X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/16dcc.git/blobdiff_plain/70f3455e44e96f0ad00501af3d6f396bc09ef436..3434a6bb461a09975ace82f172dbf7ea647d882b:/stopping.tex?ds=inline diff --git a/stopping.tex b/stopping.tex index 1ac6999..539d653 100644 --- a/stopping.tex +++ b/stopping.tex @@ -60,11 +60,14 @@ $$\tv{\pi-\mu}=\frac{1}{2}\sum_{X\in\Bool^{\mathsf{N}}}|\pi(X)-\mu(X)|.$$ Moreov $\nu$ is a distribution on $\Bool^{\mathsf{N}}$, one has $$\tv{\pi-\mu}\leq \tv{\pi-\nu}+\tv{\nu-\mu}$$ -Let $P$ be the matrix of a Markov chain on $\Bool^{\mathsf{N}}$. $P(X,\cdot)$ is the -distribution induced by the $X$-th row of $P$. If the Markov chain induced by -$P$ has a stationary distribution $\pi$, then we define +Let $P$ be the matrix of a Markov chain on $\Bool^{\mathsf{N}}$. For any +$X\in \Bool^{\mathsf{N}}$, let $P(X,\cdot)$ be the distribution induced by the +${\rm bin}(X)$-th row of $P$, where ${\rm bin}(X)$ is the integer whose +binary encoding is $X$. If the Markov chain induced by $P$ has a stationary +distribution $\pi$, then we define $$d(t)=\max_{X\in\Bool^{\mathsf{N}}}\tv{P^t(X,\cdot)-\pi}.$$ +%\ANNOT{incohérence de notation $X$ : entier ou dans $B^N$ ?} and $$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ @@ -74,7 +77,7 @@ $$t_{\rm mix}(\varepsilon)=\min\{t \mid d(t)\leq \varepsilon\}.$$ %% 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 +to be sure to be $\varepsilon$-close to the stationary distribution, wherever the chain starts. @@ -117,7 +120,6 @@ $$\P_X(X_\tau=Y)=\pi(Y).$$ \subsection{Upper bound of Stopping Time}\label{sub:stop:bound} - A stopping time $\tau$ is a {\emph strong stationary time} if $X_{\tau}$ is independent of $\tau$. The following result will be useful~\cite[Proposition~6.10]{LevinPeresWilmer2006}, @@ -249,7 +251,12 @@ let $S_{X,\ell}$ be the random variable that counts the number of steps from $X$ until we reach a configuration where $\ell$ is fair. More formally -$$S_{X,\ell}=\min \{t \geq 1\mid h(X_{t-1})\neq \ell\text{ and }Z_t=(\ell,.)\text{ and } X_0=X\}.$$ +\[ +\begin{array}{rcl} +S_{X,\ell}&=&\min \{t \geq 1\mid h(X_{t-1})\neq \ell\text{ and }Z_t=(\ell,.) \\ +&& \qquad \text{ and } X_0=X\}. +\end{array} +\] % We denote by % $$\lambda_h=\max_{X,\ell} S_{X,\ell}.$$ @@ -296,8 +303,14 @@ has, for every $i$, $\P(S_{X,\ell}\geq 2i)\leq since $S_{X,\ell}$ is positive, it is known~\cite[lemma 2.9]{proba}, that $$E[S_{X,\ell}]=\sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq i).$$ Since $\P(S_{X,\ell}\geq i)\geq \P(S_{X,\ell}\geq i+1)$, one has -$$E[S_{X,\ell}]=\sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq i)\leq -\P(S_{X,\ell}\geq 1)+\P(S_{X,\ell}\geq 2)+2 \sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq 2i).$$ +\[ +\begin{array}{rcl} + E[S_{X,\ell}]&=&\sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq i)\\ +&\leq& +\P(S_{X,\ell}\geq 1) +\P(S_{X,\ell}\geq 2)\\ +&& \qquad +2 \sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq 2i). +\end{array} +\] Consequently, $$E[S_{X,\ell}]\leq 1+1+2 \sum_{i=1}^{+\infty}\left(1-\frac{1}{4{\mathsf{N}}^2}\right)^i=2+2(4{\mathsf{N}}^2-1)=8{\mathsf{N}}^2,$$ @@ -406,7 +419,7 @@ $\textit{fair}\leftarrow\emptyset$\; \end{algorithm} 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 $E[\ts]$ +10 functions have been generated 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 $\mathsf{N}$. @@ -436,7 +449,7 @@ is realistic according the graph of $x \mapsto 2x\ln(2x+8)$. \begin{figure} \centering -\includegraphics[scale=0.5]{complexity} +\includegraphics[width=0.49\textwidth]{complexity} \caption{Average Stopping Time Approximation}\label{fig:stopping:moy} \end{figure}