alpha} > 0$

and

$\alpha > \sqrt{1+\frac{1}{\beta}} \implies \frac{dw}{d\alpha} < 0$.

i.e., $w$ attains maximum at $\alpha=\sqrt{1+\frac{1}{\beta}}$.

It follows that

$v_2^*$ attains maximum at $\alpha = \sqrt{1+\frac{1}{\beta}}$.

Therefore, to maximize the final speed given to the satellite, we must choose the ratio

$\frac{m_1}{m_2} = \sqrt{1+\frac{1}{\beta}}$.

With $\beta =\frac{1}{100}$, the optimum ratio $\frac{m_1}{m_2}=10.05$, showing that the first stage must be about ten times large than the second.

Using this ratio and keep $\epsilon=0.8, c=3.0\;km\;s^{-1}$ as before, (10) now gives

$v_2 = 7.65\;km\;s^{-1}$

a value very close to the required one.

Fig. 9

Setting $\beta = \frac{1}{128}$, we reach the goal:

$v_2^* = 7.8\;km\;s^{-1}$

Fig. 10

Fig. 11

At last, it is shown mathematically that provided the stage mass ratios ($\frac{P}{m_1+m_2}$ and $\frac{m_1}{m_2}$)are suitably chosen, a two stage rocket can indeed launch satellites into earth orbit.

Exercise 1. Show that $\alpha < \sqrt{1+\frac{1}{\beta}} \implies \frac{dw}{d\alpha} > 0$ and $\alpha > \sqrt{1+\frac{1}{\beta}} \implies \frac{dw}{d\alpha} < 0$.

Exercise 2. Using the optimum $\frac{m_1}{m_2} = \sqrt{1+\frac{1}{\beta}}$ and $\epsilon=0.8, c=3.0\;km\;s^{-1}$, solving (10) numerically for $\beta$ such that $v_2^* = 7.8$.