## Notes of Sean Li’s lecture

Lower bounding distortion via metric invariants

Let ${X}$ and ${Y}$ be metric spaces. Recall that ${c_Y(X)}$ is the infimum of bi-Lipschtz distortions of maps ${X\rightarrow Y}$. Typically, ${Y}$ is a Banach space and ${X}$ is combinatorial (finite metric space). When ${Y=\ell_2}$, one denotes by

$\displaystyle \begin{array}{rcl} c_2(n)=\sup\{c_{\ell_2}(X)\,;\,X\textrm{ an }n\textrm{-point metric space}\}. \end{array}$

Theorem 1 (Enflo 1970) ${c_2(n)}$ tends to infinity as ${n}$ tends to infinity.

Enflo gave a lower bound of ${\sqrt{\log n}}$. This is not sharp. The sharp bound is ${\log n}$. The upper bound is due to Bourgain (1986) and the lower bound to London-Linial-Rabinovich (1994) – they used expanders.

1. Enflo’s lower bound for the Hamming cube

Enflo gave a sharp distortion lower bound, but for a suboptimal example, the Hamming cube ${D_n=\{0,1\}^n}$ with ${\ell_1}$ metric (= graph metric on 1-skeleton of cube).

Proposition 2 (Enflo) For any ${f:D_n\rightarrow\ell_2}$,

$\displaystyle \begin{array}{rcl} \sum_{diagonals\,xy}|f(x)-f(y)|^2\leq \sum_{edges\,uv}|f(u)-f(v)|^2. \end{array}$

Let us begin with a

Lemma 3 (Short diagonals) In ${\ell_2}$, for any quadrilateral, the sum of squares of diagonals is less than the sum of squares of sides.

$\displaystyle \begin{array}{rcl} |x-z|^2+|y-w|^2\leq|x-y|^2+|y-z|^2+|z-w|^2+|w-x|^2. \end{array}$

Proof. By summing over coordinates, it suffices to prove this for points on a line. There, it boils down to triangle inequality.

1.1. Proof of Proposition

By induction on ${n}$. Edges of ${D_n}$ split into ${E_0}$ and ${E_1}$ (edges of 2 opposite faces) and ${E'}$ (edges joining these faces). Let ${diag_0}$ and ${diag_1}$ be the diagonals of opposite faces (there are not diagonals of ${D_n}$). Induction hypothesis gives

$\displaystyle \begin{array}{rcl} \sum_{diag_0}|f(x)-f(y)|^2&\leq &\sum_{E_0}|f(u)-f(v)|^2,\\ \sum_{diag_1}|f(x)-f(y)|^2&\leq &\sum_{E_1}|f(u)-f(v)|^2. \end{array}$

For each diagonal ${uv\in diag_0}$, there is a unique (parallel) ${u'v'\in diag_1}$ such that ${uv'}$ and ${u'v}$ are diagonals of ${D_n}$. Then ${uu'}$ and ${vv'\in E'}$. The short diagonals lemma gives

$\displaystyle \begin{array}{rcl} |f(u)-f(v')|^2+|f(u')-f(v)|^2&\leq&|f(u)-f(v)|^2+|f(v)-f(v')|^2\\ &&+|f(v')-f(u')|^2+|f(u')-f(u)|^2. \end{array}$

Summing up such inequalities, on the left hand side, each diagonal of ${D_n}$ appears once. On the right hand side, each edge in ${E'}$ and each element of ${diag_0}$ and ${diag_1}$ occurs once. With induction hypothesis, we are done.

1.2. Proof of the sharp bound for Hamming cube

Assume ${f:D_n\rightarrow\ell_2}$ is distance non decreasing and ${D}$-Lipschitz. There are ${2^{n-1}}$ diagonals and ${n.2^{n-1}}$ edges. Diagonals in ${D_n}$ have length ${n}$ and edges have length 1, their images have lengths ${\leq D}$, therefore

$\displaystyle \begin{array}{rcl} 2^{n-1}n^2\leq n.2^{n-1}D^2, \end{array}$

hence ${D\geq\sqrt{n}}$.

1.3. Enflo type

The crucial property of ${\ell_2}$ used is expressed in the Proposition. We could have had a multiplicative constant in the Proposition, this would not have spoilt the result. This motivates the following

Definition 4 Let ${p>1}$. Say that metric space ${X}$ has Enflo type ${p}$ if there exists ${T>0}$ such that for all ${n}$ and all maps ${f:D_n\rightarrow X}$,

$\displaystyle \begin{array}{rcl} \sum_{diagonals\,xy}|f(x)-f(y)|^p\leq T\sum_{edges\,uv}|f(u)-f(v)|^p. \end{array}$

The best ${T:=T_p(X)}$ is called the Enflo type ${p}$ constant of ${X}$.

