We consider the measure on lozenge tilings of a hexagon in which the probabilistic weight of every tiling is proportional to $q^{volume}$ or $q^{-volume}$, where $0< q <1$. The dynamics starts from an exact sample of the measure $q^{-volume}$ (which is produced by Vadim Gorin’s program [2]). Then, by randomly moving vetical lozenges to the left, the measure $q^{-volume}$ becomes the measure $q^{volume}$.
The simulation is based on a bijectivisation of the Yang-Baxter equation [3]. Its details are explained in the forthcoming publication [1].
Here is a sample of three random tilings of the hexagon of size $50\times 50\times 50$ in the beginning, midway, and in the end of the simulation. Throughout the simulation, each random configuration has a distribution which is of $q$-Gibbs type, but with powers of $q$ reshuffled (these are the parameters in the corresponding skew Schur factors).
The data file is a list of lists of lists in Mathematica-readable format, of the form \(\{ \lambda(1),\lambda(2),\ldots,\lambda(T) \},\) where each $\lambda(t)$ is a list of weakly interlacing integer coordinates of the form \(\{ \{ 47 \},\{ 50,47 \} , \{ 50,49,47 \} ,\ldots, \} .\) Here $t$ is the time variable. The simulation data can be “coarse” in larger tilings, or finer with every step of the Markov chain recorded.
The simulations are gifs or movies.
Link to code
(python code for simulations, simple Mathematica code for drawing)
https://arxiv.org/abs/0804.3071
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https://www.sciencedirect.com/science/article/pii/S0001870808003253
https://arxiv.org/abs/1712.04584