Secret equation

I got a call this afternoon from someone who records audio books for the blind. He wanted to know the name of a symbol he didn’t recognize. He then asked me if the equation was real.

Here’s the equation in context, from the book Michael Vey 4: Hunt for Jade Dragon. The context is as follows.

Suddenly math problems she hadn’t understood made sense. Except now they weren’t just numbers and equations, they were patterns and colors. Calculus, geometry, and trigonometry were easy to understand, simple as a game, like shooting balls at a basketball hoop that was a hundred feet wide. Then a specific sequence of numbers, letters, and symbols started running through her mind.

s(t; t_y) = k \frac{Q}{r^2} \hat{r} \int_{R^2} m(x, y) e^{-2\pi i \daleth\left(\frac{G_x xt + \daleth G_y yt_y}{2\pi}\right)} \,dx\,dy

She almost said the equation when a powerful thought came over her not to speak it out loud—that she must not ever divulge it. She new that what she was receiving was something of great importance, even if she had no idea what it meant.

I believe the symbol in question is the fourth letter of the Hebrew alphabet, ℸ (daleth).

Is this a real equation? The overall form of it looks like an integral transform. However, the two instances of ℸ in equation look suspicious.

One reason is that I’ve never seen ℸ used in math, though I read somewhere that Cantor used it for the cardinality of some set. Even so, Cantor’s use wouldn’t make sense inside an integral.

Also, the two instances of ℸ are used differently. The first is a function (or else the factors of 2 π could be cancelled out) and the second one apparently is not. Finally, the equation is symmetric in x and y if you remove the two daleths. So I suspect this was a real equation with the daleths added in for extra mystery.

Juggling projects

Yesterday on Twitter I said I was thinking about writing the names of each of my clients and leads on balls so I could literally juggle them. I was only half joking.

I didn’t write my clients and leads on balls, but I did write them on index cards. And it helped a great deal. It’s easier to think about projects when you have physical representations you can easily move around. Moving lines up and down in an org-mode file, or even moving boxes around in 2D in OneNote, doesn’t work as well.

Electronic files are great for storing, editing, and querying ideas. But they’re not the best medium for generating ideas. See Create offline, analyze online. See also Austin Kleon’s idea of having two separate desks, one digital and one analog.

Casting out sevens

A while back I wrote about a method to test whether a number is divisible by seven. I recently ran across another method for testing divisibility by 7 in Martin Gardner’s book The Unexpected Hanging and Other Mathematical Diversions. The method doesn’t save too much effort compared to simply dividing by 7, but it’s interesting. It looks a little mysterious at first, though the explanation of why it works is very simple.

Suppose you want to find whether a number n is divisible by 7. Start with the last digit of n and write a 1 under the last digit, and a 3 under the next to last digit. The digits under the digits of n cycle through 1, 3, 2, 6, 4, 5, repeatedly until there is something under each digit in n. Now multiply each digit of n by the digit under it and add the results.

For example, suppose n = 1394. Write the digits 1, 3, 2, and 6 underneath, from right to left:


The sum we need to compute is 1*6 + 3*2 + 9*3 + 4*1 = 6 + 6 + 27 + 4 = 43.

This sum, 43 in our example, has the same remainder when divided by 7 as the original number, 1394 in our example. Since 43 is not divisible by 7, neither is 1394. Not only that, the result of our method has the same remainder by 7 as the number we started with. In our example, 43 leaves a remainder of 1 by 7, so 1394 also leaves a remainder of 1.

You could apply this method repeatedly, though in this case 43 is small enough that it’s easy enough to see that it leaves a remainder of 1.

Suppose you started with a 1000-digit number n. Each digit is no more than 9, and is being multiplied by a number no more than 6. So the sum would be less than 54000. So you’ve gone from a 1000-digit number to at most a 5-digit number in one step. One or two more steps should be enough for the remainder to be obvious.

Why does this method work? The key is that the multipliers 1, 3, 2, 6, 4, 5 are the remainders when the powers of 10 are divided by 7. Since 106 has a remainder of 1 when divided by 7, the numbers 106a+b and 10b have the same remainder by 7, and that’s why the multipliers have period 6.

All the trick is doing is expanding the base 10 representation of a number and adding up the remainders when each term is divided by seven. In our example, 1394 = 1000 + 3*100 + 9*10 + 4, and mod 7 this reduces to 1*6 + 3*2 + 9*3 + 4*1, the exact calculation above.

The trick presented here is analogous to casting out nines. But since every power of 10 leaves a remainder of 1 when divided by 9, all the multipliers in casting out nines are 1.

You could follow the pattern of this method to create a divisibility rule for any other divisor, say 13 for example, by letting the multipliers be the remainders when powers of 10 are divided by 13.

