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\begin{document}

\lecture{10}{March 14, 2002}{Dan Spielman}{Fernando Ord\'o\~nez}

\section{Smoothed model for bandwidth minimization}

In the last lecture we presented Turner's analysis of the Cuthill-Mckee heuristic 
for bandwidth minimization.  The problem of bandwidth minimization of a graph is 
to find an ordering of the nodes of the graph which has the minimum possible 
bandwidth, where the bandwidth is defined as the largest difference in ordering 
among nodes that are linked. 

This problem corresponds to finding the order of rows and columns that minimizes
the bandwidth for the vertex adjacency matrix.  This is a square, symmetric, sparse
matrix.
The problem is studied for a graph $G_b(n,p)$ on $n$ nodes, such that 
neighbors up to b nodes away are connected with probability $p$.  This graph can 
be represented by the matrix 
$M\in \{0,1\}^{n\times n}$ such that 
$$ M_{i,j} = 
\left\{ \begin{array}{ll}
1 & |i-j| \le b, {\rm ~with~probability~} p \\
0 & |i-j| \le b, {\rm ~with~probability~} 1-p \\
0 & |i-j| > b \ .
\end{array}\right. $$


In the next section, we will 
see that Turner's analysis can be directly translated to smooth analysis.

Let $G_b(n)$ be the family of graphs on $n$ nodes such that if $|i-j|>b$ then $(i,j)
\not\in E$.  That is the family of graphs which have bandwidth at most $b$.  Let 
$A$ be an algorithm, such that $A(G)$ is the quality of the output of algorithm $A$.

\subsection{ Blum and Spencer, 1995}

Blum and Spencer 
  suggested to analyze an algorithm in a semi-random model, where the inputs 
distribution is partially controlled by an adversary.  This model is represented by:
$$ \max_{G \in G_b(n)} E_{P \in G_b(n,p)} \left[ A(G \oplus P) \right]  \ ,
$$
an adversary chooses $G \in G_b(n)$, in other words, the adversary chooses for each 
pair of vertices that are 
potential neighbors ($|i-j|\le b$), whether he joins them or not.  This choice can 
be flipped with probability $p$.  This is the addition of the random perturbation
$P\in G_b(n,p)$, where the addition allows to add or remove edges.
This is a natural smoothed model for the problem of bandwidth minimization.


Turner's theorem aplies with the same proof to this model, although
this does not become obvious until we strengthen the model.

Blum and Spencer suggested a stronger model, known as the
 monotone adversary model, in which the graph is drawn ramdomly, $P \in 
G_b(n,p)$ and then an adversary is allowed alter the random decisions in a monotonic
manner, that is only allowed to add edges.

How this is related to the Blum and Spencer model?  We first note that
the semi-random model is weaker than a 
semi random model that only allowes random addition 
of edges, i.e.
$$
\max_{G \in G_b(n)} E_{P \in G_b(n,p)} \left[ A(G \oplus P) \right]
 \leq  \max_{G \in G_b(n)} E_{P \in G_b(n,p)} \left[ A(G \cup P) \right]  \ .
$$
This is true because the
 random deletion of edges, can be done by the adversary
in selecting the graph $G$.

We now note that the monotone adversary model can be obtained by
  from the semi-random model in which edges are only added by
  reversing the orders of the quantifiers.
That is,
$$
 \max_{G \in G_b(n)} E_{P \in G_b(n,p)} \left[ A(G \cup P) \right] 
 \le E_{P \in G_b(n,p)}  \max_{G\in G_b(n)} \left[ A(G \cup P) \right]  \ ,
$$
where the left-hand term gives the performance in the monotone
  adversary model.
Feige and Krauthgamer point out that Turner's analysis applies directly
  to the monotone adversary model.
Thus, Turner's analysis provides
  lower bounds on the resulting bandwidth of the graph and performace guarantees 
  for level algorithms to find the bandwidth in the monotone
  adversary model.
By the arguments above, this analysis can be applied to the semi-random
  adversary or smoothed model as well.


