Mathematics
We are well aware of Euler’s famous number,
$$
e = 2.71828182845
$$
There are also quite a few ways of deriving Euler’s Number. There’s the Taylor expansion method:
$$
e = \sum _{k=0} ^{\infty} \frac{1}{k!}
$$
There is also the classical limit form:
$$
e = \lim_{n \rightarrow \infty} \left( 1 + \frac{1}{n} \right)^n
$$
Then there is another. Let $R$ be a random number generated between $[0, 1]$, inclusive. Then $e$ is the average of the number of $R$ s it takes to sum greater than $1$.
With more rigor, for uniform $(0,, 1)$ random independent variables $R_1$, $R_2$, $\ldots$, $R_n$,
$$
N = \min \left\{n: \sum_{k=1}^n R_k > 1 \right\}
$$
where
$$e = \text{Average}(n)$$
The proof can be found here, but it’s pretty math-heavy. Instead, an easier method is to write a program to verify for large enough $n$.
n | Sum Solution | Limit Solution | Random Uniform Variable |
---|---|---|---|
1 | 1 | 2 | 2 |
10 | 1.7182818011 | 2.5937424601 | 2.5 |
100 | 1.7182818284 | 2.7048138294 | 2.69 |
1000 | 1.7182818284 | 2.7169239322 | 2.717 |
10000 | 2.7181459268 | 2.7242 | |
100000 | 2.7182682371 | 2.71643 | |
1000000 | 2.7182804690 | 2.71961 | |
10000000 | 2.7182816941 | 2.7182017 | |
100000000 | 2.7182817983 | 2.71818689 | |
1000000000 | 2.7182820520 | 2.718250315 |
Source Code
def e_sum(upper_bound):
if upper_bound < 1:
return 0
return Decimal(1.0) /
Decimal(math.factorial(upper_bound)) + \
Decimal(e_sum(upper_bound - 1))
def e_limit(n):
return Decimal((1 + 1.0 / float(n))**n)
def find_greater_than_one(value=0, attempts=0):
if value <= 1:
return find_greater_than_one(value + random.uniform(0, 1), attempts + 1)
return attempts
As I go throughout my undergraduate studies, I have gotten really good at digging deeper until I completely understand a subject; however, when I observed around me, I realized that I am in the minority. Often times, students only desired to know how to get from question to answer without any intermediate thinking. When trying to hypothesize an answer, I cannot think of a definitive one. However, I know one possible contribution: encapsulation[1].
The most famous example I came across is the derivative of any trigonometric function besides $\sin x$ or $\cos x$. As any student who’s taken Calculus can tell you, $$\frac{d}{dx} \left(\tan x\right) = \sec^2 x $$
However, most students couldn't tell you:
$$\begin{align*}
\frac{d}{dx} \left(\tan x\right) &= \frac{d}{dx} \left(\frac{\sin x}{\cos x}\right) \\
&= \frac{\cos x \cos x + \sin x \sin x}{\cos^2 x} \\
&= \frac{\cos^2 x + \sin^2 x}{\cos^2 x} \\
&= \frac{1}{\cos^2 x} \\
&= \sec^2 x
\end{align*}$$
At first glance, it's hardly useful information. As a matter of fact you might think of it as piece of trivia — until you see the amount of people that miss it on a quiz or test. And entry-level Calculus is not the only place this is relevant; physics, thermodynamics, mathematics of any kind, computer science, and the like.
A question you might be thinking, why does it matter? If it's worth a couple of points on a test, is it that harmful? Maybe not, until you realize you conceptually do not understand the subject — only mechanically.
The next time you're learning something, ask yourself: "why?". The outcome might delight you.
- The act of enclosing in a capsule; the growth of a membrane around (any part) so as to enclose it in a capsule. ↩︎
Painting Gabriel’s Horn
A horn you can fill with paint, but you can't paint the surface.
There are many things in the world of mathematics that are really quite wonderful — however, I am not sure there will be anything more wonderful yet unintuitive than Gabriel's Horn.
Gabriel's Horn is thus: suppose you have the function $y = \frac{1}{x}$ where $x \in \mathbb{R}^+, 1 \leq x \leq \infty$, rotated around the $x$ axis; not too difficult to conceptualize, it looks like a horn of sorts. But here's the paradox.
Suppose we want to calculate the volume. Simple enough, using solids of revolution, we can show the volume to be:
$$
V = \pi \lim_{t \rightarrow \infty} \int _1 ^t \frac{1}{x^2} dx = \pi \lim _{t \rightarrow \infty} ( 1 - \frac{1}{t} ) = \pi
$$
A simple, elegant solution; we can expect the volume to be exactly $\pi$. So, let's see about the surface area.
