## Computer experiments for the Lyapunov exponent for MCF algorithms when dimension is larger than 3

27 mars 2020 | Catégories: sage, slabbe spkg, math | View Comments

In November 2015, I wanted to share intuitions I developped on the behavior of various distinct Multidimensional Continued Fractions algorithms obtained from various kind of experiments performed with them often involving combinatorics and digitial geometry but also including the computation of their first two Lyapunov exponents.

As continued fractions are deeply related to the combinatorics of Sturmian sequences which can be seen as the digitalization of a straight line in the grid \(\mathbb{Z}^2\), the multidimensional continued fractions algorithm are related to the digitalization of a straight line and hyperplanes in \(\mathbb{Z}^d\).

This is why I shared those experiments in what I called 3-dimensional Continued Fraction Algorithms Cheat Sheets because of its format inspired from typical cheat sheets found on the web. All of the experiments can be reproduced using the optional SageMath package slabbe where I share my research code. People asked me whether I was going to try to publish those Cheat Sheets, but I was afraid the format would change the organization of the information and data in each page, so, in the end, I never submitted those Cheat Sheets anywhere.

Here I should say that \(d\) stands for the dimension of the vector space on which the involved matrices act and \(d-1\) is the dimension of the projective space on which the algorithm acts.

One of the consequence of the Cheat Sheets is that it made us realize that the algorithm proposed by Julien Cassaigne had the same first two Lyapunov exponents as the Selmer algorithm (first 3 significant digits were the same). Julien then discovered the explanation as its algorithm is conjugated to some semi-sorted version of the Selmer algorihm. This result was shared during WORDS 2017 conference. Julien Leroy, Julien Cassaigne and I are still working on the extended version of the paper. It is taking longer mainly because of my fault because I have been working hard on aperiodic Wang tilings in the previous 2 years.

During July 2019, Wolfgang, Valérie and Jörg asked me to perform computations of the first two Lyapunov exponents for \(d\)-dimensional Multidimensional Continued Fraction algorithms for \(d\) larger than 3. The main question of interest is whether the second Lyapunov exponent keeps being negative as the dimension increases. This property is related to the notion of strong convergence almost everywhere of the simultaneous diopantine approximations provided by the algorithm of a fixed vector of real numbers. It did not take me too long to update my package since I had started to generalize my code to larger dimensions during Fall 2017. It turns out that, as the dimension increases, all known MCF algorithms have their second Lyapunov exponent become positive. My computations were thus confirming what they eventually published in their preprint in November 2019.

My motivation for sharing the results is the conference Multidimensional Continued Fractions and Euclidean Dynamics held this week (supposed to be held in Lorentz Center, March 23-27 2020, it got cancelled because of the corona virus) where some discussions during video meetings are related to this subject.

The computations performed below can be summarized in one graphics showing the values of \(1-\theta_2/\theta_1\) with respect to \(d\) for various \(d\)-dimensional MCF algorithms. It seems that \(\theta_2\) is negative up to dimension 10 for Brun, up to dimension 4 for Selmer and up to dimension 5 for ARP.

I have to say that I was disapointed by the results because the algorithm Arnoux-Rauzy-Poincaré (ARP) that Valérie and I introduced was not performing so well as its second Lyapunov exponent seems to become positive for dimension \(d\geq 6\). I had good expectations for ARP because it reaches the highest value for \(1-\theta_2/\theta_1\) in the computations performed in the Cheat Sheets, thus better than Brun, better than Selmer when \(d=3\).

The algorithm for the computation of the first two Lyapunov exponents was
provided to me by Vincent Delecroix. It applies the algorithm
\((v,w)\mapsto(M^{-1}v,M^T w)\) millions of times. The evolution of the
size of the vector \(v\) gives the first Lyapunov exponent. The evolution
of the size of the vector \(w\) gives the second Lyapunov exponent. Since
the computation is performed on 64-bits `double` floating point number, their
are numerical issues to deal with. This is why some Gramm Shimdts operation is
performed on the vector \(w\) at each time the vectors are renormalized to
keep the vector \(w\) orthogonal to \(v\). Otherwise, the numerical errors
cumulate and the computed value for the \(\theta_2\) becomes the same as
\(\theta_1\). You can look at the algorithm online starting at line 1723
of the file `mult_cont_frac_pyx.pyx` from my optional package.

