Laguerre polynomials under 1

Laguerre polynomials have many neat definitions; I am partial to {\displaystyle L_n(x) = \left(\frac{d}{dx} - 1\right)^n  \frac{x^n}{n!}} because it’s so easy to remember:

  1. Begin with {x^n}
  2. Repeat “subtract the derivative” {n} times
  3. Normalize so the constant term is 1.

For example, for n=3 this process goes as {x^3} to {x^3-3x^2} to {x^3 -6x^2 + 6x} to {x^3-9x^2+18x -6}, which normalizes to {-\frac16x^3+\frac{3}{2}x^2 -3x +1}. This would make a mean exercise on differentiating polynomials: every mistake snowballs throughout the computation.

What would happen if we added the derivative instead? Nothing really new: this change is equivalent to reversing the direction of the x-axis, so we’d end up with {L_n(-x)}. Incidentally, this shows that the polynomial {L_n(-x)} has positive coefficients, which means the behavior of {L_n} for negative {x} is boring: the values go up as {x} becomes more negative. Laguerre polynomials are all about the interval {[0,\infty)} on which they are orthogonal with respect to the weight {\exp(-x)} and therefore change sign often.

L20.png
20 Laguerre polynomials

But when I look at the plot shown above, it’s not the zeros that draw my attention (perhaps because the x-axis is not shown) but the horizontal line {y=1}, the zero-degree polynomial. The coefficients of {L_n} have alternating signs; in particular, {L_n(0)=1} and {L_n'(0)=-n}. So, nonconstant Laguerre polynomials start off with the value of 1 and immediately dive below it. All except the linear one, {L_1(x)=1-x}, eventually recover and reach 1 again (or so it seems; I don’t have a proof).

L30.png
30 Laguerre polynomials

The yellow curve that is the first to cross the blue line is the 5th degree Laguerre polynomial. Let’s see if any of the other polynomials rises about 1 sooner…

L100.png
100 Laguerre polynomials

Still, nobody beats {L_5} (and the second place is held by {L_4}). By the way, the visible expansion of oscillations is approximately exponential; multiplying the polynomials by {\exp(-x/2)} turns the envelopes into horizontal lines:

lag_functions30.png
30 Laguerre polynomials times exp(-x/2)

Back to the crossing of y=1 line. The quantity to study is the smallest positive root of {L_n - 1}, denoted  {r(n)} from now on. (It is the second smallest root overall; as discussed above, this polynomial has a root at x=0 and no negative roots.) For n=2,3,4,5,6, the value of {r(n)} is  {4, 3, 6-2\sqrt{3}, (15-\sqrt{105})/2, 6} which evaluates to 4, 3, 2.536…, 2.377…, and 6 respectively. I got these with Python / SymPy:

from sympy import *
x = Symbol('x')
[Poly(laguerre(n, x) - 1).all_roots()[1] for n in range(2, 7)]

For higher degrees we need numerics. SymPy can still help (applying .evalf() to the roots), but the process gets slow. Switching to NumPy’s roots method speeds things up, but when it indicated than {r(88)}  and a few others are in double digits, I became suspicious…  a closer check showed this was a numerical artifact.

Conjecture: {r(5) \le r(n) \le r(6)} for all {n}. Moreover, {3 < r(n) < 6} when {n \ge 7}.

Here is a closer look at the exceptional polynomials of degrees 3, 4, 5 and 6, with 1 subtracted from each:

l3456

The first local maximum of {L_n} shifts down and to the left as the degree n increases. The degree n=5 is the last for which {L_n} exceeds 1 on the first attempt, so it becomes the quickest to do so. On the other hand, n=6 fails on its first attempt to clear the bar, and its second attempt is later than for any subsequent Laguerre polynomial; so it sets the record for maximal {r(n)}.

Evaluating high-degree Laguerre polynomials is a numerical challenge: adding large terms of alternating signs can reduce accuracy dramatically. Here is a plot of the degree 98 polynomial (minus 1): where is its first positive root?