\section{Graph Bisection}

The problem is: for a given graph $G$, 
to split the nodes in two groups evenly (in reality don't need to cut evenly), 
cutting as few edges as possible

{\definition {\bf (A first algorithm) }
\begin{enumerate}
\item Start with any bisection.
\item Greedy: find nodes in each side with the highest degree to the other 
      side and swap.
\item Repeat 2.
\end{enumerate}
}

This First algorithm works well but tends to get stuck at local minimum.
The following algorithm incorporates a method that allows to escape  a local 
minimum.

{\definition {\bf (Kernighan-Lin algorithm) }
\begin{enumerate}
\item Begin with any bisection
\item Do Greedy $n/2$ times
\begin{itemize}
\item never reusing nodes
\item do even if hurts
\end{itemize}
Note that after doing all $n/2$ swaps, all nodes have changed side.
 
\item Recall best configuration
\item Repeat 2.
\end{enumerate}
}

This algorithm, which is of the class of ``tabu search" methods (we keep a list 
of tabu nodes, which can not be used), works very well in practice.
But there are no theoretical results on this.

A way to check the improvement of these algorithms, is to construct lower bounds 
to the optimal number of bisection, by for example a Linear Programming relaxation 
formulation.

{\definition {\bf (Simulated Annealing algorithm, Jerrum - Sorkin made the analysis)}

$\bullet$ Begin with a bisection \newline
$\bullet$ Pick random pairs \newline
$\bullet$ Flip is improvement is above a threshold
}

Now we turn to a special type of graph, which allows for a nicer analysis of 
some bisection algorithms.  We will consider graphs made from the Planted 
Bisection Model.

{\definition {\bf Planted Bisection Model}

Is a graph constructed by separating the nodes in two sets, defining 
edges within a group independently with probability $p$, and between groups 
independently with probability $q$.  For $q < p$.
 
}


\begin{figure}[!ht]
\begin{center}
\scalebox{0.5}{\includegraphics{plantedbisection.eps}}
\end{center}
\end{figure}


It can be shown that if $q$ is sufficiently $< p$, then the original is the 
only optimal bisection.

There is a paper by R. B. Boppana, which analyzes this problem with the average 
case analysis, when $p-q \sim \frac{1}{\sqrt{n}}$.  

Condon and Karp, 2000 provide a linear-time algorithm, that if $p-q \ge 
\frac{1}{n^{.5-\epsilon}}$, for some $\epsilon >0$, then the algorithm finds 
the optimal partition with high probability.

McSherry considers the matrix of probabilities of the edges
$$
A = \left(\begin{array}{cccccc}
p & \cdots & p & q & \cdots & q \\
\vdots & & \vdots & \vdots & & \vdots \\
p & \cdots & p & q & \cdots & q \\
q & \cdots & q & p & \cdots & p \\
\vdots & & \vdots & \vdots & & \vdots \\
q & \cdots & q & p & \cdots & p \\
\end{array}\right)
$$
Let $B$ be the adjecency matrix of the graph obtained by the planted partition.
Then think of $B$ as a rounding of $A$.  Therefore the eigenvalues of these matrices are 
related.

Matrix $A$ has rank 2, and by inspection we note that the largest eigenvalue
is $\frac{n}{2}(p+q)$ with eigenvector the vector of all ones, that is
\[
w_1^t = ( 1, \cdots, 1 ) \ ,\]
and the second largest eigenvalue is $\frac{n}{2}(p-q)$ with eigenvector 
\[
w_2^t = (1, \cdots, 1, -1, \cdots, -1) \ . \]
All other eigenvalues of matrix $A$ are equal to 0.