We know the general definition of the arc length to be $\int _a ^b \sqrt{1 + f'(x)^2}$, so combining this with our solids of revolution, we should get
$$
A = 2\pi \lim _{t \rightarrow \infty} \int _1 ^t \frac{1}{x} \sqrt{1 + \left( -\frac{1}{x^2} \right)^2 } dx
$$
However, this is not a trivial integral; however, there is a trick we can do. Suppose we take the integral $$2\pi \lim _{t \rightarrow \infty} \int _1 ^t \frac{dx}{x}$$ instead, and we can prove this integral will always be equal to or smaller than the former integral (because of the disappearance of $\sqrt{1 + (-\frac{1}{x^2})}$). So, taking this rather trivial integral, we can see that
$$
A \geq 2\pi \lim _{t \rightarrow \infty} \int _1 ^t \frac{dx}{x} \implies A \geq \lim _{t \rightarrow \infty} 2\pi \ln(t)
$$
Wait a minute; it's divergent! So we know the volume $V = \pi$, but the surface area $A \geq \infty$. This is no mistake, the math is valid. And that is simply wonderful.
A horn you can fill with paint, but you can't paint the surface.
You've seen it in the movies or read about it in the books: the Monty Hall Problem. The premise is such: you are on a game show. There's three doors, behind one is a prize. Behind the other two is nothing besides disappointment. You pick an arbitrary door. The game host, Monty Hall, opens one of the other doors and reveals that there is no prize behind it. Keeping the other two doors closed, he asks you if you would like to switch your choice or remain with your original pick. What should you do? Benc Campbell has a solution.
You're meaning to tell me that if I switch mid-game, an offer by the man who set up all the doors and the respective prizes, I would have a better chance of winning? How is this possible? Seems a bit counterintuitive. Seems almost wrong. Fortunately, we can test this with a simple program.
import Darwin // for arc4random_uniform
func random(from from: Int, to: Int, except: Array<Int>? = nil) -> Int {
let number = Int(arc4random_uniform(UInt32(to - from + 1))) + from // produce bounds, +1 to make inclusive
if except != nil && except?.indexOf(number) != nil {
return random(from: from, to: to, except: except) // recursively call until you find the a number that doesn't contain the value
}
return number
}
// returns if you won when you stayed and switched, respectively
func montyHall(doors: Int) -> (Bool, Bool) {
let originalDoor = random(from: 1, to: doors) // Your original choice
let correctDoor = random(from: 1, to: doors) // The winning choice.
let revealedDoor = random(from: 1, to: doors, except: [correctDoor, originalDoor]) // The door the judge reveals
let switchedDoor = random(from: 1, to: doors, except: [revealedDoor, originalDoor]) // Supposing you switched, you woudl
return (originalDoor == correctDoor, switchedDoor == correctDoor)
}
let testValues = [100, 1_000, 10_000, 100_000, 1_000_000, 1_000_000]
let actualNumberOfDoors = 3
for value in testValues {
var stayingCounter = 0, switchingCounter = 0
for _ in 0...value {
let (stayedWon, switchedWon) = montyHall(actualNumberOfDoors)
if stayedWon { stayingCounter += 1 }
if switchedWon { switchingCounter += 1 }
}
print("Test Case: \(value). Staying Won: \(stayingCounter), Switching Won: \(switchingCounter)")
}
There's no tricks in the code. No gimmicks. So, the results?
Input | Staying Won | Switching Won |
---|---|---|
100 | 42 | 58 |
1000 | 344 | 656 |
10000 | 3289 | 6711 |
100000 | 33203 | 66797 |
1000000 | 333178 | 666822 |
10000000 | 3335146 | 6664854 |
It appears that Ben[1] was right. Taking the limit as the input get higher, switching won roughly $66\%$ of the time — compared that to the original $33\%$ you got before you were given the option to switch doors. So, how is this possible? I'll explain.
For simplicity, suppose the prize is behind door A out of doors A, B, C. The argument can be made for any initial door, but A makes it easier. At this point, you have $\frac{1}{3}$ chance of picking the prize no matter what you pick. If you pick door A, the door which contains the prize, the host will definitely want you to change so he will offer you either door B or C. Now suppose you choose a door without the prize, either door B or C. The host has no choice but to eliminate the door without the prize. Meaning if you switch, you have the winning door.
So, why $66\%$? Computationally, if you always switch and you picked the wrong door initially, your switch will win every time. There's two incorrect choices out of three, meaning $\frac{2}{3}$ of the time you will win.
You've just had your first lesson in conditional probability.
- 21 main character, played by Jim Sturgess ↩︎