L98.png
L(98, x) – 1

Fortunately, SymPy can evaluate Laguerre polynomials at rational points using exact arithmetics since the coefficients are rational. For example, when it evaluates the expression laguerre(98, 5) > 1 to True, that’s a (computer-assisted) proof that {r(98) < 5}, which one could in principle "confirm" by computing the same rational value of {L_{98}(5) } by hand (of course, in this situation a human is far less trustworthy than a computer) . Evaluation at the 13 rational points 3, 3.25, 3.5, … , 5.75, 6 is enough to certify that {r(n) < 6} for {n} up to 200 (with the aforementioned exception of {r(6) = 6}).

The lower bounds call for Sturm’s theorem which is more computationally expensive than sampling at a few rational points. SymPy offers a root-counting routine based on this theorem (it counts the roots within the given closed interval):

for n in range(2, 101):
    num_roots = count_roots((laguerre(n,x)-1)/x, 0, 3)
    print('{} roots for n = {}'.format(num_roots, n))

Division by x eliminates the root at 0, so we are left with the root count on (0,3] — which is 1 for n=3,4 and 2 for n=5. The count is zero for all other degrees up to 100, confirming that {r(n) > 3} for {n \ge 6}.

So, the conjecture looks solid. I don’t have a clue to its proof (nor do I know if it’s something known). The only upper bound on {L_n} that I know is Szegő’s {|L_n(x)|\le \exp(x/2)} for {x\ge 0}, which is not helping here.

Complex Cantor sets

Every real number in the interval [0,1] can be written in binary as {\sum_{k=1}^\infty c_k(1/2)^k} where each coefficient {c_k} is either 0 or 1. Another way to put this: the set of all possible sums {\sum_{k=1}^\infty c_kb^k} for b = 1/2 is a line segment.

line12

What is this set for other values of “base” b, then? Let’s stick to |b| < 1 for now, so that the series converges. Nothing interesting happens for real b between 1/2 and 1; the segment grows longer, to length b/(1-b). When b is between 0 and 1, we get Cantor sets, with the classical middle-third set being the case b = 1/3.

cantor13

There is no need to consider negative b, because of a symmetry between b and -b. Indeed, up to scaling and translation, the coefficients can be taken from {-1, 1} instead of {0, 1}. Then it’s obvious that changing the sign of b is the same as flipping half of coefficients the other way — does not change the set of possible sums.

Let’s look at purely imaginary b, then. Here is b = 0.6i

j06

Why so rectangular? The real part is the sum of {c_kb^k} over even k, and the imaginary part is the sum over odd k. Each of these yields a Cantor type set as long as {|b|^2 < 1/2}. Since the odd- and even-numbered coefficients are independent of each other, we get the product of two Cantor sets. Which changes into a rectangle when  {|b| \ge \sqrt{1/2}}:

jsqrt12

(I didn’t think a full-size picture of a solid rectangle was necessary here.)

This is already interesting: the phase transition from dust to solid (connected, and even with interior) happens at different values in the real and imaginary directions: 1/2 versus {\sqrt{1/2}}. What will happen for other complex values? Using complex conjugation and the symmetry between b and -b, we reduce the problem to the quarter-disk in the first quadrant. Which still leaves a room for a lot of things to happen…

06j03.PNG
b = 0.6 + 0.3i
07j02.png
b = 0.7 + 0.2i
04j03.PNG
b = 0.4 + 0.3i
02j07.png
b = 0.2 + 0.7i

It’s clear that for |b| < 1/2 we get a totally disconnected set — it is covered by 2 copies of itself scaled by the factor of |b|, so its Hausdorff dimension is less than 1 when |b| is less than 1/2. Also, the argument of b is responsible for rotation of the scaled copies, and it looks like rotation favors disconnectivity… but then again, the pieces may link up again after being scaled-rotated a few times, so the story is not a simple one.