Related post: Divisibility rules in hex

Computing square triangular numbers

The previous post stated a formula for f(n), the nth square triangular number (i.e. the nth triangular number that is also a square number):

((17 + 12√2)n + (17 – 12√2)n – 2)/32

Now 17 – 12√2 is 0.029… and so the term (17 – 12√2)n approaches zero very quickly as n increases. So the f(n) is very nearly

((17 + 12√2)n – 2)/32

The error in the approximation is less than 0.001 for all n, so the approximation is exact when rounded to the nearest integer. So the nth square triangular number is

⌊((17 + 12√2)n +14)/32⌋

where ⌊x⌋ is the greatest integer less than x.

Here’s how you might implement this in Python:

    from math import sqrt, floor

    def f(n):
        x = 17 + 12*sqrt(2)
        return floor((x**n + 14)/32.0)

Unfortunately this formula isn’t that useful in ordinary floating point computation. When n = 11 or larger, the result is needs more bits than are available in the significand of a floating point number. The result is accurate to around 15 digits, but the result is longer than 15 digits.

The result can also be computed with a recurrence relation:

f(n) = 34 f(n-1) – f(n-2) + 2

for n > 2. (Or n > 1 if you define f(0) to be 0, a reasonable thing to do).

This only requires integer arithmetic, so it’s only limitation is the size of the integers you can compute with. Since Python has arbitrarily large integers, the following works for very large integers.

    def f(n):
        if n < 2:
            return n
        return f(n-1)*34 - f(n-2) + 2

This is a direct implementation, though it’s inefficient because of the redundant function evaluations. It could be made more efficient by, for example, using memoization.

When is a triangle a square?

Of course a triangle cannot be a square, but a triangular number can be a square number.

A triangular number is the sum of the first so many positive integers. For example, 10 is a triangular number because it equals 1+2+3+4. These numbers are called triangle numbers because you can form a triangle by having a row of one coin, two coin, three coins, etc. forming a triangle.

The smallest number that is both triangular and square is 1. The next smallest is 36. There are infinitely many numbers that are both triangular and square, and there’s even a formula for the nth number that is both a triangle and a square:

((17 + 12√2)n + (17 – 12√2)n – 2)/32

Source: American Mathematical Monthly, February 1962, page 169.

For more on triangle numbers and their generalizations, see Twelve Days of Christmas and Tetrahedral Numbers.

There is also a way to compute the square triangular numbers recursively discussed in the next post.

Basics equations of beam deflection

In the preface to his book Strength of Materials, J. P. Den Hartog says

After the alphabet and the tables of multiplication, nothing has proved quite so useful in my professional life as these six little expressions.

The six expressions he refers to are nicknamed the vergeet-me-nietjes in Dutch, which translates to forget-me-nots in English. They are also known as Dr. Myosotis’s equations because myosotis is the genus for forget-me-nots. The equations give the angular and linear deflections of a cantilever beam.

Imagine a beam anchored at one end and free on the other, subject to one of the kinds of load: a bending moment M at the opposite end, a point force P a the opposite end, or a force w distributed over the length of the beam. The equations below give the rotation (angular deflection) and displacement (linear deflection) of the free end of the beam.

Rotation Displacement
Bending moment  ML/EI  ML2/2EI
Point load  PL2/2EI  PL3/3EI
Distributed load  wL3/6EI  wL4/8EI

Here E is the modulus of elasticity, L is the length of the beam, and I is the area moment of inertia.

Deserted island books

You’ve probably heard someone ask someone else what books they would take to a deserted island. It’s usually implied that you’re bringing books for pleasure, not books that would help you survive on the island or leave it.

People often answer the question with a list of their favorite books, perhaps skewed in favor of long books. But I don’t think you should take books that have been your favorites in the past. You should take what you think would be your favorite books on a deserted island. I expect my tastes there would be very different than they are in my current suburban life.

I think of books that I could only read on a desert island, books that I’ve enjoyed in small portions but ordinarily would not have the patience to read cover-to-cover. For example, I’ve found portions of Donald Knuth’s series The Art of Computer Programming enjoyable and useful, but I can’t say I’ve read it all. Perhaps on a deserted island with little to do and few distractions I’d enjoy going through it carefully line by line, attempting all the exercises. I might even learn MMIX, something I can’t imagine doing under ordinary circumstances.

Along those lines, I might want to take some works by Thomas Aquinas such as his Summa Theologica or his commentaries on Aristotle. The little I’ve read of Aquinas has been profound, and more approachable than I expected. Still, I find it hard to read much of him. Alone on an island I might take the time to read him carefully.

For math, I might want to take Spivak’s differential geometry series, depending on how long I expect to be on this island. If I’m going to be there too long and I’m limited on books, I might want to take something else that’s more dense and less familiar.