By construction, if ${X}$ has Enflo type ${p}$,

$\displaystyle \begin{array}{rcl} c_X(D_n)\geq T_p(X)^{-1}n^{1-\frac{1}{p}}. \end{array}$

Enflo type is a bi-Lispchitz invariant. The Enflo type ${p}$ constant is covariant under Lipschitz maps: if there exists a Lipschitz map ${f:X\rightarrow Y}$, then

$\displaystyle \begin{array}{rcl} T_p(X)\leq dist(f) T_p(Y). \end{array}$

This gives lower bounds by

$\displaystyle \begin{array}{rcl} dist(f)\geq\frac{T_p(X)}{T_p(Y)}. \end{array}$

2. Banach spaces

Now I give some background on Banach spaces that will lead us to more invariants.

Say an infinite dimensional Banach space ${X}$ has Rademacher type ${p}$ if for all ${n}$ and all point ${x_1,\ldots,x_n\in X}$,

$\displaystyle \begin{array}{rcl} \mathop{\mathbb E}_\epsilon(|\sum_{i=1}^n\epsilon_i x_i|^2)\leq T^p\sum_{i=1}^n|x_i|^p, \end{array}$

where ${\epsilon_i}$ are independent uniform ${\pm 1}$-valued random variables.

This notion was introduced independently by Jorgensen and by Maurey-Pisier. Note that ${p}$ has not be ${\leq 2}$ (the real line does not have Rademacher type ${p}$ for ${p>2}$). Rademacher type is Enflo type when restricting maps ${f:D_n\rightarrow X}$ to linear maps. Therefore Enflo type ${p}$ implies Rademacher type ${p}$. A weak converse holds, as the following surprising theorem states.

Theorem 5 (Pisier) For Banach spaces, Rademacher type ${p>1}$ ${\Rightarrow}$ Enflo type ${p'}$ for all ${p'.

Open question. Does Rademacher type ${p>1}$ ${\Rightarrow}$ Enflo type ${p}$ ? Known to hold for certain subclasses of Banach spaces. E.g. UMD spaces (Naor-Schechtman).

2.2. Finite representability

Locality. Rademacher type ${p}$ depends only on the geometry of finite dimensional linear subspaces. Such properties are called local.

Definition 6 Say a Banach space ${X}$ is (crudely) finitely representable in ${Y}$ if there exists ${K\geq }$ such that for every ${n}$-dimensional subspace ${Z\subset X}$, there exists a subspace ${Z'\subset Y}$ with Banach-Mazur distance from ${Z}$ at most ${K}$.

By definition, local properties are preserved by finite representability. The following theorem suggests that local properties may be expressed as bi-Lipschitz metric invariants.

Theorem 7 (Ribe) If two infinite dimensional Banach spaces ${X}$ and ${Y}$ are uniformly homeomorphic, then they are mutually finitely representable.

Suggests has to be taken seriously. It merely gives a guideline, only a small number of invariants are understood from this point of view. We shall give an example below.

2.3. ${p}$-convexity

Let ${p>1}$. Say a Banach space is ${p}$-convex if it is uniformly convex with a modulus of convexity ${\epsilon^p}$.

Proposition 8 ${X}$ is ${p}$-convex iff there exists ${K>0}$ such that for all ${x}$, ${y\in X}$,

$\displaystyle \begin{array}{rcl} |x|^p+|y|^p \geq 2|\frac{x-y}{2}|^p+2|\frac{x+y}{2K}|^p. \end{array}$

Example. ${L^p}$ is ${p}$-convex for ${p\geq 2}$, with bounded constants. For ${p<2}$, ${L^p}$ is 2-convex with 2-convexity constant ${O(\frac{1}{\sqrt{p-1}})}$.

Theorem 9 (Pisier) 1. Every uniformly convex Banach space can be renormed to be ${p}$-convex for some ${p>1}$.

2. Uniform convexity is a local property.

3. Markov type

If ${(X_t)_{t\in{\mathbb Z}}}$ is a Markov chain on some set ${\Omega}$, denote by ${(\tilde{X}_t(k))_{t\in{\mathbb Z}}}$ the Markov chain that coincides with ${X_t}$ until ${t\leq k}$, and then continues an independent life.