The set of bases b for which the complex Cantor set is connected is a Manderbrot-like set introduced by Barnsley and Harrington in 1985. It has the symmetries of a rectangle, and features a prominent hole centered at 0 (discussed above). But it actually has infinitely many holes, with “exotic” holes being tiny islands of disconnectedness, surrounded by connected sets. This was proved in 2014 by Calegari, Koch, Walker, so I refer to Danny Calegari’s post for an explanation and more pictures (much better looking than mine).

Besides “disconnected to connected”, there is another phase transition: empty interior to nonempty interior. Hare and Sidorov proved that the complex Cantor set has nonempty interior when  {|b| > 2^{-1/4}}; their path to the proof involved a MathOverflow question The Minkowski sum of two curves which is of its own interest.

The pictures were made with a straightforward Python script, using expansions of length 20:

import matplotlib.pyplot as plt
import numpy as np
import itertools
n = 20
b = 0.6 + 0.3j
c = np.array(list(itertools.product([0, 1], repeat=n)))
w = np.array([b**k for k in range(n)]).reshape(1, -1)
z = np.sum(c*w, axis=1)
plt.plot(np.real(z), np.imag(z), '.', ms=4)
plt.axis('equal')
plt.show()

Since we are looking at partial sums anyway, it’s not necessary to limit ourselves to |b| being less than 1. Replacing b by 1/b only scales the picture, so the place to look for new kinds of pictures is the unit circle. Let’s try a 7th root of unity:

7throot.PNG
b = exp(pi i / 7)

The set above looks sparse because many points overlap. Let’s change b to something non-algebraic:

etoi.PNG
b = exp(i)

What’s with the cusps along the perimeter?

35 categories of Stack Overflow comments

Google’s BigQuery dataset now includes Stack Overflow data dump, including the text of over 50 million comments posted on the site. What do these comments say? I picked the most frequent ones and grouped them by topic. The counts are an underestimate: there is only so much time I was willing to spend organizing synonymous comments.

