# tcs math – some mathematics of theoretical computer science

## June 15, 2010

### The generic chaining

Filed under: lecture, Math — Tags: , , , — James Lee @ 11:27 am

In the last post, we considered a Gaussian process ${\{X_t\}_{t \in T}}$ and were trying to find upper bounds on the quantity ${\mathop{\mathbb E}\sup_{t \in T} X_t}$. We saw that one could hope to improve over the union bound by clustering the points and then taking mini union bounds in each cluster.

Hierarchical clustering

To specify a clustering, we’ll take a sequence of progressively finer approximations to our set ${T}$. First, recall that we fixed ${t_0 \in T}$, and we have used the observation that ${\mathop{\mathbb E}\sup_{t \in T} X_t = \mathop{\mathbb E}\sup_{t \in T} (X_t-X_{t_0})}$.

Now, assume that ${T}$ is finite. Write ${T_0 = \{t_0\}}$, and consider a sequence of subsets ${\{T_n\}}$ such that ${T_0 \subseteq T_1 \subseteq T_2 \subseteq \cdots \subseteq T}$. We will assume that for some large enough ${m}$, we have ${T_n = T}$ for ${n \geq m}$. For every ${n \geq 0}$, let ${\pi_n : T \rightarrow T_n}$ denote a “closest point map” which sends $t \in T$ to the closest point in $T_n$.

The main point is that we can now write, for any ${t \in T}$,

$\displaystyle X_t - X_{t_0} = \sum_{n \geq 1} X_{\pi_n(t)} - X_{\pi_{n-1}(t)}. \ \ \ \ \ (1)$

This decomposition is where the term “chaining” arises, and now the idea is to bound the probability that ${X_t - X_{t_0}}$ is large in terms of the segments in the chain.

What should ${T_n}$ look like?

One question that arises is how we should think about choosing the approximations ${T_n}$. We are trading off two measures of quality: The denser ${T_n}$ is in the set ${T}$ (or, more precisely, in the set ${T_{n-1}}$) the smaller the variances of the segments ${X_{\pi_n(t)}-X_{\pi_{n-1}(t)}}$ will be. On the other hand, the larger ${T_n}$ is, the more segments we’ll have to take a union bound over.

So far, we haven’t used any property of our random variables except for the fact that they are centered. To make a more informed decision about how to choose the sets ${\{T_n\}}$, let’s recall the classical Gaussian concentration bound.

Lemma 1 For every ${s,t \in T}$ and ${\lambda > 0}$,

$\displaystyle \mathop{\mathbb P}(X_s - X_t > \lambda) \leq \exp\left(-\frac{\lambda^2}{2\, d(s,t)^2}\right). \ \ \ \ \ (2)$

This should look familiar: ${X_s-X_t}$ is a mean-zero Gaussian with variance ${d(s,t)^2}$.

Now, a first instinct might be to choose the sets ${T_n}$ to be progressively denser in ${T}$. In this case, a natural choice would be to insist on something like ${T_n}$ being a ${2^{-n}}$-net in ${T}$. If one continues down this path in the right way, a similar theory would develop. We’re going to take a different route and consider the other side of the tradeoff.

Instead of insisting that ${T_n}$ has a certain level of accuracy, we’ll insist that ${T_n}$ is at most a certain size. Should we require ${|T_n| \leq n}$ or ${|T_n| \leq 2^n}$, or use some other function? To figure out the right bound, we look at (2). Suppose that ${g_1, g_2, \ldots, g_m}$ are i.i.d. ${N(0,1)}$ random variables. In that case, applying (2) and a union bound, we see that to achieve

$\displaystyle \mathop{\mathbb P}(\exists i : g_i > B) \leq m \mathop{\mathbb P}(g_1 > B) < 1,$

we need to select ${B \asymp \sqrt{\log m}}$. If we look instead at ${m^2}$ points instead of ${m}$ points, the bound grows to ${\sqrt{2 \log m}}$. Thus we can generally square the number of points before the union bound has to pay a constant factor increase. This suggests that the right scaling is something like ${|T_{n+1}| = |T_n|^2}$. So we’ll require that ${|T_n| \leq 2^{2^n}}$ for all ${n \geq 1}$.

