Tuesday, May 30, 2017

A Puzzle of Clever Connections Nears a Happy End

The puzzle was given a memorable nickname, the “happy ending” problem (or “happy end” problem as originally dubbed by Erdős), for reasons that had nothing to do with math. Instead, it reflected the primary nonmathematical consequence of their discussion of points, lines and shapes: Esther Klein and George Szekeres fell in love and married on June 13, 1937. Yet as the decades passed, mathematicians made virtually no progress in proving the conjecture. (The only other shape whose result is known is a hexagon, which requires at least 17 points, as proved by Szekeres and Lindsay Peters in 2006.) Now, in work recently published in the Journal of the American Mathematical Society, Andrew Suk of the University of Illinois, Chicago, provides nearly decisive evidence that the intuition that guided Erdős and Szekeres more than 80 years ago was correct.



A Puzzle of Clever Connections Nears a Happy End

Thursday, November 19, 2015

On a class of random walks

$\newcommand{\bZ}{\mathbb{Z}}$ $\newcommand{\bR}{\mathbb{R}}$  $\newcommand{\bE}{\mathbb{E}}$ $\newcommand{\eC}{\mathscr{C}}$ $\newcommand{\bP}{\mathbb{P}}$  $\newcommand{\eF}{\mathscr{F}}$ $\newcommand{\whS}{\widehat{S}}$ $\DeclareMathOperator{\var}{\boldsymbol{var}}$ $\newcommand{\bC}{\mathbb{C}}$

I heard   of these random walks from my number-theorist colleague Andrei Jorza who  gave me a probabilistic translation of some    observations in number theory. What follows is rather a variation  on  that theme.



There is a whole family of such walks, parametrized by a complex number  $\newcommand{\ii}{\boldsymbol{i}}$ $\rho=\cos \theta +\ii \sin\theta$, $\theta\in [0,\pi]$, $\ii=\sqrt{-1}$.

This is  discrete time walk  on  the configuration space  $\newcommand{\lan}{\langle}$ $\newcommand{\ran}{\rangle}$

$$  \eC_\rho:= L[\rho] \times  C(\rho,-\rho), $$

where $C(\rho,-\rho)$ is the subgroup of $S^1$ generated by $\pm\rho$, i.e.,

$$ C(\rho,-\rho)=\bigl\{\; \pm \rho^k;\;\;k\in\bZ \;\bigr\},$$

and $L[\rho]\subset  \bC$ is the additive subgroup of $\bC$ generated by the elements $\rho^n$, $n\in\bZ_{\geq 0}$.

The state of the walk at time $n$ is given  by a pair of random variables $(X_n,  V_n)$, where one should think of $X_n\in L[\rho]$  as describing the "position'' and $V_n\in G(\rho,-\rho)$ as describing the ``velocity''   at time $n$.

Here is how the random walk takes place.  Suppose that at time $n$ we are in the configuration $(X_n, V_n)$. To decide where to go next, toss a fair coin.   If a  the Head shows up  then

$$V_{n+1}=\rho V_n,\;\; X_{n+1}= X_n+V_{n+1}. $$

Otherwise,

$$ V_{n+1}=-\rho V_n,\;\; X_{n+1}= X_n+V_{n+1}. $$

More abstractly, we are given a probability space $(\Omega,\eF,\bP)$ and a sequence of  independent, identically distributed variables

$$ S_n:\Omega\to\{-1,1\},\;\;\bP[S_n=\pm 1]=\frac{1}{2},\;\;n\in\bZ_{\geq 0}. $$

If we set
$$
\whS_n:=\prod_{k=1}^nS_k, $$
then we have
$$
V_n= \whS_n \rho^n,\;\;X_n=\sum_{k=1}^n V_n=\sum_{k=1}^n \whS_k\rho_k.
$$
Observing that

$$\bE[\whS_k]=0,\;\;\var[\whS_k]=\prod_{j=1}^k\var[S_j]=1,  $$ we deduce

$$\bE[X_n]=0,\;\;\var[S_n]=\bE[X_n\bar{X}_n] $$

$$=\sum_{j,k=1}^n \bE[ \whS_j\whS_k]\rho^{k-j}=\sum_{j=1}^n \bE[ \whS_j^2]+2\sum_{1\leq j<k\leq}^n \bE[ \whS_j\whS_k]\rho^{k-j}=n. $$

Example 1.   Suppose that $\theta=\frac{\pi}{2}$ so that $\rho=\ii$. In this case $L[\rho]$ is the lattice $\bZ^2\subset \bC$ and $G(\ii,-\ii)$ is the group  of forth  order roots of $1$.  At time $n=0$,  the moving particle is at the origin facing  East,. turns right or left  (in this case North/South) with equal probability. Once   reaches a new intersection, it turns  right/left  with equal probability.  The following MAPLE generated animation  a loop describes a 90-step   portion of one such walk.



This is somewhat typical   of  what one observes in general.


Example 2.   Suppose that  $\theta  =\frac{\pi}{3}$. In this case $G(\rho,-\rho)$ is the group of    6th order roots of  $1$ and $L[\rho]$ is a lattice in $\bC$.   The following  MAPLE generated   animation depicts a  90 step portion of such a walk and the patterns you observe are typical.




Example 3.    Assume   that the angle $\theta$ is small and $\frac{\theta}{\pi}$ is not rational,  say $\cos \theta=0.95$. The animation below  depicts a 90-step stretch of such a walk.




Example 4.   For comparison, I have included an animation of a 90-step segment of the usual uniform random walk on $\bZ^2$.




Clearly, I am interested to  figure out what is happening and I have a hard time asking a good question. A first  question  that comes to my mind would be to find   statistical invariants  that would distinguish the  random walk in Example 1 from the random walk in Example  4.    I will refer to them as $RW_1$ and $RW_4$

 Clear each of these two  walks   surround  many unit squares with vertices in the lattice $\bZ^2$.  For $j=1,4$ will denote by $s_j(n)$ the expected number of unit squares      surrounded by a  walk of $RW_j$ of length $n$.. Which grows faster as $n\to\infty$, $s_1(n)$ or $s_4(n)$?

I will probably update this posting as I get more ideas, but maybe this shot in the dark will initiate a conversation and will suggest other, better questions.