  1. Thank you” comments (128960 in total) are the most common by far. Typical forms: Thank you very/so much!, Thanks a lot :), Perfect, thanks! The popularity of the emoticon in the second version is attributable to the minimal length requirement for comments: they must contain at least 15 characters. The laziest way to pad the text is probably Thank you……
  2. You are welcome” (50090), presumably posted in response to group 1 comments. You’re welcome. You’re welcome! You’re welcome 🙂 Users need that punctuation or emoticon to reach 15 characters. Although those not contracting “you are” don’t have this problem.
  3. Updated answer” (30979) invites whoever raised objections about the previous version of the answer to read it again.
  4.  “What is your question?” (20830) is the most common type of critical comments toward questions.
  5. This is not an answer” (17306) is the most common criticism for answers; usually posted automatically by reviewers. Typical form: This does not provide an answer to the question. To critique or request clarification from an author, leave a comment below their post. Another one is for questions posted in the answer box: If you have a new question, please ask it by clicking the Ask Question button. Include a link to this question if it helps provide context.
  6. What error are you getting?” (13439) is a request for debugging information.
  7. What have you tried?” (12640) often comes with the link whathaveyoutried.com and is a sufficiently notorious kind of comments that Stack Overflow software deletes them if anyone “flags” the comment. And it’s easy to cast flags automatically, so I substantially reduced the number of such comments since this data dump was uploaded. Further context: Should Stack Overflow (and Stack Exchange in general) be awarding “A”s for Effort?
  8. Post your code” (11486) can sometimes be a form of “what have you tried”; in other times it’s a logical response to someone posting an error message without the code producing it. Can you post your code? Post your code. Please post your code. Show your code. Where is your code? And so on.
  9. It does not work” (9674) — either the question author, or someone else with the same issue did not benefit from the solution. Maybe it’s wrong, maybe they used it wrong.
  10. This is a link-only answer” (9501) usually comes from reviewers in the standard form While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference. Link-only answers can become invalid if the linked page changes.
  11. I updated the question” (8060), presumably in response to critical comments.
  12. Why the downvote(s)?” (6005) is asking whoever voted down the post to explain their position. Usually fruitless; if the voter wanted to say something, they would already.
  13. This is a duplicate question” (3859) is inserted automatically when someone moves for a question to be marked as a duplicate. Such comments are normally deleted automatically when the required number of close-votes is reached; but some remain. The most common by far is possible duplicate of What is a Null Pointer Exception, and how do I fix it? 
  14. I edited your title” (3795) is directed at users who title their questions like “Java: How to read a CSV file?”, using a part of the title as a tag. Standard form: I have edited your title. Please see, “Should questions include “tags” in their titles?“, where the consensus is “no, they should not”.
  15. Post a MCVE” (3775) – the line on which the error is thrown is probably not enough to diagnose the problem; on the other hand, a wall of code with an entire program is too much. One of standard forms: Questions seeking debugging help (“why isn’t this code working?”) must include the desired behavior, a specific problem or error and the shortest code necessary to reproduce it in the question itself. Questions without a clear problem statement are not useful to other readers. See: How to create a Minimal, Complete, and Verifiable example.
  16. That is correct” (3158)  usually refers to a statement made in another comment.
  17. It works” (3109) is the counterpart of group 9 above. Often used with “like a charm” but do charms actually work?
  18. What do you mean?” (2998) – for when an exchange in comments leads to more confusion.
  19. What tool are you using?” (2649) indicates that the question author forgot to specify either the language, OS, or the DBMS they are using.
  20. Good answer” (2607) – various forms of praise, This should be the accepted answer. This is the correct answer. Excellent answer! The first form additionally indicates that the question author did not pick the best answer as “accepted”.
  21. This question is off-topic” (2377) is a template for close votes with a custom explanation. For some years Stack Overflow used This question appears to be off-topic because… but then switched to the more assertive I’m voting to close this question as off-topic because…
  22. This is a low quality answer” (2003) is a response to answers that contain nothing but code, perhaps preceded by “try this”. Example: While this code snippet may solve the question, including an explanation really helps to improve the quality of your post. Remember that you are answering the question for readers in the future, and those people might not know the reasons for your code suggestion.
  23. Is this homework?” (1995) is not a particularly fruitful type of comments.
  24. Does this work?” (1916) is meant to obtain some response from question asker who has not yet acknowledged the answer.
  25. The link is dead” (1250) is a major reason why group 10 comments exist.
  26. http://stackoverflow.com/help/ . . .” (1117) and nothing but the link. Directs to one of Help Center articles such as “How to ask”. Maybe there should also be “How to Comment”
  27. Thanks are discouraged” (1046) … so all those group 1 comments aren’t meant to be. But this is mostly about posts rather than comments. Unlike forum sites, we don’t use “Thanks”, or “Any help appreciated”, or signatures on Stack Overflow. See “Should ‘Hi’, ‘thanks,’ taglines, and salutations be removed from posts?.
  28. Format your code” (967) – yes, please. Select the code block and press Ctrl-K. Thanks in advance. Oops, forgot about the previous group.
  29. What doesn’t work?” (926) is a response to vague comments of group 9.
  30. Don’t use mysql_* functions” (693) or Russian hackers will pwn your site. Comes with a link-rich explanation: Please, don’t use `mysql_*` functions in new code. They are no longer maintained and are officially deprecated. See the red box? Learn about prepared statements instead, and use PDO or MySQLithis article will help you decide which. If you choose PDO, here is a good tutorial.
  31. Add tags” (625) often comes up in the context of database questions. Which RDBMS is this for? Please add a tag to specify whether you’re using `mysql`, `postgresql`, `sql-server`, `oracle` or `db2` – or something else entirely.
  32. Improve title” (585) is like group 14, but invites the user to edit the title instead of doing it for them.
  33. Use modern JOIN syntax” (301) bemoans obsolete ways of dealing with databases. Bad habits to kick : using old-style JOINs – that old-style *comma-separated list of tables* style was replaced with the *proper* ANSI `JOIN` syntax in the ANSI-92 SQL Standard (more than 20 years ago) and its use is discouraged.
  34. More SQL woes” (272) is another template: Side note: you should not use the `sp_` prefix for your stored procedures. Microsoft has reserved that prefix for its own use (see *Naming Stored Procedures*, and you do run the risk of a name clash sometime in the future. It’s also bad for your stored procedure performance. It’s best to just simply avoid `sp_` and use something else as a prefix – or no prefix at all!
  35. I have the same problem” (241) is a kind of comments that really should not exist.