The generic chaining

This leads us to the generic chaining bound, due to Fernique (though the formulation we state here is from Talagrand).

Theorem 2 Let ${\{X_t\}_{t \in T}}$ be a Gaussian process, and let ${T_0 \subseteq T_1 \subseteq \cdots \subseteq T}$ be a sequence of subsets such that ${|T_0|=1}$ and ${|T_n| \leq 2^{2^{n}}}$ for ${n \geq 1}$. Then,

$\displaystyle \mathop{\mathbb E}\sup_{t \in T} X_t \leq O(1) \sup_{t \in T} \sum_{n \geq 0} 2^{n/2} d(t, T_n). \ \ \ \ \ (3)$

Proof: As before, let ${\pi_n : T \rightarrow T_n}$ denote the closest point map and let ${T_0 = \{t_0\}}$. Using (2), for any ${n \geq 1}$, ${t \in T}$, and ${u > 0}$, we have

$\displaystyle \mathop{\mathbb P}\left(|X_{\pi_n(t)} - X_{\pi_{n-1}(t)}| > u 2^{n/2} d(\pi_n(t),\pi_{n-1}(t))\right) \leq \exp\left(-\frac{u^2}{2} 2^n\right).$

Now, the number of pairs ${(\pi_n(t),\pi_{n-1}(t))}$ can be bounded by ${|T_n| \cdot |T_{n-1}| \leq 2^{2^{n+1}}}$, so we have

$\displaystyle \mathop{\mathbb P}\left(\exists t : |X_{\pi_n(t)} - X_{\pi_{n-1}(t)}| > u 2^{n/2} d(\pi_n(t),\pi_{n-1}(t))\right) \leq 2^{2^{n+1}} \exp\left(-\frac{u^2}{2} 2^n\right). \ \ \ \ \ (4)$

If we define the event

$\displaystyle \Omega_u = \left\{ \forall n \geq 1, t \in T : |X_{\pi_n(t)} - X_{\pi_{n-1}(t)}| \leq u 2^{n/2} d(\pi_n(t),\pi_{n-1}(t))\right\},$

then summing (4) yields,

$\displaystyle \mathop{\mathbb P}(\overline{\Omega_u}) \leq \sum_{n \geq 1} 2^{2^{n+1}} \exp\left(-\frac{u^2}{2} 2^n\right) \leq O(1)\, e^{-u^2} \ \ \ \ \ (5)$

for ${u \geq 4}$, since we get geometrically decreasing summands.

Write

$\displaystyle S = \sup_{t \in T} \sum_{n \geq 1} 2^{n/2} d(\pi_n(t), \pi_{n-1}(t)).$

Note that if ${\Omega_u}$ occurs, then ${\sup_{t \in T} (X_t - X_{t_0}) \leq uS}$. Thus (5) implies that for $u \geq 4$,

$\displaystyle \mathop{\mathbb P}(\sup_{t \in T} X_t - X_{t_0} > uS) \leq O(1) \, e^{-u^2},$

which implies that

$\displaystyle \mathop{\mathbb E} \sup_{t \in T} X_t \leq O(S) \leq O(1) \sup_{t \in T} \sum_{n \geq 1} 2^{n/2} d(\pi_n(t), \pi_{n-1}(t)). \ \ \ \ \ (6)$

Finally, by the triangle inequality,

$\displaystyle d(\pi_n(t), \pi_{n-1}(t)) \leq d(t, T_n) + d(t, T_{n-1}) \leq 2\,d(t,T_{n-1}).$

Plugging this into (6) recovers (3). $\Box$

Theorem 1.2 gives us a fairly natural way to upper bound the expected supremum using a hierarchical clustering of ${T}$. Rather amazingly, as we’ll see in the next post, this upper bound is tight. Talagrand’s majorizing measure theorem states that if we take the best choice of ${\{T_n\}}$ in Theorem 1.2, then the upper bound in (3) is within a constant factor of ${\mathop{\mathbb E} \sup_{t \in T} X_t}$.