Say a metric space ${X}$ is Markov ${p}$-convex if there exists ${\pi>0}$ such that for every Markov chain on a set ${\Omega}$ and every map ${f:\Omega\rightarrow X}$,

$\displaystyle \begin{array}{rcl} \sum_{k=0}^{\infty}\sum_{t\in{\mathbb Z}}\frac{1}{2^{kp}}\mathop{\mathbb E}(d(f(X_t),f(\tilde{X}_t(t-2^k)))^p)\leq \pi^p\sum_{t\in{\mathbb Z}}\mathop{\mathbb E}(d(f(X_t),f(X_{t-1}))^p). \end{array}$

The best ${\pi}$ is called the Markov ${p}$-convexity constant ${\pi_p(X)}$ of ${X}$.

3.1. Trees

Let us start with an example which is not Markov convex. Let ${B_n}$ be the complete rooted binary tree of depth ${n}$. Let ${X_t}$ be the standard downward random walk on ${B_n}$ with probability ${1/2}$, stopped at leaves.

Proposition 10 There is a ${C(p)}$ such that the Markov ${p}$ convexity constant has to be

$\displaystyle \pi_p(B_n)\geq C\,(\log n)^{1/p}.$

It follows that the infinite rooted binary tree ${B_\infty}$ is not Markov convex. We see that trees are really extreme for Markov convexity: they allow maximal branching for Markov chains.

Proof. The right hand side (or denominator in ${\pi}$) equals ${n}$, since the distance ${d(X_t,X_{t+1})=1}$ by construction. To estimate the right hand side (or numerator in ${\pi}$), assume that ${0\leq t-2^k\leq t\leq n}$ and consider the independent chains ${(X_t)}$ and ${(\tilde{X}_t(t-2^k))}$ over ${2^k}$ time steps. With probability ${1/2}$, at time ${t-2^k}$, they jump to different nodes. If this happens, later on, they walk down the tree along different branches, therefore ${d(X_t,\tilde{X}_t(t-2^k))}$ increases by 2 at each step, leading to

$\displaystyle \begin{array}{rcl} d(X_t,\tilde{X}_t(t-2^k))\geq 2.2^k, \end{array}$

and thus

$\displaystyle \begin{array}{rcl} \mathop{\mathbb E}(d(X_t,\tilde{X}_t(t-2^k))^p)\geq \frac{1}{2}(2.2^k)^p\sim 2^{kp}. \end{array}$

Summing over ${t}$ from ${2^k}$ to ${n}$ gives

$\displaystyle \begin{array}{rcl} \sum_{t=2^k}^{n}\mathop{\mathbb E}(d(X_t,\tilde{X}_t(t-2^k))^p)\geq (n-2^k)2^{kp}. \end{array}$

Summing over ${k}$ from 0 to ${\log_2 n}$ gives

$\displaystyle \begin{array}{rcl} \sum_{k=0}^{\log n}\sum_{t=2^k}^{n}\mathop{\mathbb E}(d(X_t,\tilde{X}_t(t-2^k))^p)\geq n\log n, \end{array}$

whence ${\pi^p\geq\log n}$.

Theorem 11 If metric space ${X}$ is Markov ${p}$-convex, then

$\displaystyle \begin{array}{rcl} c_X(B_n)\geq C(\log n)^{1/p}. \end{array}$

Indeed, the Markov ${p}$-convexity constant is Lipschitz covariant.

3.2. The case of Banach spaces

Theorem 12 An infinite dimensional Banach space can be renormed to be ${p}$-convex iff it is Markov ${p}$-convex.

I prove only one implication. Let ${X}$ be a ${p}$-convex Banach space, i.e.

$\displaystyle \begin{array}{rcl} |x|^p+|y|^p \geq 2|\frac{x-y}{2}|^p+2|\frac{x+y}{2K}|^p. \end{array}$

One first proves a consequence, the fork inequality:

$\displaystyle \begin{array}{rcl} \frac{1}{2^{p-1}}(|x-w|^p+|x-z|^p)+\frac{1}{4^{p-1}K}|z-w|^p \leq 2|y-x|^p+|y-w|^p+|z-y|^p. \end{array}$

Note that only the tip of the fork (${z}$ and ${w}$) involves the ${p}$-convexity constant, the other coefficients take specific values.

Apply the fork inequality to the images by ${f}$ of ${X_{t-2^{k+1}}}$, ${X_{t-2^{k}}}$, ${X_t}$, ${\tilde{X}_t(t-2^k)}$ and take expectation. By independence,…

3.3. Question

Does one have a metric invariant for uniform smoothness ?