Vectorization of integration

A frequently asked question about numerical integration is: how to speed up the process of computing {\int_a^b f(x,p)\,dx} for many values of parameter {p}? Running an explicit for over the values of {p} seems slow… is there a way to vectorize this, performing integration on an array of functions at once (which in reality means, pushing the loop to a lower level language)?

Usually, the answer is some combination of “no” and “you are worrying about the wrong thing”. Integration routines are typically adaptive, meaning they pick evaluation points based on the function being integrated. Different values of the parameter will require different evaluation points to achieve the desired precision, so vectorization is out of question. This applies, for example, to quad method of SciPy integration module.

Let’s suppose we insist on vectorization and are willing to accept a non-adaptive method. What are our options, considering SciPy/NumPy? I will compare

The test case is {\int_0^1 (p+1)x^p\,dx} with {p=0,0.01,\dots,99.99}. Theoretically all these are equal to {1}. But most of these functions are not analytic at {0}, and some aren’t even differentiable there, so their integration is not a piece of cake.

Keeping in mind that Romberg’s integration requires {2^k} subintervals, let’s use {1024} equal subintervals, hence {1025} equally spaced sample points. Here is the vectorized evaluation of all our functions at these points, resulting in a 2D array “values”:

import numpy as np
import scipy.integrate as si
x = np.linspace(0, 1, 1025).reshape(1, -1)
dx = x[0,1] - x[0,0]
p = np.arange(0, 100, 0.01).reshape(-1, 1)
values = (p+1)*x**p

This computation took 0.799 seconds on my AWS instance (t2.nano). Putting the results of evaluation together takes less time:

  • Trapezoidal rule np.trapz(values, dx=dx) took 0.144 seconds and returned values ranging from 0.99955 to 1.00080.
  • Simpson’s rule si.simps(values, dx=dx) took 0.113 seconds and returned values ranging from 0.99970 to 1.0000005.
  • Romberg quadrature si.romb(values, dx=dx) was fastest at 0.0414 seconds, and also most accurate, with output between 0.99973 and 1.000000005.

Conclusions so far:

  • SciPy’s implementation of Romberg quadrature is surprisingly fast, considering that this algorithm is the result of repeated Richardson extrapolation of the trapezoidal rule (and Simpson’s rule is just the result of the first extrapolation step). It’d be nice to backport whatever makes Romberg so effective back to Simpson’s rule.
  • The underestimation errors 0.999… are greatest when {p} is near zero, so the integrand is very nonsmooth at {0}. The lack of smoothness renders Richardson extrapolation ineffective, hence all three rules have about the same error here.
  • The overestimation errors are greatest when {p} is large. The function is pretty smooth then, so upgrading from trapezoidal to Simpson to Romberg brings substantial improvements.

All of this is nice, but the fact remains that non-vectorized adaptive integration is both faster and much more accurate. The following loop with quad, which uses adaptive Clenshaw-Curtis quadrature, runs in 0.616 seconds (less than it took to evaluate our functions on a grid) and returns values between 0.99999999995 and 1.00000000007. Better to use exponential notation here: {1-5\cdot 10^{-11} } and {1+7\cdot 10^{-11}}.

funcs = [lambda x, p=p_: (p+1)*x**p for p_ in np.arange(0, 100, 0.01)]
result = [si.quad(fun, 0, 1)[0] for fun in funcs]

An adaptive algorithm shines when {p} is small, by placing more evaluation points near zero. It controls both over- and under- estimates equally well, continuing to add points until the required precision is reached.

The last hope of non-adaptive methods is Gaussian quadrature, which is implemented in SciPy as fixed_quad (“fixed” referring to the number of evaluation points used). With 300 evaluation points, the integration takes 0.263 seconds (excluding the computation of nodes and weights, which can be cached) and the results are between {1-2\cdot 10^{-12}} and {1+2\cdot 10^{-7}}. This is twice as fast as a loop with quad, and more accurate for large {p} — but sadly, not so accurate for small {p}. As said before, {x^p} with {p} near zero is really a showcase for adaptive methods.

Kolakoski turtle curve

Let’s take another look at the Kolakoski sequence (part 1, part 2) which, by definition, is the sequence of 1s and 2s in which the nth term is equal to the length of the nth run of consecutive equal numbers in the same sequence. When a sequence has only two distinct entries, it can be visualized with the help of a turtle that turns left (when the entry is 1) or right (when the entry is 2). This visualization method seems particularly appropriate for the  Kolakoski sequence since there are no runs of 3 equal entries, meaning the turtle will never move around a square of sidelength equal to its step. In particular, this leaves open the possibility of getting a simple curve… Here are the first 300 terms; the turtle makes its first move down and then goes left-right-right-left-left-right-left-… according to the terms 1,2,2,1,1,2,1,…

300 terms of the sequence
300 terms of the sequence

No self-intersections yet… alas, at the 366th term it finally happens.

First self-intersection: 366 terms
First self-intersection: 366 terms

Self-intersections keep occurring after that:

1000 terms
1000 terms

again and again…

5000 terms
5000 terms

Okay, the curve obviously doesn’t mind intersecting self. But it can’t be periodic since the Kolakoski sequence isn’t. This leaves two questions unresolved:

  • Does the turtle ever get above its initial position? Probably… I haven’t tried more than 5000 terms
  • Is the curve bounded? Unlikely, but I’ve no idea how one would dis/prove that. For example, there cannot be a long diagonal run (left-right-left-right-left) because having 1,2,1,2,1 in the sequence implies that elsewhere, there are three consecutive 1s, and that doesn’t happen.

Here’s the Python code used for the above. I represented the sequence as a Boolean array with 1 = False, 2 = True.

import numpy as np
import turtle
n = 500                   # number of terms to compute
a = np.zeros(n, dtype=np.bool_)
j = 0                     # the index to look back at 
same = False   # will next term be same as current one?
for i in range(1, n):
    if same:
        a[i] = a[i-1]     # current run continues
        same = False
    else:
        a[i] = not a[i-1] # the run is over
        j += 1            # another run begins
        same = a[j]       # a[j] determines its length

turtle.hideturtle()
turtle.right(90)
for i in range(n):
    turtle.forward(10)    # used steps of 10 or 5 pixels 
    if a[i]:
        turtle.right(90)
    else:
        turtle.left(90)

A necklace of tears

The problem is: given a set of objects drawn from several groups, put all of them in a row in a “uniform” way. Whatever that means.

For example, suppose we have 21 gemstones: 9 red, 5 blue, 3 green, 2 cyan, 1 magenta and 1 yellow. How to place them on a string to make a reasonably looking necklace? The criterion is subjective, but we can probably agree that

Arrangement #1
Arrangement #1

looks better than

Arrangement #2
Arrangement #2

(referring to the uniformity of distributions, not the aesthetics of color.)

