# tcs math – some mathematics of theoretical computer science

## July 8, 2010

### Majorizing measures: Gaussian tools

Filed under: lecture, Math — Tags: , , , — James Lee @ 2:33 am

In order to prove that the chaining argument is tight, we will need some additional properties of Gaussian processes. For the chaining upper bound, we used a series of union bounds specified by a tree structure. As a first step in producing a good lower bound, we will look at a way in which the union bound is tight.

Theorem 1 (Sudakov inequality) For some constant $C > 0$, the following holds. Let ${\{X_t\}_{t \in T}}$ be a Gaussian process such that for every distinct ${s,t \in T}$, we have ${d(s,t) \geq \alpha}$. Then,

$\displaystyle \mathop{\mathbb E} \sup_{t \in T} X_t \geq C \alpha \sqrt{\log |T|}.$

The claim is an elementary calculation for a sequence of i.i.d. ${N(0,1)}$ random variables ${g_1, g_2, \ldots, g_n}$ (i.e. ${\mathop{\mathbb E} \sup_i g_i \geq C\sqrt{\log n}}$). We will reduce the general case to this one using Slepian’s comparison lemma.

Lemma 2 (Slepian’s Lemma) Let ${\{X_t\}_{t \in T}}$ and ${\{Y_t\}_{t \in T}}$ be two Gaussian processes such that for all ${s,t \in T}$,

$\displaystyle \mathop{\mathbb E} \,|X_s - X_t|^2 \geq \mathop{\mathbb E} \,|Y_s - Y_t|^2. \ \ \ \ \ (1)$

Then ${\mathop{\mathbb E} \sup_{t \in T} X_t \geq \mathop{\mathbb E} \sup_{t \in T} Y_t}$.

There is a fairly elementary proof of Slepian’s Lemma (see, e.g. the Ledoux-Talagrand book), if one is satisfied with the weaker conclusion ${2\, \mathop{\mathbb E}\,|X_s-X_t|^2 \geq \mathop{\mathbb E}\,|Y_s-Y_t|^2}$, which suffices for our purposes.

To see that Lemma 2 yields Theorem 1, take a family ${\{X_t\}_{t \in T}}$ with ${d(s,t) \geq \alpha}$ for all ${s \neq t \in T}$ and consider the associated variables ${Y_t = \frac{\alpha}{\sqrt{2}} g_t}$ where ${\{g_t\}_{t \in T}}$ is a family of i.i.d. ${N(0,1)}$ random variables. It is straightforward to verify that (1) holds, hence by the lemma, ${\mathop{\mathbb E} \sup_{t \in T} X_t \geq \frac{\alpha}{\sqrt{2}} \mathop{\mathbb E} \sup_{t \in T} g_t}$, and the result follows from the i.i.d. case.

The Sudakov inequality gives us “one level” of a lower bound; the following strengthening will allow us to use it recursively. If we have a Gaussian process ${\{X_t\}_{t \in T}}$ and ${A \subseteq T}$, we will use the notation

$\displaystyle g(A) = \mathop{\mathbb E} \sup_{t \in A} X_t.$

For ${t \in T}$ and ${R \geq 0}$, we also use the notation

$\displaystyle B(t,R) = \{ s \in T : d(s,t) \leq R \}.$

Here is the main theorem of this post; its statement is all we will require for our proof of the majorizing measures theorem:

Theorem 3 For some constants $C > 0$ and ${r > 1}$, the following holds. Suppose ${\{X_t\}_{t \in T}}$ is a Gaussian process, and let ${t_1, t_2, \ldots, t_m \in T}$ be such that ${d(t_i,t_j) \geq \alpha}$ for ${i \neq j}$. Then,

$\displaystyle g(T) \geq C \alpha \sqrt{\log m} + \min_{i=1,2,\ldots,m} g(B(t_i, \alpha/r)).$

The proof of the preceding theorem relies on the a strong concentration property for Gaussian processes. First, we recall the classical isoperimetric inequality for Gaussian space (see, for instance, (2.9) here).
We remind the reader that for a function ${F : \mathbb R^n \rightarrow \mathbb R}$,

$\displaystyle \|F\|_{\mathrm{Lip}} = \sup_{x \neq y \in \mathbb R^n} \frac{|F(x)-F(y)|}{\|x-y\|}.$

Theorem 4 Let ${F : \mathbb R^n \rightarrow \mathbb R}$, and let ${\mu = \int F \,d\gamma_n}$, where ${\gamma_n}$ is the standard ${n}$-dimensional Gaussian measure. Then,

$\displaystyle \gamma_n\left(x \in \mathbb R^n : |F(x)-\mu| > \lambda\right) \leq 2\,\exp\left(\frac{-\lambda^2}{2 \|F\|_{\mathrm{Lip}}}\right). \ \ \ \ \ (2)$

Using this, we can prove the following remarkable fact.

Theorem 5 Let ${\{X_t\}_{t \in T}}$ be a Gaussian process, then

$\displaystyle \mathbb P\left(\left|\sup_{t \in T} X_t - \mathop{\mathbb E} \sup_{t \in T} X_t\right| > \lambda\right) \leq 2\,\exp\left(\frac{-\lambda^2}{2 \sup_{t \in T} \mathop{\mathbb E}(X_t^2)}\right). \ \ \ \ \ (3)$

A notable aspect of this statement is that only the maximum variance affects the concentration, not the number of random variables. We now prove Theorem 5 using Theorem 4.

Proof: We will prove it in the case ${|T|=n}$, but of course our bound is independent of ${n}$. The idea is that given a Gaussian process ${\{X_1, X_2, \ldots, X_n\}}$, we can write

$\displaystyle X_i = a_{i1} \,g_1 + a_{i2}\, g_2 + \cdots + a_{in}\, g_n,$

for ${i=1,2,\ldots, n}$, where ${\{g_i\}_{i=1}^n}$ are standard i.i.d. normals, and the matrix ${A=(a_{i,j})}$ is a matrix of real coefficients. In this case, if ${g = (g_1, g_2, \ldots, g_n)}$ is a standard ${n}$-dimensional Gaussian, then the vector ${Ag}$ is distributed as ${(X_1, X_2, \ldots, X_n)}$.

If we put ${F(x)=\max \{ (Ax)_i : i=1,\ldots,n\}}$, then Theorem 4 yields (3) as long as ${\|F\|_{\mathrm{Lip}} \leq \max_i \sqrt{\mathop{\mathbb E}(X_i^2)}}$. It is easy to see that

$\displaystyle \|F\|_{\mathrm{Lip}} = \|A\|_{2 \rightarrow \infty} = \sup_{\|x\|_2 = 1} \|A x\|_{\infty}.$

But ${\|A\|_{2 \rightarrow \infty}}$ is just the maximum ${\ell_2}$ norm of any row of ${A}$, and the ${\ell_2}$ norm of row ${i}$ is

$\displaystyle \sqrt{\sum_{j=1}^n a_{ij}^2} = \sqrt{\mathop{\mathbb E}(X_i^2)}.$

$\Box$

Using this theorem, we are ready to prove Theorem 3. I will only give a sketch here, but filling in the details is not too difficult.

Assume that the conditions of Theorem 3 hold. Pick an arbitrary ${t_0 \in T}$, and recall that we can write

$\displaystyle g(T) = \mathop{\mathbb E} \sup_{t \in T} X_t = \mathop{\mathbb E} \sup_{t \in T} (X_t - X_{t_0})$

since our gaussians are centered.

Now, by Theorem 1,

$\displaystyle \mathop{\mathbb E} \max_{i=1,\ldots,m} \left(X_{t_i} - X_{t_0}\right) \geq C \alpha \sqrt{\log m}.$

Suppose that ${t_1}$ achieves this. By definition,

$\displaystyle g(B(t_1, \alpha/r)) = \mathop{\mathbb E} \sup_{t \in B(t_1, \alpha/r)} \left(X_t - X_{t_1}\right),$

so we could hope that for some ${t \in B(t_1,\alpha/r)}$, we simultaneously have ${X_t - X_{t_1} \geq g(B(t_1,\alpha/r))}$, yielding

$\displaystyle X_t - X_{t_0} = (X_t - X_{t_1}) + (X_{t_1} - X_{t_0}) \geq C\alpha \sqrt{\log m} + g(B(t_1, \alpha/r)). \ \ \ \ \ (4)$

The problem, of course, is that the events we are discussing are not independent.

This is where Theorem 5 comes in. For any ${i}$, all the variances of the variables ${\{X_t - X_{t_i} : t \in B(t_i,\alpha/r)\}}$ are bounded by ${d(t,t_i)^2 \leq (\alpha/r)^2}$. This implies that we can choose a constant ${c_0 > 0}$ such that

$\displaystyle \mathbb P\left(\left|\sup_{t \in B(t_i,\alpha/r)} X_t - g(B(t_i, \alpha/r))\right| > c_0 (\alpha/r) \sqrt{\log m}\right) \leq m^{-2}.$

So, in fact, we can expect that none of the ${m}$ random variables ${\sup_{t \in B(t_i,\alpha/r)} X_t}$ will deviate from its expected value by more than ${c_0 (\alpha/r) \sqrt{\log m}}$. Which means we can (morally) replace (4) by

$\displaystyle \begin{array}{rl} X_t - X_{t_0} &= (X_t - X_{t_1}) + (X_{t_1} - X_{t_0}) \\ &\geq C\alpha \sqrt{\log m} + g(B(t_1, \alpha/r)) - c_0 (\alpha/r) \sqrt{\log m}. \end{array}$

But now by choosing ${r = 2 C c_0}$, the error term is absorbed.