{\definition {\bf (McSherry's algorithm)}

$\bullet$ Take 2 eigenvectors of $B$ with the largest eigenvalues. \newline
$\bullet$ Use second eigenvector to partition the graph.
}

For the analysis of McSherry's algorithm, we need the following two theorems 

{\theorem For $A$ is a symmetric matrix and $E = A-B$.
If $\lambda_1, \lambda_2, \cdots, \lambda_n$ 
are eignevalues of $A$, with eigenvectors $v_1, v_2, \cdots, v_n$ and $w_1, w_2, 
\cdots, w_n$ eigenvectors of $B$, with $\theta_i = {\rm angle}(v_i,w_i)$.  Then
$$\theta_i \sim \frac{1}{2}\sin (2\theta_i) \le \frac{\|E\|}{{\rm gap}(i,A)} \ ,$$
where ${\rm gap}(i,A) = \min_{j\ne i}|\lambda_i - \lambda_j|$.
} 

{\theorem (Alon-Krivelevich-Vu)

In this case $\|E\|\le 4p\sqrt{n}$ with probability $1 - \frac{1}{\exp (n)}$.
}

In the analysis of McSherry's algorithm, note that ${\rm gap}(2,A) = \frac{n}{2}(p-q)$
which implies that $\theta_2 \leq \frac{8p}{\sqrt{n}(p-q)}$. 
Note that $v_2^t = \frac{1}{\sqrt{n}}(1,\cdots, 1,-1,\cdots, -1)$ and that $w_2^t$ is 
such that $(w_2)_i > 0$ for one group and $(w_2)_i < 0$ for the other group.
If we missclassify $k$ entries, then $\|v_2 - w_2\| \ge \sqrt{\frac{k}{n}}$, since 
for each missclassified entries there is least a contrbution of $\frac{1}{\sqrt{n}}$ 
in $v_2-w_2$.

\begin{figure}[!ht]
\begin{center}
\scalebox{0.5}{\includegraphics{geoargument.eps}}
\end{center}
\end{figure}

By the geometric argument above we note that $\|v_2-w_2\| \le 2 \sin (\theta_2/2) \sim 
\frac{1}{2}\sin(2\theta_2)$.  From the previous bounds, 
$$
\sqrt{\frac{k}{n}}\le \|v_2-w_2\| \le \frac{1}{2}\sin(2\theta_2) \le \frac{8p}{\sqrt{n}(p-q)} \ , $$
this implies that 
$$ k \le \left( \frac{8p}{p-q}\right)^2 \ . $$
This is a bound, independent of $n$, of the number of missclassified entries.

\section*{Ideas on Random Graphs}

Graphs in practice are not planted bisection.  What can be said for other 
types of graphs?
\newpage

For example Fully triangulated planar graphs. 
\begin{figure}[!ht]
\begin{center}
\scalebox{0.5}{\includegraphics{fullytry.eps}}
\end{center}
\end{figure}

Consider perturbations where: \newline
$\bullet$ delete a random edge \newline
$\bullet$ add the other diagonal in the square formed \newline

What can be proven with this perturbation model?


\section*{Property-Preserving Perturbations}

Define a function $f$, from the space of graphs considered, that for a graph
$G$, returns in $f(G)$ a vector where each coordinate is a property of interest
(max cut, coloring number, graph is planar or not, etc).

Some coordinates can be in the reals (the max flow of the graph), while other 
coordinates can be binary, the graph has a property or does not.

Can define different perturbation models.  For 
$$P \leftarrow G(n,p) \hspace{1em} {\rm ~analyze~}\hspace{1em} f(G+P) \ ,$$
or
$$P \leftarrow G(n,p) \hspace{1em} {\rm ~consider~perturbations~that~}
\hspace{1em} f(G+P)=f(G) \ ,$$
or
$$P \leftarrow G(n,p) \hspace{1em} {\rm ~consider~perturbations~that~}
\hspace{1em} \|f(G+P)-f(G)\|\le \epsilon \ .$$


paper on the subject distributed in the next class.

\end{document}