For science, I’d take Gravitation by Misner, Thorne, and Wheeler. I’ve intended to read that book for many years and have started a couple times. In college I couldn’t afford this price; now I can’t afford the time.

For fiction, I’d take Patrick O’Brian’s Aubrey/Maturin series because I haven’t read it, I’ve heard it is very well written, and it’s long.



Attributing changes to numerator and denominator

This afternoon I helped someone debug a financial spreadsheet. One of the reasons spreadsheets can be so frustrating to work with is that assumptions are hard to see. You have to click on cells one at a time to find formulas, then decode cell coordinates into their meanings.

The root problem turned out to be an assumption that sounds reasonable. You’re making two changes, one to a numerator and one to a denominator. The total change equals the sum of the results of each change separately. Except that’s not so.

At this point, a mathematician would say “Of course you can’t split the effects like that. It’s nonlinear.” But it’s worth pursuing a little further. For one thing, it doesn’t help a general audience to just say “it’s nonlinear.” For another, it’s worth seeing when it is appropriate, at least approximately, to attribute the effects this way.

You start with a numerator and denominator, N/D, then change N to N+n and change D to D+d. The total change is then (N+n)/(D+d) – N/D.

The result from only the change in the numerator is n/D. The result from only the change in denominator is N/(D+d) – N/D.

The difference between the total change and the sum of the two partial changes is


The assumption that you can take the total change and attribute it to each change separately is wrong in general. But it is correct if n or d is zero, and it is approximately correct with nd is small. This can make the bug harder to find. It could also be useful when nd is indeed small and you don’t need to be exact.

Also, if all the terms are positive, the discrepancy is negative, i.e. the total change is less than the sum of the partial changes. Said another way, allocating the change to each cause separately over-estimates the total change.


Last digit of largest known prime

In my previous post, we looked at the largest known prime,  P = 257885161 – 1, and how many digits it has in various bases. This post looks at how to find the last digit of P in base b. We assume b < P. (If b = P then the last digit is 0, and if b > P the last digit is P.)

If b is a power of 2, we showed in the previous post that the last digit of P is b-1.

If b is odd, we can find the last digit using Euler’s totient theorem. Let φ(b) be the number of positive integers less than b and relatively prime to b. Then Euler’s theorem tells us that 2φ(b) ≡ 1 mod b since b is odd. So if r is the remainder when 57885161 is divided by φ(b), then the last digit of Q = P+1 in base b is the same as the last digit of 2r in base b.

For example, suppose we wanted to know the last digit of P in base 15. Since φ(15) = 8, and the remainder when 57885161 is divided by 8 is 1, the last digit of Q base 15 is 2. So the last digit of P is 1.

So we know how to compute the last digit in base b if b is a power or 2 or odd. Factor out all the powers of 2 from b so that b = 2k d and d is odd. We can find the last digit base 2k, and we can find the last digit base d, so can we combine these to find the last digit base b? Yes, that’s exactly what the Chinese Remainder Theorem is for.

To illustrate, suppose we want to find the last digit of P in base 12. P ≡ 3 mod 4 and P ≡ 1 mod 3, so P ≡ 7 mod 12. (The numbers are small enough here to guess. For a systematic approach, see the post mentioned above.) So the last digit of P is 7 in base 12.

If you’d like to write a program to play around with this, you need to be able to compute φ(n). You may have an implementation of this function in your favorite environment. For example, it’s sympy.ntheory.totient in Python. If not, it’s easy to compute φ(n) if you can factor n. You just need to know two things about φ. First, it’s a multiplicative function, meaning that if a and b are relatively prime, then φ(ab) = φ(a) φ(b). Second, if p is a prime, then φ(pk) = pkpk-1.

Playing with the largest known prime

The largest known prime at the moment is P = 257885161 – 1. This means that in binary, the number is a string of 57,885,161 ones.

You can convert binary numbers to other bases that are powers of two, say 2k, by starting at the right end and converting to the new base in blocks of size k. So in base 4, we’d start at the right end and convert pairs of bits to base 4. Until we get to the left end, all the pairs are 11, and so they all become 3’s in base four. So in base 4, P would be written as a 1 followed by 28,942,580 threes.

Similarly, we could write P in base 8 by starting at the right end and converting triples of bits to base 8. Since 57885161 = 3*19295053 + 2, we’ll have two bits left over when we get to the left end, so in base 8, P would be written as 3 followed by 19,295,053 sevens. And since 57885161 = 4*14471290 + 1, the hexadecimal (base 16) representation of P is a 1 followed by 14,471,290 F’s.

