\documentclass[12pt]{article}

\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{latexsym}
\usepackage{enumerate}
\usepackage{amstext}
%\usepackage{amsthm}

\usepackage{amssymb}
\usepackage{epsfig}
\usepackage{array}
\usepackage{tabularx}
\usepackage{color}
\usepackage{epsfig}
\usepackage{epic}
\usepackage{fancyhdr}


\newtheorem{define}{Definition}
%\newcommand{\Z}{{\mathbb{Z}}}
%\usepackage{psfig}

\oddsidemargin=0.15in
\evensidemargin=0.15in
\topmargin=-.5in
\textheight=9in
\textwidth=6.25in



\begin{document}
\input{preamble.tex}

\renewcommand{\Pr}{\mathrm{\bf Pr}}
\renewcommand{\E}{\mathrm{\bf E}}
\newcommand{\V}{\mathrm{\bf Var}}
\newcommand{\I}{\mathrm{\bf I}}
\newcommand{\cov}{\mathrm{\bf Cov}}



%{lecture number}{lecture date}{Ronitt Rubinfeld}{Your name}
\lecture{12}{March 20, 2006}{Ronitt Rubinfeld}{Petar Maymounkov}

%%%% body goes in here %%%%

\subsection*{Generating Random Spamming Trees with Markov Chains (Part I)}

This lecture continues the analysis of the algorithm for
generating uniform samples from the set of spanning trees of a graph.
The algorithm is defined as follows:
\begin{itemize}
\item The state of the Markov chain is a rooted spanning tree (of the
input graph G) with directed edges, such that all edges point toward
the root.
\item Begin from an arbitrary rooted tree.  If the graph did not have
self-loops, add them.
\item Repeat several times:
\begin{itemize}
\item Let the root of the current state's tree be $v$. Pick a random
edge $(v,w)$ uniformly at random.
\item Add $(v,w)$ to the tree. This creates a cycle.
\item Delete the single edge outgoing from $w$, and
set $w$ as the new root.
\end{itemize}
\end{itemize}

\begin{fact} Two basic properties of this Markov chain are:
\begin{enumerate}
\item The chain is ergodic, because:
\begin{enumerate}
\item It is irreducible (left as an exercise)
\item It is aperiodic (With the self-loops added)
\end{enumerate}
\item The out-degree of each Markov state is equal to the degree of
the corresponding tree node in the input graph, plus 1 to account for
the self-loops.
\item The in-degree of each Markov state is equal to its out-degree.
\end{enumerate}
\end{fact}

Since the in- and out-degrees of the Markov chain are the same, we
have that the stationary distribution $\tilde{\pi}$ is such that:
\begin{align*}
\tilde{\pi}_s = \frac{1 + \text{Degree of root at state $s$}}{z}
\end{align*}
here $z$ is a normalizing factor:
\[
z =(2 m + 2 n) \cdot (\text{\# of unrooted spanning trees})
\]
where $m$ stands for the number of edges in the input graph, and $n$
stands for the number of vertices.

Next we need to verify that when the stationary distribution is
reached, each unrooted tree is equally likely to be reached:
\begin{align*}
\Pr[\text{output unrooted tree $T$}] &= 
\sum_{v\in V} \Pr[\text{reach state $(T,v)$}] \\
&= \sum_{v\in V} \frac{\deg (v) + 1}{z} \\
&= \frac{2m + 2n}{z} \\
&= \frac{1}{\text{\# unrooted trees}}
\end{align*}

Finally, we need to verify that the Markov chain is ``rapid
mixing'', i.e. that after a small number of Markov transitions
(algorithm steps) starting from an arbitrary state the resulting
distribution approximates the stationary. To show this, we need to
take a digression into ``coupling techniques for proving rapid
convergence''.

\subsection*{Coupling for Markov Chains}

For notation, we are going to use $(x,y)\sim w$ and $(x,y)\in_R w$
interchangably to mean that the random variables $(x,y)$ are
distributed according to the joint distribution $w$.

\begin{definition}
For distributions $u$ on $\Omega$ and $v$ on $\Omega$, a distribution
$w$ on the (joint) event space $\Omega^2$ is a
\textbf{coupling} if:
\begin{align*}
\forall x \quad \sum_y w(x,y) &= u(x) \\
\forall y \quad \sum_x w(x,y) &= v(y)
\end{align*}
i.e. the marginal distributions on $x$ and $y$ equal $u$ and $v$,
respectively.
\end{definition}