I do not know from where Vincent took that algorithm. So, I do not know how exact it is and whether there exits any proof of lower bounds and upper bounds on the computations being performed. What I can say is that it is quite reliable in the sense that is returns the same values over and over again (by that I mean 3 common most significant digits) with any fixed inputs (number of iterations).

Below, I show the code illustrating how to reproduce the results.

The version 0.6 (November 2019) of my package `slabbe` includes the
necessary code to deal with some \(d\)-dimensional Multidimensional Continued
Fraction (MCF) algorithms. Its documentation is available online. It is a PIP
package, so it can be installed like this:

sage -pip install slabbe

Recall that the dimension \(d\) below is the linear one and \(d-1\) is the dimension of the space for the corresponding projective algorithm.

Import the Brun, Selmer and Arnoux-Rauzy-Poincaré MCF algorithms from the optional package:

sage: from slabbe.mult_cont_frac import Brun, Selmer, ARP

The computation of the first two Lyapunov exponents performed on one single orbit:

sage: Brun(dim=3).lyapunov_exponents(n_iterations=10^7) (0.30473782969922547, -0.11220958022368056, 1.3682167728713919)

The starting point is taken randomly, but the results of the form of a 3-tuple \((\theta_1,\theta_2,1-\theta_2/\theta_1)\) are about the same:

sage: Brun(dim=3).lyapunov_exponents(n_iterations=10^7) (0.30345018206132324, -0.11171509867725296, 1.3681497170915415)

Increasing the dimension \(d\) yields:

sage: Brun(dim=4).lyapunov_exponents(n_iterations=10^7) (0.32639514522732005, -0.07191456560115839, 1.2203297648654456) sage: Brun(dim=5).lyapunov_exponents(n_iterations=10^7) (0.30918877340506756, -0.0463930802132972, 1.1500477514185734)

It performs an orbit of length \(10^7\) in about .5 seconds, of length \(10^8\) in about 5 seconds and of length \(10^9\) in about 50 seconds:

sage: %time Brun(dim=3).lyapunov_exponents(n_iterations=10^7) CPU times: user 540 ms, sys: 0 ns, total: 540 ms Wall time: 539 ms (0.30488799356325225, -0.11234354880132114, 1.3684748208296182) sage: %time Brun(dim=3).lyapunov_exponents(n_iterations=10^8) CPU times: user 5.09 s, sys: 0 ns, total: 5.09 s Wall time: 5.08 s (0.30455473631148755, -0.11217550411862384, 1.3683262505689446) sage: %time Brun(dim=3).lyapunov_exponents(n_iterations=10^9) CPU times: user 51.2 s, sys: 0 ns, total: 51.2 s Wall time: 51.2 s (0.30438755982577026, -0.11211562816821799, 1.368331834035505)

Here, in what follows, I must admit that I needed to do a small fix to my package, so the code below will not work in version 0.6 of my package, I will update my package in the next days in order that the computations below can be reproduced:

sage: from slabbe.lyapunov import lyapunov_comparison_table

For each \(3\leq d\leq 20\), I compute 30 orbits and I show the most significant digits and the standard deviation of the 30 values computed.

For Brun algorithm:

