ajout espace bib

[rairo15.git] / stopping.tex

diff --git a/stopping.tex b/stopping.tex
index 0ad3e8a7bffba16803c5a9c8d0700e8cb6fbd0cc..3a07e06a67fc073968d7355d2bff8196d485c29f 100644 (file)
--- a/stopping.tex
+++ b/stopping.tex
@@ -75,29 +75,31 @@ P=\dfrac{1}{6} \left(
 
 
 
 
 
 

-This section considers functions $f: \Bool^n \rightarrow \Bool^n $ 
+This section considers functions $f: \Bool^{\mathsf{N}} \rightarrow \Bool^{\mathsf{N}} $ 

 issued from an hypercube where an Hamiltonian path has been removed.
 A specific random walk in this modified hypercube is first 
 introduced. We further detail
 a theoretical study on the length of the path 
 which is sufficient to follow to get a uniform distribution.
 issued from an hypercube where an Hamiltonian path has been removed.
 A specific random walk in this modified hypercube is first 
 introduced. We further detail
 a theoretical study on the length of the path 
 which is sufficient to follow to get a uniform distribution.

- 
+Notice that for a general references on Markov chains
+see~\cite{LevinPeresWilmer2006}
+, and particularly Chapter~5 on stopping times.  

 
 
 
 
 
 
 
 

-First of all, let $\pi$, $\mu$ be two distributions on $\Bool^n$. The total
+First of all, let $\pi$, $\mu$ be two distributions on $\Bool^{\mathsf{N}}$. The total

 variation distance between $\pi$ and $\mu$ is denoted $\tv{\pi-\mu}$ and is
 defined by
 variation distance between $\pi$ and $\mu$ is denoted $\tv{\pi-\mu}$ and is
 defined by

-$$\tv{\pi-\mu}=\max_{A\subset \Bool^n} |\pi(A)-\mu(A)|.$$ It is known that
-$$\tv{\pi-\mu}=\frac{1}{2}\sum_{X\in\Bool^n}|\pi(X)-\mu(X)|.$$ Moreover, if
-$\nu$ is a distribution on $\Bool^n$, one has
+$$\tv{\pi-\mu}=\max_{A\subset \Bool^{\mathsf{N}}} |\pi(A)-\mu(A)|.$$ It is known that
+$$\tv{\pi-\mu}=\frac{1}{2}\sum_{X\in\Bool^{\mathsf{N}}}|\pi(X)-\mu(X)|.$$ Moreover, if
+$\nu$ is a distribution on $\Bool^{\mathsf{N}}$, one has

 $$\tv{\pi-\mu}\leq \tv{\pi-\nu}+\tv{\nu-\mu}$$
 
 $$\tv{\pi-\mu}\leq \tv{\pi-\nu}+\tv{\nu-\mu}$$
 

-Let $P$ be the matrix of a Markov chain on $\Bool^n$. $P(X,\cdot)$ is the
+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
 distribution induced by the $X$-th row of $P$. If the Markov chain induced by
 $P$ has a stationary distribution $\pi$, then we define

-$$d(t)=\max_{X\in\Bool^n}\tv{P^t(X,\cdot)-\pi}.$$
+$$d(t)=\max_{X\in\Bool^{\mathsf{N}}}\tv{P^t(X,\cdot)-\pi}.$$

 
 and
 
 
 and
 


@@ -121,10 +123,10 @@ $$t_{\rm mix}(\varepsilon)\leq \lceil\log_2(\varepsilon^{-1})\rceil t_{\rm mix}(
 
 
 
 
 
 

-Let $(X_t)_{t\in \mathbb{N}}$ be a sequence of $\Bool^n$ valued random
+Let $(X_t)_{t\in \mathbb{N}}$ be a sequence of $\Bool^{\mathsf{N}}$ valued random

 variables. A $\mathbb{N}$-valued random variable $\tau$ is a {\it stopping
   time} for the sequence $(X_i)$ if for each $t$ there exists $B_t\subseteq
 variables. A $\mathbb{N}$-valued random variable $\tau$ is a {\it stopping
   time} for the sequence $(X_i)$ if for each $t$ there exists $B_t\subseteq

-(\Bool^n)^{t+1}$ such that $\{\tau=t\}=\{(X_0,X_1,\ldots,X_t)\in B_t\}$. 
+(\Bool^{\mathsf{N}})^{t+1}$ such that $\{\tau=t\}=\{(X_0,X_1,\ldots,X_t)\in B_t\}$. 