The approach I took was to repeatedly add the “most underrepresented” gem, defined by maximal difference between projected frequency of appearance (e.g., 5/21 for blue) and the frequency of appearance so far in the string. (Taking the convention 0/0=0 here.) Here is this algorithm in Python — not in Ruby, which would be more topical.

count = {'r': 9, 'b': 5, 'g': 3, 'c': 2, 'm': 1, 'y': 1}
length = sum(count.values())
str = ''
while len(str) < length:
    deficit = {}
    for char in count:
        deficit[char] = count[char]/length - (str.count(char)/len(str) if str else 0)
    str += max(deficit, key=deficit.get)
print(str) 

The output, “rbgcryrbrmgrbrcbrgrbr”, is what the first image in this post represents. The second image is the result of an ill-fated attempt to replace difference by ratio when determining the under-representation.

I initially thought that two unique gems (yellow and magenta) would end up together, but this hasn’t happened: after one is added, the frequency of more common gems drops, allowing them to come back into play for a while. Still, the left half of the string is noticeably more diverse than the right half. It’d be better if two unique gems were in roughly symmetrical position, and generally there would be no preference between left-right and right-left directions.

Perhaps the new character should be added to the string either on the right or on the left, in alternating fashion. That should make things nice and symmetric, right?

Wrong.

Alternating concatenation
Alternating concatenation

The search continues…

Update: Rahul suggested in a comment to adjust the deficit computation to

deficit[char] = count[char]/length - str.count(char)/(len(str) + 1)

This has some advantages but on the other hand, two unique gems (magenta and yellow) are placed next to each other, which is something I wanted to avoid.

capture
Dividing by len(str) + 1

Autogenerated numbers

An integer is automorphic if its square ends with that integer, like 762 = 5776. This notion is clearly base-dependent. Ignoring trivial 0 and 1, in base 10 there are two such numbers for any number of digits; they comprise two infinite sequences that can be thought of informally as “infinite” solutions of x2 = x, and formally as solutions of this equation in the ring of 10-adic numbers. They are …56259918212890625 and …740081787109376, both recorded in OEIS, as Wolfram MathWorld points out.

There is a difference between these two sequences. The former naturally grows from the single digit 5, by repeatedly squaring and keeping one more digit than we had: 52 = 25,  252 = 625, 6252= 390625, 06252 = 390625, 906252 = 8212890625, … (One should not get greedy and keep all the digits after squaring: for example, the leading 3 in 390625 is not the digit we want.)  The process described above does not work for 6, because 62 = 36 rather than 76. For the lack of a better term, I’ll call the infinite numbers such as …56259918212890625 autogenerated.

According to Wikipedia, the number of b-adic solutions of x2 = x is 2d where d is the number of distinct prime factors of b. (Another way to put it: there are as many solutions as square-free divisors of the base.) Two of the solutions are trivial: 0 and 1. So, infinite automorphic numbers exist in every base that is not a prime power, and only in those.

Autogenerated numbers are rarer. For example, there are none in base 12. Indeed, the two viable seed digits are 4 and 9: in base 12, 42 is 14 and 92 is 69. But 14 is not automorphic: 142 = 194. With 69 we get a little further: 692 = 3969. But then 969 is not automorphic, and the process stops.

Computer search suggests that autogenerated numbers exist if and only if the base is 2 mod 4 (and is not 2). Furthermore, there is exactly one autogenerated number for each such base, and its seed is half the base. Some examples, with 20 digits shown in each base:

  • … 21314 15515 22213 50213 in base 6
  • … 92256 25991 82128 90625 in base 10
  • … 8676a 8cba5 7337a a0c37 in base 14
  • … aea80 1g4c9 68da4 e1249 in base 18
  • … 179aa 1f0e7 igdi8 d185b in base 22
  • … b9ofn odpbn 31mm3 h1g6d in base 26
  • … f46rg 1jirj r6f3f e1q7f in base 30
  • … g2khh vlas5 k7h4h i248h in base 34

I don’t have a proof of the “2 mod 4” claim, but it may well have a proof somewhere already… According to Dave Richeson, the autogenerating property of 5 in base 10 was discussed in a blog post by Foxmaths!, but the blog is private now. It is also stated in their OEIS article as “a(n+1) = a(n)^2 mod 10^(n+1). – Eric M. Schmidt, Jul 28 2012”.