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.


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.


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


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}}:


(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…

b = 0.6 + 0.3i
b = 0.7 + 0.2i
b = 0.4 + 0.3i
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)

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:

b = exp(pi i / 7)

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

b = exp(i)

What’s with the cusps along the perimeter?

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
        a[i] = not a[i-1] # the run is over
        j += 1            # another run begins
        same = a[j]       # a[j] determines its length

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

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”.

Graphs with an invertible incidence matrix

Graphs are often encoded by their adjacency matrix: a symmetric matrix where {1} in the entry {(i,j)} means there is an edge between vertices labeled {i} and {j}. Another useful encoding is the incidence matrix, in which rows correspond to edges and columns to vertices (or the other way around). Having {1} in the entry {(i,j)} means that {i}th edge is incident to {j}th vertex. Simply put, each row contains exactly two nonzero entries, which indicate the endpoints of an edge.

An incidence matrix is generally rectangular. It is the square matrix when the graph has as many edges as vertices. For example, the 4-cycle has incidence matrix

{M = \displaystyle \begin{pmatrix} 1 & 1 & 0 & 0 \\ 0 & 1 & 1 & 0 \\ 0 & 0 & 1 & 1 \\ 1 & 0 & 0 & 1 \end{pmatrix}}

(up to relabeling of vertices). This matrix is not invertible. Indeed, {M(1,-1,1,-1)^T=0}, which is a reflection of the fact that the graph is bipartite. Indeed, for any bipartite graph the {\pm 1} vector describing a 2-coloring of the vertices belongs to the kernel of the incidence matrix.

The incidence matrix of an odd cycle is invertible, however. What other such “invertible” graphs exist? Clearly, the disjoint union of “invertible” graphs is itself invertible, so we can focus on connected graphs.

There are no invertible graphs on 1 or 2 vertices. For 3 through 8 vertices, exhaustive search yields the following counts:

  • 1 graph on 3 vertices (3-cycle)
  • 1 graph on 4 vertices (3-cycle with an edge attached)
  • 4 graphs on 5 vertices
  • 8 graphs on 6 vertices
  • 23 graphs on 7 vertices
  • 55 graphs on 8 vertices

The numbers match the OEIS sequence A181688 but it’s not clear whether this is for real or a coincidence. The brute force search takes long for more than 8 vertices. It’s probably better to count such graphs by using their (folklore?) characterization as “trees planted on an odd cycle”, described by Chris Godsil on MathOverflow.

Some examples:




The examples were found and plotted by this Python script.

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from itertools import combinations

def is_invertible(graph, n):
    matrix = []
    for i in range(n):
        row = [0]*n
        row[graph[i][0]] = 1
        row[graph[i][1]] = 1
    return abs(np.linalg.det(matrix)) > 0.5

n = 6   # number of vertices
graphs = combinations(combinations(range(n), 2), n)
inv = []
for g in graphs:
    if is_invertible(g, n):
        gr = nx.Graph(g)
        if nx.is_connected(gr) and not any([nx.is_isomorphic(gr, gr2) for gr2 in inv]):
            plt.savefig("graph{}_{}.png".format(n, len(inv)))

print("There are {} connected invertible graphs with {} vertices".format(len(inv), n))

Random graphs with two-sided degree bounds

I wanted some random examples of graphs where every vertex has degree between two given parameters, minDegree and maxDegree. Like this one:

15 vertices, degree from 3 to 4
15 vertices, degree from 3 to 4

The approach I took was very simple (and not suitable for construction of very large or very regular graphs). Each edge appears with probability p. If the minimal degree is too small, this probability is multiplied by 1.1. If the maximal degree is too big, the probability is divided by 1.1. Either way, the process repeats until success.

20 vertices, degree from 1 to 5

So far, this blog had too much Matlab/Scilab and not enough Python. I’ll try to restore the balance. Here, numpy generates random matrices and takes care of degree restrictions; networkx lays out the graph.

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

vertices = 15
minDegree = 3
maxDegree = 4
p = 0.5
success = False

while not success:
    a = (np.random.random((vertices, vertices)) < p).astype(int)
    a = np.maximum(a, np.matrix.transpose(a))
    np.fill_diagonal(a, 0)
    s = a.sum(axis=1)
    success = True
    if min(s) < minDegree:
        success = False
        p = p * 1.1
    if max(s) > maxDegree:
        success = False
        p = p / 1.1
g = nx.Graph(a)

One more time:

25 vertices, degree from 2 to 4