# How Good is the Leighton-Rao Relaxation?

Continuing our seemingly never-ending series on approximations to edge expansion, we come to the question of how good is the Leighton-Rao relaxation.

Given a graph $G=(V,E)$, we are trying to approximate the sparsest cut problem (which, in turn, approximates the edge expansion problem within a factor of two) defined as

$\displaystyle \phi(G) := \min_{S\subseteq V} \frac {edges(S,V-S)} {\frac 1n |S| \cdot |V-S|}$

And we have seen that an equivalent characterization of sparsest cut is:

$(1) \ \ \ \displaystyle \phi(G) = \min_{x\in {\mathbb R}^V} \frac{ \sum_{i,j} A(i,j) | x_i - x_j | } {\frac 1n \sum_{i,j} |x_i - x_j | }$

In the Leighton-Rao relaxation, we have

$(2) \ \ \ \displaystyle LR(G) := \min_{d\in {\cal M}(V)} \frac{ \sum_{i,j} A(i,j) d(i,j) }{\frac 1n \sum_{i,j} d(i,j)}$

where ${\cal M}(V)$ is the set of all semi-metrics $d: V \times V \rightarrow {\mathbb R}$, that is, of all functions such that $d(i,j) = d(j,i) \geq 0$, $d(i,i)=0$, and that obey the “triangle inequality”

$d(i,j) \leq d(i,k) + d(k,j)$

for all $i,j,k \in V$.

This is clearly a relaxation of the sparsest cut problem, because for every $x \in {\mathbb R}^V$ the “distances” $|x_i - x_j|$ define indeed a semi-metric over $V$.

How bad can the relaxation be? Take a family of regular constant-degree expander graphs, that is graphs where $\phi$ is a constant and the degree $k$ is a constant independent of $n=|V|$. Define $d(i,j)$ to be the shortest path distance between $i$ and $j$ in the graph. This is clearly a semi-metric, and we have

$\displaystyle \sum_{i,j} A(i,j) d(i,j) = kn = O(n)$

because along every edge the distance is precisely one, and

$\displaystyle \sum_{i,j} d(i,j) = \Omega (n^2 \log_k n)$

because, for every vertex $i$, at most $k^t$ other vertices can be within
distance $t$, and so at least half the vertices are at distance $\Omega( \log_k n)$.

So we have

$\displaystyle LR(G) = O \left( \frac 1 {\log n} \right)$

even though $\phi(G) = \Omega(1)$, so the error in the approximation can be as bad as a factor of $\log n$. We shall prove that this is as bad as it gets.

Our task is then: given a feasible solution to (2), that is, a distance function defined on the vertices of $G$, find a feasible solution to sparsest cut whose cost is as small as possible, and no more than a $O(\log n)$ factor off from the cost of the solution to (2) that we started from. Instead of directly looking for a cut, we shall look for a solution to (1), which is a more flexible formulation.

The general strategy will be, given $d$, to find a distribution $X$ over vectors $x \in {\mathbb R}^V$ such that for every two vertices $i,j$ we have

$(3) \ \ \ \displaystyle \Omega\left( \frac {1}{\log n} \right) \cdot d(i,j) \leq {\mathbb E} | X_i - X_j | \leq d(i,j)$

Suppose that we start from a solution to (2) of cost $c$, that is, we have a semimetric $d(\cdot,\cdot)$ such that

$\displaystyle \sum_{i,j} A_{i,j} d(i,j) = c \cdot \frac 1n \sum_{i,j} d(i,j)$

Then our distribution $X$ is such that

$\displaystyle {\mathbb E} \sum_{i,j} A_{i,j} |X_i - X_j| \leq \sum_{i,j} A_{i,j} d(i,j)$

and

$\displaystyle \sum_{i,j} d(i,j) \leq O \left( {\log n} \right) {\mathbb E} \sum_{i,j}|X_i - X_j|$

so

$\displaystyle {\mathbb E} \left[ \sum_{i,j} A_{i,j} |X_i - X_j| - O(c\log n) \cdot \frac 1n \sum_{i,j} |X_i - X_j| \right] \leq 0$

and there must exist a vector $x$ in the support of the distribution $X$ such that

$\displaystyle \sum_{i,j} A_{i,j} |x_i - x_j| - O(c\log n) \cdot \frac 1n \sum_{i,j} |x_i - x_j| \leq 0$

and now we are done because the cost of $x$ in (1) is at most $O(\log n)$ times the optimum of the Leighton-Rao relaxation.

Though this works, it seems an overkill to require condition (3) for all pairs of vertices. It seems sufficient to require

${\mathbb E} |X_i-X_j| \leq d(i,j)$

only for edges, and to require

$d(i,j) \leq O(\log n) \cdot {\mathbb E} |X_i-X_j|$

only “on average” over all pairs of vertices. It can be shown, however, that if one wants to round a generalization of the Leighton-Rao relaxation to sparsest cut with “non-uniform demands,” then any rounding algorithm can be turned into a rounding algorithm that satisfies (3).

So how do we start from an arbitrary metric and come up with a probabilistic linear order of the vertices so that their distances in the original metric are well approximated in the linear ordering? We will describe a method of Bourgain, whose applicability in this setting is due Linial, London and Rabinovich.

The starting point is to see that if $A$ is a set of vertices, and we define

$\displaystyle X_i := d(A,i) := \min_{a\in A} d(a,i)$

then, no matter how we choose $A$, we always have

$|X_i - X_j | = |d(A,i) - d(A,j) | \leq d(i,j)$

[Hint: use the triangle inequality to see that $d(A,j) \leq d(A,i) + d(i,j)$.]

This means that “all we have to do” is to find a distribution over sets $A$ such that for every $i,j$ we have

$(3) \ \ \ \displaystyle {\mathbb E} | d(A,i) - d(A,j) | \geq \Omega \left( \frac 1 {\log n} \right) \cdot d(i,j)$

If you want to make it sound more high-brow, you may call such a distribution a Frechet embedding of $d$ into $\ell_1$. Constructing such a distribution will be the subject of the next post.