 In other words, the event $\{\tau = t \}$ only depends on the values of 
 $(X_0,X_1,\ldots,X_t)$, not on $X_k$ with $k > t$. 
  
 In other words, the event $\{\tau = t \}$ only depends on the values of 
 $(X_0,X_1,\ldots,X_t)$, not on $X_k$ with $k > t$. 
  


@@ -138,72 +140,77 @@ randomized stopping time (possibly depending on the starting position $X$),
 such that  the distribution of $X_\tau$ is $\pi$:
 $$\P_X(X_\tau=Y)=\pi(Y).$$
 
 such that  the distribution of $X_\tau$ is $\pi$:
 $$\P_X(X_\tau=Y)=\pi(Y).$$
 

+A stopping time $\tau$ is a {\emph strong stationary time} if $X_{\tau}$ is
+independent of $\tau$. 
+

 
 

-\begin{Theo}
-If $\tau$ is a strong stationary time, then $d(t)\leq \max_{X\in\Bool^n}
+\begin{thrm}
+If $\tau$ is a strong stationary time, then $d(t)\leq \max_{X\in\Bool^{\mathsf{N}}}

 \P_X(\tau > t)$.
 \P_X(\tau > t)$.

-\end{Theo}
+\end{thrm}

 
 
 %Let $\Bool^n$ be the set of words of length $n$. 
 Let $E=\{(X,Y)\mid
 
 
 %Let $\Bool^n$ be the set of words of length $n$. 
 Let $E=\{(X,Y)\mid

-X\in \Bool^n, Y\in \Bool^n,\ X=Y \text{ or } X\oplus Y \in 0^*10^*\}$.
+X\in \Bool^{\mathsf{N}}, Y\in \Bool^{\mathsf{N}},\ X=Y \text{ or } X\oplus Y \in 0^*10^*\}$.

 In other words, $E$ is the set of all the edges in the classical 
 In other words, $E$ is the set of all the edges in the classical 

-$n$-cube. 
-Let $h$ be a function from $\Bool^n$ into $\llbracket 1, n \rrbracket$.
+${\mathsf{N}}$-cube. 
+Let $h$ be a function from $\Bool^{\mathsf{N}}$ into $\llbracket 1, {\mathsf{N}} \rrbracket$.

 Intuitively speaking $h$ aims at memorizing for each node 
 Intuitively speaking $h$ aims at memorizing for each node 

-$X \in \Bool^n$ which edge is removed in the Hamiltonian cycle,
-\textit{i.e.} which bit in $\llbracket 1, n \rrbracket$ 
+$X \in \Bool^{\mathsf{N}}$ which edge is removed in the Hamiltonian cycle,
+\textit{i.e.} which bit in $\llbracket 1, {\mathsf{N}} \rrbracket$ 

 cannot be switched.
 
 
 
 We denote by $E_h$ the set $E\setminus\{(X,Y)\mid X\oplus Y =
 cannot be switched.
 
 
 
 We denote by $E_h$ the set $E\setminus\{(X,Y)\mid X\oplus Y =

-0^{n-h(X)}10^{h(X)-1}\}$. This is the set of the modified hypercube, 
-\textit{i.e.}, the $n$-cube where the Hamiltonian cycle $h$ 
+0^{{\mathsf{N}}-h(X)}10^{h(X)-1}\}$. This is the set of the modified hypercube, 
+\textit{i.e.}, the ${\mathsf{N}}$-cube where the Hamiltonian cycle $h$ 

 has been removed.
 
 We define the Markov matrix $P_h$ for each line $X$ and 
 each column $Y$  as follows:
 has been removed.
 
 We define the Markov matrix $P_h$ for each line $X$ and 
 each column $Y$  as follows:

-$$\left\{
+\begin{equation}
+\left\{

 \begin{array}{ll}
 \begin{array}{ll}

-P_h(X,X)=\frac{1}{2}+\frac{1}{2n} & \\
+P_h(X,X)=\frac{1}{2}+\frac{1}{2{\mathsf{N}}} & \\

 P_h(X,Y)=0 & \textrm{if  $(X,Y)\notin E_h$}\\
 P_h(X,Y)=0 & \textrm{if  $(X,Y)\notin E_h$}\\

-P_h(X,Y)=\frac{1}{2n} & \textrm{if $X\neq Y$ and $(X,Y) \in E_h$}
+P_h(X,Y)=\frac{1}{2{\mathsf{N}}} & \textrm{if $X\neq Y$ and $(X,Y) \in E_h$}

 \end{array}
 \right.
 \end{array}
 \right.

-$$ 
+\label{eq:Markov:rairo}
+\end{equation} 

 
 

-We denote by $\ov{h} : \Bool^n \rightarrow \Bool^n$ the function 
-such that for any $X \in \Bool^n $, 
-$(X,\ov{h}(X)) \in E$ and $X\oplus\ov{h}(X)=0^{n-h(X)}10^{h(X)-1}$. 
-The function $\ov{h}$ is said {\it square-free} if for every $X\in \Bool^n$,
+We denote by $\ov{h} : \Bool^{\mathsf{N}} \rightarrow \Bool^{\mathsf{N}}$ the function 
+such that for any $X \in \Bool^{\mathsf{N}} $, 
+$(X,\ov{h}(X)) \in E$ and $X\oplus\ov{h}(X)=0^{{\mathsf{N}}-h(X)}10^{h(X)-1}$. 
+The function $\ov{h}$ is said {\it square-free} if for every $X\in \Bool^{\mathsf{N}}$,

 $\ov{h}(\ov{h}(X))\neq X$. 
 
 $\ov{h}(\ov{h}(X))\neq X$. 
 

-\begin{Lemma}\label{lm:h}
+\begin{lmm}\label{lm:h}

 If $\ov{h}$ is bijective and square-free, then $h(\ov{h}^{-1}(X))\neq h(X)$.
 If $\ov{h}$ is bijective and square-free, then $h(\ov{h}^{-1}(X))\neq h(X)$.

-\end{Lemma}
+\end{lmm}

 
 \begin{proof}
 Let $\ov{h}$ be bijective.
 
 \begin{proof}
 Let $\ov{h}$ be bijective.

-Let $k\in \llbracket 1, n \rrbracket$ s.t. $h(\ov{h}^{-1}(X))=k$.
+Let $k\in \llbracket 1, {\mathsf{N}} \rrbracket$ s.t. $h(\ov{h}^{-1}(X))=k$.

 Then $(\ov{h}^{-1}(X),X)$ belongs to $E$ and 
 Then $(\ov{h}^{-1}(X),X)$ belongs to $E$ and 

-$\ov{h}^{-1}(X)\oplus X = 0^{n-k}10^{k-1}$.
+$\ov{h}^{-1}(X)\oplus X = 0^{{\mathsf{N}}-k}10^{k-1}$.

 Let us suppose $h(X) = h(\ov{h}^{-1}(X))$. In such a case, $h(X) =k$.
 By definition of $\ov{h}$, $(X, \ov{h}(X)) \in E $ and 
 Let us suppose $h(X) = h(\ov{h}^{-1}(X))$. In such a case, $h(X) =k$.
 By definition of $\ov{h}$, $(X, \ov{h}(X)) \in E $ and 

-$X\oplus\ov{h}(X)=0^{n-h(X)}10^{h(X)-1} = 0^{n-k}10^{k-1}$.
+$X\oplus\ov{h}(X)=0^{{\mathsf{N}}-h(X)}10^{h(X)-1} = 0^{{\mathsf{N}}-k}10^{k-1}$.

 Thus $\ov{h}(X)= \ov{h}^{-1}(X)$, which leads to $\ov{h}(\ov{h}(X))= X$.
 This contradicts the square-freeness of $\ov{h}$.
 \end{proof}
 
 Let $Z$ be a random variable that is uniformly distributed over
 Thus $\ov{h}(X)= \ov{h}^{-1}(X)$, which leads to $\ov{h}(\ov{h}(X))= X$.
 This contradicts the square-freeness of $\ov{h}$.
 \end{proof}
 
 Let $Z$ be a random variable that is uniformly distributed over

-$\llbracket 1, n \rrbracket \times \Bool$.
-For $X\in \Bool^n$, we
+$\llbracket 1, {\mathsf{N}} \rrbracket \times \Bool$.
+For $X\in \Bool^{\mathsf{N}}$, we

 define, with $Z=(i,b)$,  
 $$
 \left\{
 \begin{array}{ll}
 define, with $Z=(i,b)$,  
 $$
 \left\{
 \begin{array}{ll}

-f(X,Z)=X\oplus (0^{n-i}10^{i-1}) & \text{if } b=1 \text{ and } i\neq h(X),\\
+f(X,Z)=X\oplus (0^{{\mathsf{N}}-i}10^{i-1}) & \text{if } b=1 \text{ and } i\neq h(X),\\

 f(X,Z)=X& \text{otherwise.} 
 \end{array}\right.
 $$
 f(X,Z)=X& \text{otherwise.} 
 \end{array}\right.
 $$


@@ -218,7 +225,7 @@ $$
 %%%%%%%%%%%%%%%%%%%%%%%%%%%ù
 %\section{Stopping time}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%ù
 %\section{Stopping time}
 

-An integer $\ell\in \llbracket 1,n \rrbracket$ is said {\it fair} 
+An integer $\ell\in \llbracket 1,{\mathsf{N}} \rrbracket$ is said {\it fair} 

 at time $t$ if there
 exists $0\leq j <t$ such that $Z_{j+1}=(\ell,\cdot)$ and $h(X_j)\neq \ell$.
 In other words, there exist a date $j$ before $t$ where 
 at time $t$ if there
 exists $0\leq j <t$ such that $Z_{j+1}=(\ell,\cdot)$ and $h(X_j)\neq \ell$.
 In other words, there exist a date $j$ before $t$ where 


@@ -226,14 +233,14 @@ the first element of the random variable $Z$ is exactly $l$
 (\textit{i.e.}, $l$ is the strategy at date $j$) 
 and where the configuration $X_j$ allows to traverse the edge $l$.  
  
 (\textit{i.e.}, $l$ is the strategy at date $j$) 
 and where the configuration $X_j$ allows to traverse the edge $l$.  
  

-Let $\ts$ be the first time all the elements of $\llbracket 1, n \rrbracket$
+Let $\ts$ be the first time all the elements of $\llbracket 1, {\mathsf{N}} \rrbracket$

 are fair. The integer $\ts$ is a randomized stopping time for
 the Markov chain $(X_t)$.
 
 
 are fair. The integer $\ts$ is a randomized stopping time for
 the Markov chain $(X_t)$.
 
 

-\begin{Lemma}
+\begin{lmm}

 The integer $\ts$ is a strong stationary time.
 The integer $\ts$ is a strong stationary time.

-\end{Lemma}
+\end{lmm}

 
 \begin{proof}
 Let $\tau_\ell$ be the first time that $\ell$ is fair. The random variable
 
 \begin{proof}
 Let $\tau_\ell$ be the first time that $\ell$ is fair. The random variable


@@ -250,57 +257,59 @@ 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
 lemma.\end{proof}
 
 $\ell$-th bit of $X_t$ is $0$ or $1$ with the same probability, proving the
 lemma.\end{proof}
 

-\begin{Theo} \label{prop:stop}
+\begin{thrm} \label{prop:stop}

 If $\ov{h}$ is bijective and square-free, then
 If $\ov{h}$ is bijective and square-free, then

-$E[\ts]\leq 8n^2+ n\ln (n+1)$. 
-\end{Theo}
+$E[\ts]\leq 8{\mathsf{N}}^2+ 4{\mathsf{N}}\ln ({\mathsf{N}}+1)$. 
+\end{thrm}

 
 

-For each $X\in \Bool^n$ and $\ell\in\llbracket 1,n\rrbracket$, 
+For each $X\in \Bool^{\mathsf{N}}$ and $\ell\in\llbracket 1,{\mathsf{N}}\rrbracket$, 

 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
 $$S_{X,\ell}=\min \{t \geq 1\mid h(X_{t-1})\neq \ell\text{ and }Z_t=(\ell,.)\text{ and } X_0=X\}.$$
 
 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
 $$S_{X,\ell}=\min \{t \geq 1\mid h(X_{t-1})\neq \ell\text{ and }Z_t=(\ell,.)\text{ and } X_0=X\}.$$
 

- We denote by
-$$\lambda_h=\max_{X,\ell} S_{X,\ell}.$$
+%  We denote by
+% $$\lambda_h=\max_{X,\ell} S_{X,\ell}.$$

 
 
 
 

-\begin{Lemma}\label{prop:lambda}
-If $\ov{h}$ is a square-free bijective function, then the inequality 
-$E[\lambda_h]\leq 8n^2$ is established.
-
-\end{Lemma}
+\begin{lmm}\label{prop:lambda}
+Let $\ov{h}$ is a square-free bijective function. Then
+for all $X$ and 
+all $\ell$, 
+the inequality 
+$E[S_{X,\ell}]\leq 8{\mathsf{N}}^2$ is established.
+\end{lmm}

 
 \begin{proof}
 For every $X$, every $\ell$, one has $\P(S_{X,\ell})\leq 2)\geq
 
 \begin{proof}
 For every $X$, every $\ell$, one has $\P(S_{X,\ell})\leq 2)\geq

-\frac{1}{4n^2}$. 
+\frac{1}{4{\mathsf{N}}^2}$. 

 Let $X_0= X$.
 Indeed, 
 \begin{itemize}
 \item if $h(X)\neq \ell$, then
 Let $X_0= X$.
 Indeed, 
 \begin{itemize}
 \item if $h(X)\neq \ell$, then

-$\P(S_{X,\ell}=1)=\frac{1}{2n}\geq \frac{1}{4n^2}$. 
+$\P(S_{X,\ell}=1)=\frac{1}{2{\mathsf{N}}}\geq \frac{1}{4{\mathsf{N}}^2}$. 

 \item otherwise, $h(X)=\ell$, then
 $\P(S_{X,\ell}=1)=0$.
 \item otherwise, $h(X)=\ell$, then
 $\P(S_{X,\ell}=1)=0$.

-But in this case, intutively, it is possible to move
+But in this case, intuitively, it is possible to move

 from $X$ to $\ov{h}^{-1}(X)$ (with probability $\frac{1}{2N}$). And in
 $\ov{h}^{-1}(X)$ the $l$-th bit can be switched. 
 More formally,
 since $\ov{h}$ is square-free,
 $\ov{h}(X)=\ov{h}(\ov{h}(\ov{h}^{-1}(X)))\neq \ov{h}^{-1}(X)$. It follows
 that $(X,\ov{h}^{-1}(X))\in E_h$. We thus have
 from $X$ to $\ov{h}^{-1}(X)$ (with probability $\frac{1}{2N}$). And in
 $\ov{h}^{-1}(X)$ the $l$-th bit can be switched. 
 More formally,
 since $\ov{h}$ is square-free,
 $\ov{h}(X)=\ov{h}(\ov{h}(\ov{h}^{-1}(X)))\neq \ov{h}^{-1}(X)$. It follows
 that $(X,\ov{h}^{-1}(X))\in E_h$. We thus have

-$P(X_1=\ov{h}^{-1}(X))=\frac{1}{2N}$. Now, by Lemma~\ref{lm:h},
+$P(X_1=\ov{h}^{-1}(X))=\frac{1}{2{\mathsf{N}}}$. Now, by Lemma~\ref{lm:h},

 $h(\ov{h}^{-1}(X))\neq h(X)$. Therefore $\P(S_{x,\ell}=2\mid
 $h(\ov{h}^{-1}(X))\neq h(X)$. Therefore $\P(S_{x,\ell}=2\mid

-X_1=\ov{h}^{-1}(X))=\frac{1}{2N}$, proving that $\P(S_{x,\ell}\leq 2)\geq
-\frac{1}{4N^2}$.
+X_1=\ov{h}^{-1}(X))=\frac{1}{2{\mathsf{N}}}$, proving that $\P(S_{x,\ell}\leq 2)\geq
+\frac{1}{4{\mathsf{N}}^2}$.

 \end{itemize}
 
 
 
 
 \end{itemize}
 
 
 
 

-Therefore, $\P(S_{X,\ell}\geq 3)\leq 1-\frac{1}{4n^2}$. By induction, one
+Therefore, $\P(S_{X,\ell}\geq 3)\leq 1-\frac{1}{4{\mathsf{N}}^2}$. By induction, one

 has, for every $i$, $\P(S_{X,\ell}\geq 2i)\leq
 has, for every $i$, $\P(S_{X,\ell}\geq 2i)\leq

-\left(1-\frac{1}{4n^2}\right)^i$.
+\left(1-\frac{1}{4{\mathsf{N}}^2}\right)^i$.

  Moreover,
 since $S_{X,\ell}$ is positive, it is known~\cite[lemma 2.9]{proba}, that
 $$E[S_{X,\ell}]=\sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq i).$$
  Moreover,
 since $S_{X,\ell}$ is positive, it is known~\cite[lemma 2.9]{proba}, that
 $$E[S_{X,\ell}]=\sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq i).$$


@@ -309,38 +318,52 @@ $$E[S_{X,\ell}]=\sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq i)\leq
 \P(S_{X,\ell}\geq 1)+\P(S_{X,\ell}\geq 2)+2 \sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq 2i).$$
 Consequently,
 $$E[S_{X,\ell}]\leq 1+1+2
 \P(S_{X,\ell}\geq 1)+\P(S_{X,\ell}\geq 2)+2 \sum_{i=1}^{+\infty}\P(S_{X,\ell}\geq 2i).$$
 Consequently,
 $$E[S_{X,\ell}]\leq 1+1+2

-\sum_{i=1}^{+\infty}\left(1-\frac{1}{4n^2}\right)^i=2+2(4n^2-1)=8n^2,$$
+\sum_{i=1}^{+\infty}\left(1-\frac{1}{4{\mathsf{N}}^2}\right)^i=2+2(4{\mathsf{N}}^2-1)=8{\mathsf{N}}^2,$$

 which concludes the proof.
 \end{proof}
 
 which concludes the proof.
 \end{proof}
 

-Let $\ts^\prime$ be the first time that there are exactly $n-1$ fair
-elements. 
+Let $\ts^\prime$ be the time used to get all the bits but one fair.

 
 

-\begin{Lemma}\label{lm:stopprime}
-One has $E[\ts^\prime]\leq n \ln (n+1).$
-\end{Lemma}
+\begin{lmm}\label{lm:stopprime}
+One has $E[\ts^\prime]\leq 4{\mathsf{N}} \ln ({\mathsf{N}}+1).$
+\end{lmm}

 
 \begin{proof}
 This is a classical  Coupon Collector's like problem. Let $W_i$ be the
 random variable counting the number of moves done in the Markov chain while
 
 \begin{proof}
 This is a classical  Coupon Collector's like problem. Let $W_i$ be the
 random variable counting the number of moves done in the Markov chain while

-we had exactly $i-1$ fair bits. One has $\ts^\prime=\sum_{i=1}^{n-1}W_i$.
+we had exactly $i-1$ fair bits. One has $\ts^\prime=\sum_{i=1}^{{\mathsf{N}}-1}W_i$.

  But when we are at position $X$ with $i-1$ fair bits, the probability of
  But when we are at position $X$ with $i-1$ fair bits, the probability of

- obtaining a new fair bit is either $1-\frac{i-1}{n}$ if $h(X)$ is fair,
- or  $1-\frac{i-2}{n}$ if $h(X)$ is not fair. It follows that 
-$E[W_i]\leq \frac{n}{n-i+2}$. Therefore
-$$E[\ts^\prime]=\sum_{i=1}^{n-1}E[W_i]\leq n\sum_{i=1}^{n-1}
- \frac{1}{n-i+2}=n\sum_{i=3}^{n+1}\frac{1}{i}.$$
-
-But $\sum_{i=1}^{n+1}\frac{1}{i}\leq 1+\ln(n+1)$. It follows that
-$1+\frac{1}{2}+\sum_{i=3}^{n+1}\frac{1}{i}\leq 1+\ln(n+1).$
+ obtaining a new fair bit is either $1-\frac{i-1}{{\mathsf{N}}}$ if $h(X)$ is fair,
+ or  $1-\frac{i-2}{{\mathsf{N}}}$ if $h(X)$ is not fair. 
+
+Therefore,
+$\P (W_i=k)\leq \left(\frac{i-1}{{\mathsf{N}}}\right)^{k-1} \frac{{\mathsf{N}}-i+2}{{\mathsf{N}}}.$
+Consequently, we have $\P(W_i\geq k)\leq \left(\frac{i-1}{{\mathsf{N}}}\right)^{k-1} \frac{{\mathsf{N}}-i+2}{{\mathsf{N}}-i+1}.$
+It follows that $E[W_i]=\sum_{k=1}^{+\infty} \P (W_i\geq k)\leq {\mathsf{N}} \frac{{\mathsf{N}}-i+2}{({\mathsf{N}}-i+1)^2}\leq \frac{4{\mathsf{N}}}{{\mathsf{N}}-i+2}$.
+
+
+
+It follows that 
+$E[W_i]\leq \frac{4{\mathsf{N}}}{{\mathsf{N}}-i+2}$. Therefore
+$$E[\ts^\prime]=\sum_{i=1}^{{\mathsf{N}}-1}E[W_i]\leq 
+4{\mathsf{N}}\sum_{i=1}^{{\mathsf{N}}-1} \frac{1}{{\mathsf{N}}-i+2}=
+4{\mathsf{N}}\sum_{i=3}^{{\mathsf{N}}+1}\frac{1}{i}.$$
+
+But $\sum_{i=1}^{{\mathsf{N}}+1}\frac{1}{i}\leq 1+\ln({\mathsf{N}}+1)$. It follows that
+$1+\frac{1}{2}+\sum_{i=3}^{{\mathsf{N}}+1}\frac{1}{i}\leq 1+\ln({\mathsf{N}}+1).$

 Consequently,
 Consequently,

-$E[\ts^\prime]\leq n (-\frac{1}{2}+\ln(n+1))\leq n\ln(n+1)$.
+$E[\ts^\prime]\leq 
+4{\mathsf{N}} (-\frac{1}{2}+\ln({\mathsf{N}}+1))\leq 
+4{\mathsf{N}}\ln({\mathsf{N}}+1)$.

 \end{proof}
 
 One can now prove Theorem~\ref{prop:stop}.
 
 \begin{proof}
 \end{proof}
 
 One can now prove Theorem~\ref{prop:stop}.
 
 \begin{proof}

-One has $\ts\leq \ts^\prime+\lambda_h$. Therefore,
+Since $\ts^\prime$ is the time used to obtain $\mathsf{N}-1$ fair bits.
+Assume that the last unfair bit is $\ell$. One has
+$\ts=\ts^\prime+S_{X_\tau,\ell}$, and therefore
+$E[\ts] = E[\ts^\prime]+E[S_{X_\tau,\ell}]$. Therefore,

 Theorem~\ref{prop:stop} is a direct application of
 lemma~\ref{prop:lambda} and~\ref{lm:stopprime}.
 \end{proof}
 Theorem~\ref{prop:stop} is a direct application of
 lemma~\ref{prop:lambda} and~\ref{lm:stopprime}.
 \end{proof}


@@ -348,7 +371,7 @@ lemma~\ref{prop:lambda} and~\ref{lm:stopprime}.
 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. 
 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 does not consider (balanced) Hamiltonian cycles, which 

 are more regular and more binding than this constraint.
 In this later context, we claim that the upper bound for the stopping time 
 should be reduced.
 are more regular and more binding than this constraint.
 In this later context, we claim that the upper bound for the stopping time 
 should be reduced.