sage: algos = [Brun(d) for d in range(3,21)] sage: %time lyapunov_comparison_table(algos, n_orbits=30, n_iterations=10^7, ncpus=8) CPU times: user 190 ms, sys: 2.8 s, total: 2.99 s Wall time: 6min 31s Algorithm \#Orbits $\theta_1$ (std) $\theta_2$ (std) $1-\theta_2/\theta_1$ (std) +-------------+----------+--------------------+---------------------+-----------------------------+ Brun (d=3) 30 0.3045 (0.00040) -0.1122 (0.00017) 1.3683 (0.00022) Brun (d=4) 30 0.32632 (0.000055) -0.07188 (0.000051) 1.2203 (0.00014) Brun (d=5) 30 0.30919 (0.000032) -0.04647 (0.000041) 1.1503 (0.00013) Brun (d=6) 30 0.28626 (0.000027) -0.03043 (0.000035) 1.1063 (0.00012) Brun (d=7) 30 0.26441 (0.000024) -0.01966 (0.000027) 1.0743 (0.00010) Brun (d=8) 30 0.24504 (0.000027) -0.01207 (0.000024) 1.04926 (0.000096) Brun (d=9) 30 0.22824 (0.000021) -0.00649 (0.000026) 1.0284 (0.00012) Brun (d=10) 30 0.2138 (0.00098) -0.0022 (0.00015) 1.0104 (0.00074) Brun (d=11) 30 0.20085 (0.000015) 0.00106 (0.000022) 0.9947 (0.00011) Brun (d=12) 30 0.18962 (0.000017) 0.00368 (0.000021) 0.9806 (0.00011) Brun (d=13) 30 0.17967 (0.000011) 0.00580 (0.000020) 0.9677 (0.00011) Brun (d=14) 30 0.17077 (0.000011) 0.00755 (0.000021) 0.9558 (0.00012) Brun (d=15) 30 0.16278 (0.000012) 0.00900 (0.000017) 0.9447 (0.00010) Brun (d=16) 30 0.15556 (0.000011) 0.01022 (0.000013) 0.93433 (0.000086) Brun (d=17) 30 0.149002 (9.5e-6) 0.01124 (0.000015) 0.9246 (0.00010) Brun (d=18) 30 0.14303 (0.000010) 0.01211 (0.000019) 0.9153 (0.00014) Brun (d=19) 30 0.13755 (0.000012) 0.01285 (0.000018) 0.9065 (0.00013) Brun (d=20) 30 0.13251 (0.000011) 0.01349 (0.000019) 0.8982 (0.00014)

For Selmer algorithm:

sage: algos = [Selmer(d) for d in range(3,21)] sage: %time lyapunov_comparison_table(algos, n_orbits=30, n_iterations=10^7, ncpus=8) CPU times: user 203 ms, sys: 2.78 s, total: 2.98 s Wall time: 6min 27s Algorithm \#Orbits $\theta_1$ (std) $\theta_2$ (std) $1-\theta_2/\theta_1$ (std) +---------------+----------+--------------------+---------------------+-----------------------------+ Selmer (d=3) 30 0.1827 (0.00041) -0.0707 (0.00017) 1.3871 (0.00029) Selmer (d=4) 30 0.15808 (0.000058) -0.02282 (0.000036) 1.1444 (0.00023) Selmer (d=5) 30 0.13199 (0.000033) 0.00176 (0.000034) 0.9866 (0.00026) Selmer (d=6) 30 0.11205 (0.000017) 0.01595 (0.000036) 0.8577 (0.00031) Selmer (d=7) 30 0.09697 (0.000012) 0.02481 (0.000030) 0.7442 (0.00032) Selmer (d=8) 30 0.085340 (8.5e-6) 0.03041 (0.000032) 0.6437 (0.00036) Selmer (d=9) 30 0.076136 (5.9e-6) 0.03379 (0.000032) 0.5561 (0.00041) Selmer (d=10) 30 0.068690 (5.5e-6) 0.03565 (0.000023) 0.4810 (0.00032) Selmer (d=11) 30 0.062557 (4.4e-6) 0.03646 (0.000021) 0.4172 (0.00031) Selmer (d=12) 30 0.057417 (3.6e-6) 0.03654 (0.000017) 0.3636 (0.00028) Selmer (d=13) 30 0.05305 (0.000011) 0.03615 (0.000018) 0.3186 (0.00032) Selmer (d=14) 30 0.04928 (0.000060) 0.03546 (0.000051) 0.2804 (0.00040) Selmer (d=15) 30 0.046040 (2.0e-6) 0.03462 (0.000013) 0.2482 (0.00027) Selmer (d=16) 30 0.04318 (0.000011) 0.03365 (0.000014) 0.2208 (0.00028) Selmer (d=17) 30 0.040658 (3.3e-6) 0.03263 (0.000013) 0.1974 (0.00030) Selmer (d=18) 30 0.038411 (2.7e-6) 0.031596 (9.8e-6) 0.1774 (0.00022) Selmer (d=19) 30 0.036399 (2.2e-6) 0.030571 (8.0e-6) 0.1601 (0.00019) Selmer (d=20) 30 0.0346 (0.00011) 0.02955 (0.000093) 0.1452 (0.00019)

For Arnoux-Rauzy-Poincaré algorithm:

sage: algos = [ARP(d) for d in range(3,21)] sage: %time lyapunov_comparison_table(algos, n_orbits=30, n_iterations=10^7, ncpus=8) CPU times: user 226 ms, sys: 2.76 s, total: 2.99 s Wall time: 13min 20s Algorithm \#Orbits $\theta_1$ (std) $\theta_2$ (std) $1-\theta_2/\theta_1$ (std) +--------------------------------+----------+--------------------+---------------------+-----------------------------+ Arnoux-Rauzy-Poincar\'e (d=3) 30 0.4428 (0.00056) -0.1722 (0.00025) 1.3888 (0.00016) Arnoux-Rauzy-Poincar\'e (d=4) 30 0.6811 (0.00020) -0.16480 (0.000085) 1.24198 (0.000093) Arnoux-Rauzy-Poincar\'e (d=5) 30 0.7982 (0.00012) -0.0776 (0.00010) 1.0972 (0.00013) Arnoux-Rauzy-Poincar\'e (d=6) 30 0.83563 (0.000091) 0.0475 (0.00010) 0.9432 (0.00012) Arnoux-Rauzy-Poincar\'e (d=7) 30 0.8363 (0.00011) 0.1802 (0.00016) 0.7845 (0.00020) Arnoux-Rauzy-Poincar\'e (d=8) 30 0.8213 (0.00013) 0.3074 (0.00023) 0.6257 (0.00028) Arnoux-Rauzy-Poincar\'e (d=9) 30 0.8030 (0.00012) 0.4205 (0.00017) 0.4763 (0.00022) Arnoux-Rauzy-Poincar\'e (d=10) 30 0.7899 (0.00011) 0.5160 (0.00016) 0.3467 (0.00020) Arnoux-Rauzy-Poincar\'e (d=11) 30 0.7856 (0.00014) 0.5924 (0.00020) 0.2459 (0.00022) Arnoux-Rauzy-Poincar\'e (d=12) 30 0.7883 (0.00010) 0.6497 (0.00012) 0.1759 (0.00014) Arnoux-Rauzy-Poincar\'e (d=13) 30 0.7930 (0.00010) 0.6892 (0.00014) 0.1309 (0.00014) Arnoux-Rauzy-Poincar\'e (d=14) 30 0.7962 (0.00012) 0.7147 (0.00015) 0.10239 (0.000077) Arnoux-Rauzy-Poincar\'e (d=15) 30 0.7974 (0.00012) 0.7309 (0.00014) 0.08340 (0.000074) Arnoux-Rauzy-Poincar\'e (d=16) 30 0.7969 (0.00015) 0.7411 (0.00014) 0.07010 (0.000048) Arnoux-Rauzy-Poincar\'e (d=17) 30 0.7960 (0.00014) 0.7482 (0.00014) 0.06005 (0.000050) Arnoux-Rauzy-Poincar\'e (d=18) 30 0.7952 (0.00013) 0.7537 (0.00014) 0.05218 (0.000046) Arnoux-Rauzy-Poincar\'e (d=19) 30 0.7949 (0.00012) 0.7584 (0.00013) 0.04582 (0.000035) Arnoux-Rauzy-Poincar\'e (d=20) 30 0.7948 (0.00014) 0.7626 (0.00013) 0.04058 (0.000025)

The computation of the figure shown above is done with the code below:

sage: brun_list = [1.3683, 1.2203, 1.1503, 1.1063, 1.0743, 1.04926, 1.0284, 1.0104, 0.9947, 0.9806, 0.9677, 0.9558, 0.9447, 0.93433, 0.9246, 0.9153, 0.9065, 0.8982] sage: selmer_list = [ 1.3871, 1.1444, 0.9866, 0.8577, 0.7442, 0.6437, 0.5561, 0.4810, 0.4172, 0.3636, 0.3186, 0.2804, 0.2482, 0.2208, 0.1974, 0.1774, 0.1601, 0.1452] sage: arp_list = [1.3888, 1.24198, 1.0972, 0.9432, 0.7845, 0.6257, 0.4763, 0.3467, 0.2459, 0.1759, 0.1309, 0.10239, 0.08340, 0.07010, 0.06005, 0.05218, 0.04582, 0.04058] sage: brun_points = list(enumerate(brun_list, start=3)) sage: selmer_points = list(enumerate(selmer_list, start=3)) sage: arp_points = list(enumerate(arp_list, start=3)) sage: G = Graphics() sage: G += plot(1+1/(x-1), x, 3, 20, legend_label='Optimal algo:$1+1/(d-1)$', linestyle='dashed', color='blue', thickness=3) sage: G += line([(3,1), (20,1)], color='black', legend_label='Strong convergence threshold', linestyle='dotted', thickness=2) sage: G += line(brun_points, legend_label='Brun', color='cyan', thickness=3) sage: G += line(selmer_points, legend_label='Selmer', color='green', thickness=3) sage: G += line(arp_points, legend_label='ARP', color='red', thickness=3) sage: G.ymin(0) sage: G.axes_labels(['$d$','']) sage: G.show(title='Computation of first 2 Lyapunov Exponents: comparison of the value $1-\\theta_2/\\theta_1$\n for $d$-dimensional MCF algorithms Brun, Selmer and ARP for $3\\leq d\\leq 20$')

## Choisir un numéro de version pour un package

22 mars 2019 | Mise à jour: 27 mars 2019 | Catégories: Uncategorized | View Comments

Dans le choix d'un numéro de version pour un package, une bonne source à lire est le Semantic Versioning 2.0.0 que j'ai découvert dans la présentation How to make package managers cry faite par Kenneth Hoste à FOSDEM 2018 mentionnée sur sage-devel. On peut aussi lire le PEP 440 de Python.

Le résumé de Semantic Versioning indique:

Given a version number MAJOR.MINOR.PATCH, increment the: MAJOR version when you make incompatible API changes, MINOR version when you add functionality in a backwards-compatible manner, and PATCH version when you make backwards-compatible bug fixes. Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.

## Comment installer et utiliser RISE, une extension du notebook Jupyter pour faire des présentations

24 janvier 2019 | Catégories: sage | View Comments

La semaine dernière, Jeroen Demeyer a fait une présentation lors de l'Atelier PARI/GP 2019 au sujet de cypari2.

La présentation de Jeroen consistait en des diapositives HTML où les calculs sont faits en direct (avec Jupyter) et où on peut les modifier en direct dans les diapositives. Impressionant! Tout cela grâce au package Python RISE.

Pour installer et utiliser RISE, une extension du Jupyter Notebook pour faire des présentations éditables, il ne suffit pas de l'installer il faut aussi recopier les css au bon endroit. Pour l'installer dans Sage, il suffit de faire:

sage -pip install rise sage -sh jupyter-nbextension install rise --py --sys-prefix

Après on peut consulter ce démo sur youtube et la documentation de RISE est ici.

## Comparison of Wang tiling solvers

12 décembre 2018 | Catégories: sage, slabbe spkg, math | View Comments

During the last year, I have written a Python module to deal with Wang tiles containing about 4K lines of code including doctests and documentation.

It can be installed like this:

sage -pip install slabbe

It can be used like this:

sage: from slabbe import WangTileSet sage: tiles = [(2,4,2,1), (2,2,2,0), (1,1,3,1), (1,2,3,2), (3,1,3,3), ....: (0,1,3,1), (0,0,0,1), (3,1,0,2), (0,2,1,2), (1,2,1,4), (3,3,1,2)] sage: T0 = WangTileSet([map(str,t) for t in tiles]) sage: T0.tikz(ncolumns=11).pdf()

The module on wang tiles contains a class `WangTileSolver` which contains
three reductions of the Wang tiling problem the first using MILP solvers,
the second using SAT solvers and the third using Knuth's dancing links.

Here is one example of a tiling found using the dancing links reduction:

sage: %time tiling = T0.solver(10,10).solve(solver='dancing_links') CPU times: user 36 ms, sys: 12 ms, total: 48 ms Wall time: 65.5 ms sage: tiling.tikz().pdf()

All these reductions now allow me to compare the efficiency of various types of solvers restricted to the Wang tiling type of problems. Here is the list of solvers that I often use.

Solver | Description |
---|---|

'Gurobi' |
MILP solver |

'GLPK' |
MILP solver |

'PPL' |
MILP solver |

'LP' |
a SAT solver using a reduction to LP |

'cryptominisat' |
SAT solver |

'picosat' |
SAT solver |

'glucose' |
SAT solver |

'dancing_links' |
Knuth's algorihm |

In this recent work on the substitutive structure of Jeandel-Rao tilings, I introduced various Wang tile sets \(T_i\) for \(i\in\{0,1,\dots,12\}\). In this blog post, we will concentrate on the 11 Wang tile set \(T_0\) introduced by Jeandel and Rao as well as \(T_2\) containing 20 tiles and \(T_3\) containing 24 tiles.

**Tiling a n x n square**

The most natural question to ask is to find valid Wang tilings of \(n\times n\) square with given Wang tiles. Below is the time spent by each mentionned solvers to find a valid tiling of a \(n\times n\) square in less than 10 seconds for each of the three wang tile sets \(T_0\), \(T_2\) and \(T_3\).

We remark that MILP solvers are slower. Dancing links can solve 20x20 squares with Jeandel Rao tiles \(T_0\) and SAT solvers are performing very well with Glucose being the best as it can find a 55x55 tiling with Jeandel-Rao tiles \(T_0\) in less than 10 seconds.

**Finding all dominoes allowing a surrounding of given radius**

One thing that is often needed in my research is to enumerate all horizontal and vertical dominoes that allow a given surrounding radius. This is a difficult question in general as deciding if a given tile set admits a tiling of the infinite plane is undecidable. But in some cases, the information we get from the dominoes admitting a surrounding of radius 1, 2, 3 or 4 is enough to conclude that the tiling can be desubstituted for instance. This is why we need to answer this question as fast as possible.

Below is the comparison in the time taken by each solver to compute all vertical and horizontal dominoes allowing a surrounding of radius 1, 2 and 3 (in less than 1000 seconds for each execution).

What is surprising at first is that the solvers that performed well in the first \(n\times n\) square experience are not the best in the second experiment computing valid dominoes. Dancing links and the MILP solver Gurobi are now the best algorithms to compute all dominoes. They are followed by picosat and cryptominisat and then glucose.

**The source code of the above comparisons**

The source code of the above comparison can be found in this Jupyter notebook. Note that it depends on the use of Glucose as a Sage optional package (#26361) and on the most recent development version of slabbe optional Sage Package.

## Wooden laser-cut Jeandel-Rao tiles

07 septembre 2018 | Catégories: sage, slabbe spkg, math | View Comments

I have been working on Jeandel-Rao tiles lately.

Before the conference Model Sets and Aperiodic Order held in Durham UK (Sep 3-7 2018), I thought it would be a good idea to bring some real tiles at the conference. So I first decided of some conventions to represent the above tiles as topologically closed disk basically using the representation of integers in base 1:

With these shapes, I created a 33 x 19 patch. With 3cm on each side, the patch takes 99cm x 57cm just within the capacity of the laser cut machine (1m x 60 cm):

With the help of David Renault from LaBRI, we went at Coh@bit, the FabLab of Bordeaux University and we laser cut two 3mm thick plywood for a total of 1282 Wang tiles. This is the result:

One may recreate the 33 x 19 tiling as follows (note that I am using
Cartesian-like coordinates, so the first list `data[0]` actually is the first
column from bottom to top):

sage: data = [[10, 4, 6, 1, 3, 3, 7, 0, 9, 7, 2, 6, 1, 3, 8, 7, 0, 9, 7], ....: [4, 5, 6, 1, 8, 10, 4, 0, 9, 3, 8, 7, 0, 9, 7, 5, 0, 9, 3], ....: [3, 7, 6, 1, 7, 2, 5, 0, 9, 8, 7, 5, 0, 9, 3, 7, 0, 9, 10], ....: [10, 4, 6, 1, 3, 8, 7, 0, 9, 7, 5, 6, 1, 8, 10, 4, 0, 9, 3], ....: [2, 5, 6, 1, 8, 7, 5, 0, 9, 3, 7, 6, 1, 7, 2, 5, 0, 9, 8], ....: [8, 7, 6, 1, 7, 5, 6, 1, 8, 10, 4, 6, 1, 3, 8, 7, 0, 9, 7], ....: [7, 5, 6, 1, 3, 7, 6, 1, 7, 2, 5, 6, 1, 8, 7, 5, 0, 9, 3], ....: [3, 7, 6, 1, 10, 4, 6, 1, 3, 8, 7, 6, 1, 7, 5, 6, 1, 8, 10], ....: [10, 4, 6, 1, 3, 3, 7, 0, 9, 7, 5, 6, 1, 3, 7, 6, 1, 7, 2], ....: [2, 5, 6, 1, 8, 10, 4, 0, 9, 3, 7, 6, 1, 10, 4, 6, 1, 3, 8], ....: [8, 7, 6, 1, 7, 5, 5, 0, 9, 10, 4, 6, 1, 3, 3, 7, 0, 9, 7], ....: [7, 5, 6, 1, 3, 7, 6, 1, 10, 4, 5, 6, 1, 8, 10, 4, 0, 9, 3], ....: [3, 7, 6, 1, 10, 4, 6, 1, 3, 3, 7, 6, 1, 7, 2, 5, 0, 9, 8], ....: [10, 4, 6, 1, 3, 3, 7, 0, 9, 10, 4, 6, 1, 3, 8, 7, 0, 9, 7], ....: [4, 5, 6, 1, 8, 10, 4, 0, 9, 3, 3, 7, 0, 9, 7, 5, 0, 9, 3], ....: [3, 7, 6, 1, 7, 2, 5, 0, 9, 8, 10, 4, 0, 9, 3, 7, 0, 9, 10], ....: [10, 4, 6, 1, 3, 8, 7, 0, 9, 7, 5, 5, 0, 9, 10, 4, 0, 9, 3], ....: [2, 5, 6, 1, 8, 7, 5, 0, 9, 3, 7, 6, 1, 10, 4, 5, 0, 9, 8], ....: [8, 7, 6, 1, 7, 5, 6, 1, 8, 10, 4, 6, 1, 3, 3, 7, 0, 9, 7], ....: [7, 5, 6, 1, 3, 7, 6, 1, 7, 2, 5, 6, 1, 8, 10, 4, 0, 9, 3], ....: [3, 7, 6, 1, 10, 4, 6, 1, 3, 8, 7, 6, 1, 7, 2, 5, 0, 9, 8], ....: [10, 4, 6, 1, 3, 3, 7, 0, 9, 7, 2, 6, 1, 3, 8, 7, 0, 9, 7], ....: [4, 5, 6, 1, 8, 10, 4, 0, 9, 3, 8, 7, 0, 9, 7, 5, 0, 9, 3], ....: [3, 7, 6, 1, 7, 2, 5, 0, 9, 8, 7, 5, 0, 9, 3, 7, 0, 9, 10], ....: [10, 4, 6, 1, 3, 8, 7, 0, 9, 7, 5, 6, 1, 8, 10, 4, 0, 9, 3], ....: [3, 3, 7, 0, 9, 7, 5, 0, 9, 3, 7, 6, 1, 7, 2, 5, 0, 9, 8], ....: [8, 10, 4, 0, 9, 3, 7, 0, 9, 10, 4, 6, 1, 3, 8, 7, 0, 9, 7], ....: [7, 5, 5, 0, 9, 10, 4, 0, 9, 3, 3, 7, 0, 9, 7, 5, 0, 9, 3], ....: [3, 7, 6, 1, 10, 4, 5, 0, 9, 8, 10, 4, 0, 9, 3, 7, 0, 9, 10], ....: [10, 4, 6, 1, 3, 3, 7, 0, 9, 7, 5, 5, 0, 9, 10, 4, 0, 9, 3], ....: [2, 5, 6, 1, 8, 10, 4, 0, 9, 3, 7, 6, 1, 10, 4, 5, 0, 9, 8], ....: [8, 7, 6, 1, 7, 5, 5, 0, 9, 10, 4, 6, 1, 3, 3, 7, 0, 9, 7], ....: [7, 5, 6, 1, 3, 7, 6, 1, 10, 4, 5, 6, 1, 8, 10, 4, 0, 9, 3]]

The above patch have been chosen among 1000 other randomly generated as the closest to the asymptotic frequencies of the tiles in Jeandel-Rao tilings (or at least in the minimal subshift that I describe in the preprint):

sage: from collections import Counter sage: c = Counter(flatten(data)) sage: tile_count = [c[i] for i in range(11)]

The asymptotic frequencies:

sage: phi = golden_ratio.n() sage: Linv = [2*phi + 6, 2*phi + 6, 18*phi + 10, 2*phi + 6, 8*phi + 2, ....: 5*phi + 4, 2*phi + 6, 12/5*phi + 14/5, 8*phi + 2, ....: 2*phi + 6, 8*phi + 2] sage: perfect_proportions = vector([1/a for a in Linv])

Comparison of the number of tiles of each type with the expected frequency:

sage: header_row = ['tile id', 'Asymptotic frequency', 'Expected nb of copies', ....: 'Nb copies in the 33x19 patch'] sage: columns = [range(11), perfect_proportions, vector(perfect_proportions)*33*19, tile_count] sage: table(columns=columns, header_row=header_row) tile id Asymptotic frequency Expected nb of copies Nb copies in the 33x19 patch +---------+----------------------+-----------------------+------------------------------+ 0 0.108271182329550 67.8860313206280 67 1 0.108271182329550 67.8860313206280 65 2 0.0255593590340479 16.0257181143480 16 3 0.108271182329550 67.8860313206280 71 4 0.0669152706817991 41.9558747174880 42 5 0.0827118232955023 51.8603132062800 51 6 0.108271182329550 67.8860313206280 65 7 0.149627093977301 93.8161879237680 95 8 0.0669152706817991 41.9558747174880 44 9 0.108271182329550 67.8860313206280 67 10 0.0669152706817991 41.9558747174880 44

I brought the \(33\times19=641\) tiles at the conference and offered to the first 7 persons to find a \(7\times 7\) tiling the opportunity to keep the 49 tiles they used. 49 is a good number since the frequency of the lowest tile (with id 2) is about 2% which allows to have at least one copy of each tile in a subset of 49 tiles allowing a solution.

A natural question to ask is how many such \(7\times 7\) tilings does there
exist? With ticket #25125 that was merged in Sage 8.3 this Spring, it is
possible to enumerate and count solutions in parallel with Knuth dancing links
algorithm. After the installation of the Sage Optional package slabbe (`sage
-pip install slabbe`), one may compute that there are 152244 solutions.

sage: from slabbe import WangTileSet sage: tiles = [(2,4,2,1), (2,2,2,0), (1,1,3,1), (1,2,3,2), (3,1,3,3), ....: (0,1,3,1), (0,0,0,1), (3,1,0,2), (0,2,1,2), (1,2,1,4), (3,3,1,2)] sage: T0 = WangTileSet(tiles) sage: T0_solver = T0.solver(7,7) sage: %time T0_solver.number_of_solutions(ncpus=8) CPU times: user 16 ms, sys: 82.3 ms, total: 98.3 ms Wall time: 388 ms 152244

One may also get the list of all solutions and print one of them:

sage: %time L = T0_solver.all_solutions(); print(len(L)) 152244 CPU times: user 6.46 s, sys: 344 ms, total: 6.8 s Wall time: 6.82 s sage: L[0] A wang tiling of a 7 x 7 rectangle sage: L[0].table() # warning: the output is in Cartesian-like coordinates [[1, 8, 10, 4, 5, 0, 9], [1, 7, 2, 5, 6, 1, 8], [1, 3, 8, 7, 6, 1, 7], [0, 9, 7, 5, 6, 1, 3], [0, 9, 3, 7, 6, 1, 8], [1, 8, 10, 4, 6, 1, 7], [1, 7, 2, 2, 6, 1, 3]]

This is the number of distinct sets of 49 tiles which admits a 7x7 solution:

sage: from collections import Counter sage: def count_tiles(tiling): ....: C = Counter(flatten(tiling.table())) ....: return tuple(C.get(a,0) for a in range(11)) sage: Lfreq = map(count_tiles, L) sage: Lfreq_count = Counter(Lfreq) sage: len(Lfreq_count) 83258

Number of other solutions with the same set of 49 tiles:

sage: Counter(Lfreq_count.values()) Counter({1: 49076, 2: 19849, 3: 6313, 4: 3664, 6: 1410, 5: 1341, 7: 705, 8: 293, 9: 159, 14: 116, 10: 104, 12: 97, 18: 44, 11: 26, 15: 24, 13: 10, 17: 8, 22: 6, 32: 6, 16: 3, 28: 2, 19: 1, 21: 1})

How the number of \(k\times k\)-solutions grows for k from 0 to 9:

sage: [T0.solver(k,k).number_of_solutions() for k in range(10)] [0, 11, 85, 444, 1723, 9172, 50638, 152244, 262019, 1641695]

Unfortunately, most of those \(k\times k\)-solutions are not extendable to a tiling of the whole plane. Indeed the number of \(k\times k\) patches in the language of the minimal aperiodic subshift that I am able to describe and which is a proper subset of Jeandel-Rao tilings seems, according to some heuristic, to be something like:

[1, 11, 49, 108, 184, 268, 367, 483]

I do not share my (ugly) code for this computation yet, as I will rather share clean code soon when times come. So among the 152244 about only 483 (0.32%) of them are prolongable into a uniformly recurrent tiling of the plane.

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