Additionally, to measure the ``closeness'' of two distributions, we
use the following:

\begin{definition}The \textbf{total variation distance} between 
$u$ and $v$ is:
\begin{align*}
d_{TV}(u,v) &= \frac{1}{2} ||u - v||_1 \\
      &= \frac{1}{2}\sum_{x\in\Omega} |u(x)-v(x)| \\
      &= \max_{s\subseteq\Omega} \big\{u(s)-v(s)\big\}
\end{align*}
\end{definition}

\begin{lemma}[Coupling Lemma]
For any $u$ and $v$ both on a finite event space $\Omega$,
a coupling $w$, and $(x,y)\sim w$, we have that:
\[
d_{TV}(u,v) \leq \Pr[x\neq y]
\]
\end{lemma}

\begin{proof}
For every $z\in\Omega$, we have $w(z,z)\leq \min \{u(z),v(z)\}$, 
hence for $(x,y)\sim w$:
\begin{align*}
\Pr[x = y] &= \sum_z w(z,z) \\
 &\leq \sum_z \min \{u(z), v(z)\}
\end{align*}
And therefore:
\begin{align*}
\Pr[x \neq y] &\geq 1-\sum_z \min \{u(z), v(z)\} \\
  &= \sum_z \Big(u(z) - \min \{u(z),v(z)\}\Big) \\
  &=\sum_{\underset{v(z)<u(z)}{z}} \Big(u(z) - v(z)\Big) \\
  &=\max_S\{u(s)-v(s)\} \\
  &= d_{TV} (u,v)
\end{align*}
\end{proof}

Now consider two random walks of $t$ steps $X_t$ and $Y_t$, starting
at states (or distributions over states) $a$ and $b$ respectively, and
using the same transition matrix. Also define the joint distribution
of $X_t$ and $Y_t$ to be such that when they first meet they stay
together.

More specifically now, consider a coupling $(X_t, Y_t)$, where $b$ is
chosen according to the Markov chain's stationary distribution
$\tilde{\pi}$.  The coupling lemma implies that:
\begin{align*}
d_{TV}(X_t,\tilde{\pi}) \leq 
\Pr[X_t \neq Y_t | X_0 = a \wedge Y_0\sim\tilde{\pi}]
\end{align*}
Here we have abused notation by using $X_t$ to also refer to the
distribution of the random variable $X_t$. This inequality suggests
that our goal will be to find an appropriate coupling, for which the
two distributions $X_t$ and $Y_t=\tilde{\pi}$ become close for small
values of $t$. More formally:

Let $T$ be the expected first coupling time (when the two random
processes arrive at the same state), then:

\begin{theorem}
\[
d_{TV}(X_t,\tilde{\pi}) \leq \frac{T}{t}
\]
\end{theorem}

\begin{proof}
Let $\tau$ be the random variable denoting the time when the
two random walks meet for the first time. Then:
\begin{align*}
d_{TV}(X_t,\tilde{\pi}) &\leq \Pr [X_t \neq Y_t] 
\quad&\text{by Coupling Lemma}\\
 &= \Pr[\tau > t] \\
 &\leq T / t \quad&\text{by Markov Inequality}
\end{align*}
\end{proof}

\subsection*{Generating Random Spamming Trees with Markov Chains (Part II)}

Now, we define the coupling process for our problem at hand:
\begin{enumerate}
\item Run the processes $X^t$ and $Y^t$ independently until just the
tree roots collide
\item Force the roots to move identically hereafter
\end{enumerate}

We have:

\begin{align*}
\E[\text{coupling time}] &= \E[\text{time for roots to collide}]
+ \E[\text{time until trees identical}|\text{same roots}]
\end{align*}

The first term (on the right) is left as an exercise. The second term
equals the cover time on the input graph, because once the coupling
has walked through all vertices of the input graph, the two trees are
sure to be identical. This is because once the two trees share a root,
a step from root $u$ to root $v$ ensures that edge $(u,v)$ will be in
both trees.

\subsection*{Conductance Preliminaries}

Let $G=(V,E)$ be given, and let $(S,\bar{S})$ be a cut, i.e.
$S\subseteq V$, $\bar{S}=V\backslash S$. Then we'll make a first
attempt at defining the ``conductance'' of the cut as:
\[
\phi(S,\bar{S}) = \frac{|E_{S,\bar{S}}|}{|E_S|}
\]
Where:
\begin{align*}
E_{S,\bar{S}}&=\{(u,v)|(u,v)\in E \wedge ((u,v) \in
S\times\bar{S} \vee (v,u) \in S\times\bar{S} )\} \\
%
E_S &= \{(u,v)| (u,v)\in E \wedge (u\in S \vee v\in S)\}
\end{align*}
 Then
the conductance of the graph $G$ might be defined as:
\[
\phi_G = \min_{S:|S|=|V|/2} \phi (S,\bar{S})
\]
In the next lecture, we'll look at a more appropriate definition.

\end{document}