What about base 10, i.e. how many digits does P have? The number of digits in a positive integer n is ⌊log10n⌋ + 1 where ⌊x⌋ is the greatest integer less than x. For positive numbers, this just means chop off the decimals and keep the integer part.

We’ll make things slightly easier for a moment and find how many digits P+1 has.

log10(P+1) = log10(257885161) = 57885161 log10(2)  = 17425169.76…

and so P+1 has 17425170 digits, and so does P. How do we know P and P+1 have the same number of digits? There are several ways you could argue this. One is that P+1 is not a power of 10, so the number of digits couldn’t change between P and P+1.

Update: How many digits does P have in other bases?

We can take the formula above and simply substitute b for 10: The base b representation of a positive integer n has ⌊logbn⌋ + 1 digits.

As above, we’d rather work with Q = P+1 than P. The numbers P and Q will have the same number of digits unless Q is a power of the base b. But since Q is a power of 2, this could only happen if b is also a power of two, say b = 2k, and k is a divisor of 57885161. But 57885161 is prime, so this could only happen if b = Q. If b > P, then P has one digit base b. So we can concentrate on the case b < Q, in which case P and Q have the same number of digits. The number of digits in Q is then 57885161 logb(2) + 1.

If b = 2k, we can generalize the discussion above about bases 4, 8, and 16. Let q and r be the quotient and remainder when 57885161 is divided by k. The first digit is r and the remaining q digits are b-1.

Next post: Last digit of largest known prime


Last digits of Fibonacci numbers

If you write out a sequence of Fibonacci numbers, you can see that the last digits repeat every 60 numbers.

The 61st Fibonacci number is 2504730781961. The 62nd is 4052739537881. Since these end in 1 and 1, the 63rd Fibonacci number must end in 2, etc. and so the pattern starts over.

It’s not obvious that the cycle should have length 60, but it is fairly easy to see that there must be a cycle. There are only 10*10 possibilities for two consecutive digits. Since the Fibonacci numbers are determined by a two-term recurrence, and since the last digit of a sum is determined by the sum of the last digits, the sequence of last digits must repeat eventually. Here “eventually” means after at most 10*10 terms.

Replace “10” by any other base in the paragraph above to show that the sequence of last digits must be cyclic in any base. In base 16, for example, the period is 24. In hexadecimal notation the 25th Fibonacci number is 12511 and the 26th is 1DA31, so the 27th must end in 2, etc.

Here’s a little Python code to find the period of the last digits of Fibonacci numbers working in any base b.

from sympy import fibonacci as f

def period(b):
    for i in range(1, b*b+1):
        if f(i)%b == 0 and f(i+1)%b == 1:

This shows that in base 100 the period is 300. So in base 10 the last two digits repeat every 300 terms.

The period seems to vary erratically with base as shown in the graph below.

The Monosyllabic Raven

David Morice rewrote Edgar Allen Poe’s poem The Raven in words of one syllable. Here is the first stanza:

Once at twelve on one night’s drear, ’twas while I, weak and tired thought here
On the words in lots of quaint and odd old tomes of mind’s lost lore,
While I dozed, so near a nap, there came but then a soft, quick tap,
As of one who made a rap, a rap at my front room’s closed door.
“‘Tis some guest,” I spoke, voice low, “who taps at my front room’s closed door,
Well, Just this, and not much more.”

Morice’s entire poem is available here.

Related post: A rewriting of The Raven that gives a mnemonic for the digits of pi.

The success of OOP

Allen Wirfs-Brock gave the following defense of OOP a few days ago in a series of six posts on Twitter:

A young developer approached me after a conf talk and said, “You must feel really bad about the failure of object-oriented programming.” I was confused. I said, “What do you mean that object-orient programming was a failure. Why do you think that?”

He said, “OOP was supposed to fix all of our software engineering problems and it clearly hasn’t. Building software today is just as hard as it was before OOP. came along.”

“Have you ever look at the programs we were building in the early 1980s? At how limited their functionality and UIs were? OOP has been an incredible success. It enabled us to manage complexity as we grew from 100KB applications to today’s 100MB applications.”

Of course OOP hasn’t solved all software engineering problems. Neither has anything else. But OOP has been enormously successful in allowing ordinary programmers to write much larger applications. It has become so pervasive that few programmers consciously think about it; it’s simply how you write software.

I’ve written several posts poking fun at the excesses of OOP and expressing moderate enthusiasm for functional programming, but I appreciate OOP. I believe functional programming will influence object oriented programming, but not replace it.


Life lessons from differential equations

Ten life lessons from differential equations:

  1. Some problems simply have no solution.
  2. Some problems have no simple solution.
  3. Some problems have many solutions.
  4. Determining that a solution exists may be half the work of finding it.
  5. Solutions that work well locally may blow up when extended too far.

Related posts: