X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/16dcc.git/blobdiff_plain/e1fe6e435ee452003a7135763d26e2320756398c..fe1f98f02955dc8615b2671ed8529a02eb73f024:/stopping.tex?ds=sidebyside diff --git a/stopping.tex b/stopping.tex index 409dd83..1ac6999 100644 --- a/stopping.tex +++ b/stopping.tex @@ -33,18 +33,18 @@ P=\dfrac{1}{6} \left( 0&0&0&0&1&0&4&1 \\ 0&0&0&1&0&1&0&4 \end{array} -\right) +\right). \] \end{xpl} A specific random walk in this modified hypercube is first introduced (See section~\ref{sub:stop:formal}). We further -theoretical study this random walk to -provide a upper bound of fair sequences + study this random walk in a theoretical way to +provide an upper bound of fair sequences (See section~\ref{sub:stop:bound}). We finally complete these study with experimental -results that reduce this bound (Sec.~\ref{sub:stop:stop}). +results that reduce this bound (Sec.~\ref{sub:stop:exp}). Notice that for a general references on Markov chains see~\cite{LevinPeresWilmer2006}, and particularly Chapter~5 on stopping times. @@ -69,9 +69,13 @@ and $$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ -Intuitively speaking, $t_{\rm mix}$ is a mixing time -\textit{i.e.}, is the time until the matrix $X$ of a Markov chain -is $\epsilon$-close to a stationary distribution. +%% Intuitively speaking, $t_{\rm mix}$ is a mixing time +%% \textit{i.e.}, is the time until the matrix $X$ of a Markov chain +%% is $\epsilon$-close to a stationary distribution. + +Intutively speaking, $t_{\rm mix}(\varepsilon)$ is the time/steps required +to be sure to be $\varepsilon$-close to the staionary distribution, wherever +the chain starts. @@ -115,7 +119,7 @@ $$\P_X(X_\tau=Y)=\pi(Y).$$ A stopping time $\tau$ is a {\emph strong stationary time} if $X_{\tau}$ is -independent of $\tau$. +independent of $\tau$. The following result will be useful~\cite[Proposition~6.10]{LevinPeresWilmer2006}, \begin{thrm}\label{thm-sst} @@ -231,7 +235,8 @@ This probability is independent of the value of the other bits. Moving next in the chain, at each step, the $l$-th bit is switched from $0$ to $1$ or from $1$ to $0$ each time with the same probability. Therefore, for $t\geq \tau_\ell$, the -$\ell$-th bit of $X_t$ is $0$ or $1$ with the same probability, proving the +$\ell$-th bit of $X_t$ is $0$ or $1$ with the same probability, and +independently of the value of the other bits, proving the lemma.\end{proof} \begin{thrm} \label{prop:stop} @@ -345,7 +350,7 @@ direct application of lemma~\ref{prop:lambda} and~\ref{lm:stopprime}. \end{proof} Now using Markov Inequality, one has $\P_X(\tau > t)\leq \frac{E[\tau]}{t}$. -With $t=32N^2+16N\ln (N+1)$, one obtains: $\P_X(\tau > t)\leq \frac{1}{4}$. +With $t_n=32N^2+16N\ln (N+1)$, one obtains: $\P_X(\tau > t_n)\leq \frac{1}{4}$. Therefore, using the defintion of $t_{\rm mix)}$ and Theorem~\ref{thm-sst}, it follows that $t_{\rm mix}\leq 32N^2+16N\ln (N+1)=O(N^2)$. @@ -354,11 +359,11 @@ $t_{\rm mix}\leq 32N^2+16N\ln (N+1)=O(N^2)$. Notice that the calculus of the stationary time upper bound is obtained under the following constraint: for each vertex in the $\mathsf{N}$-cube there are one ongoing arc and one outgoing arc that are removed. -The calculus does not consider (balanced) Hamiltonian cycles, which +The calculus doesn't consider (balanced) Hamiltonian cycles, which are more regular and more binding than this constraint. Moreover, the bound -is obtained using Markov Inequality which is frequently coarse. For the -classical random walkin the $\mathsf{N}$-cube, without removing any +is obtained using the coarse Markov Inequality. For the +classical (lazzy) random walk the $\mathsf{N}$-cube, without removing any Hamiltonian cylce, the mixing time is in $\Theta(N\ln N)$. We conjecture that in our context, the mixing time is also in $\Theta(N\ln N)$. @@ -376,12 +381,6 @@ number of iterations such that all elements $\ell\in \llbracket 1,{\mathsf{N}} \ by calling this code many times with many instances of function and many seeds. -Practically speaking, for each number $\mathsf{N}$,$ 3 \le \mathsf{N} \le 16$, -10 functions have been generaed according to method presented in section~\ref{sec:hamilton}. For each of them, the calculus of the approximation of $E[\ts]$ -is executed 10000 times with a random seed. The table~\ref{table:stopping:moy} -summarizes results. It can be observed that the approximation is largely -wœsmaller than the upper bound given in theorem~\ref{prop:stop}. - \begin{algorithm}[ht] %\begin{scriptsize} \KwIn{a function $f$, an initial configuration $x^0$ ($\mathsf{N}$ bits)} @@ -389,36 +388,63 @@ wœsmaller than the upper bound given in theorem~\ref{prop:stop}. $\textit{nbit} \leftarrow 0$\; $x\leftarrow x^0$\; -$\textit{visited}\leftarrow\emptyset$\; - -\While{$\left\vert{\textit{visited}}\right\vert < \mathsf{N} $} +$\textit{fair}\leftarrow\emptyset$\; +\While{$\left\vert{\textit{fair}}\right\vert < \mathsf{N} $} { - $ s \leftarrow \textit{Random}(n)$ \; + $ s \leftarrow \textit{Random}(\mathsf{N})$ \; $\textit{image} \leftarrow f(x) $\; - \If{$x[s] \neq \textit{image}[s]$}{ - $\textit{visited} \leftarrow \textit{visited} \cup \{s\}$ + \If{$\textit{Random}(1) \neq 0$ and $x[s] \neq \textit{image}[s]$}{ + $\textit{fair} \leftarrow \textit{fair} \cup \{s\}$\; + $x[s] \leftarrow \textit{image}[s]$\; } - $x[s] \leftarrow \textit{image}[s]$\; $\textit{nbit} \leftarrow \textit{nbit}+1$\; } \Return{$\textit{nbit}$}\; %\end{scriptsize} -\caption{Pseudo Code of the stoping time calculus} +\caption{Pseudo Code of stoping time calculus } \label{algo:stop} \end{algorithm} - - - -\begin{table} -$$ -\begin{array}{|*{15}{l|}} -\hline -\mathsf{N} & 3 & 4 & 5 & 6 & 7& 8 & 9 & 10& 11 & 12 & 13 & 14 & 15 & 16 \\ -\hline -\mathsf{N} & 3 & 10.9 & 5 & 17.7 & 7& 25 & 9 & 32.7& 11 & 40.8 & 13 & 49.2 & 15 & 16 \\ -\hline -\end{array} -$$ -\caption{Average Stopping Time}\label{table:stopping:moy} -\end{table} +Practically speaking, for each number $\mathsf{N}$, $ 3 \le \mathsf{N} \le 16$, +10 functions have been generaed according to method presented in section~\ref{sec:hamilton}. For each of them, the calculus of the approximation of $E[\ts]$ +is executed 10000 times with a random seed. The Figure~\ref{fig:stopping:moy} +summarizes these results. In this one, a circle represents the +approximation of $E[\ts]$ for a given $\mathsf{N}$. +The line is the graph of the function $x \mapsto 2x\ln(2x+8)$. +It can firstly +be observed that the approximation is largely +smaller than the upper bound given in theorem~\ref{prop:stop}. +It can be further deduced that the conjecture of the previous section +is realistic according the graph of $x \mapsto 2x\ln(2x+8)$. + + + + + +% \begin{table} +% $$ +% \begin{array}{|*{14}{l|}} +% \hline +% \mathsf{N} & 4 & 5 & 6 & 7& 8 & 9 & 10& 11 & 12 & 13 & 14 & 15 & 16 \\ +% \hline +% \mathsf{N} & 21.8 & 28.4 & 35.4 & 42.5 & 50 & 57.7 & 65.6& 73.5 & 81.6 & 90 & 98.3 & 107.1 & 115.7 \\ +% \hline +% \end{array} +% $$ +% \caption{Average Stopping Time}\label{table:stopping:moy} +% \end{table} + +\begin{figure} +\centering +\includegraphics[scale=0.5]{complexity} +\caption{Average Stopping Time Approximation}\label{fig:stopping:moy} +\end{figure} + + + +%%% Local Variables: +%%% mode: latex +%%% TeX-master: "main" +%%% ispell-dictionary: "american" +%%% mode: flyspell +%%% End: