Affine Restriction estimates imply Affine Isoperimetric inequalities

One thing I absolutely love about harmonic analysis is that it really has something interesting to say about nearly every other field of Analysis. Today’s example is exactly of this kind: I will show how a Fourier Restriction estimate can say something about Affine Geometry. This was first noted by Carbery and Ziesler (see below for references).

1. Affine Isoperimetric Inequality

Recall the Affine Invariant Surface Measure that we have defined in a previous post. Given a hypersurface \Sigma \subset \mathbb{R}^d sufficiently smooth to have a well-defined Gaussian curvature \kappa_{\Sigma}(\xi) (where \xi ranges over \Sigma ) and with surface measure denoted by d\sigma_{\Sigma} , we can define the Affine Invariant Surface measure as the weighted surface measure

\displaystyle d\Omega_{\Sigma}(\xi) := |\kappa_{\Sigma}(\xi)|^{1/(d+1)} \, d\sigma_{\Sigma}(\xi);

this measure has the property of being invariant under the action of SL(\mathbb{R}^d) – hence the name. Here invariant means that if \varphi is an equi-affine map (thus volume preserving) then

\displaystyle \Omega_{\varphi(\Sigma)}(\varphi(E)) = \Omega_{\Sigma}(E)

for any measurable E \subseteq \Sigma .
The Affine Invariant Surface measure can be used to formulate a very interesting result in Affine Differential Geometry – an inequality of isoperimetric type. Let K \subset \mathbb{R}^d be a convex body – say, centred in the origin and symmetric with respect to it, i.e. K = - K . We denote by \partial K the boundary of the convex body K and we can assume for the sake of the argument that \partial K is sufficiently smooth – for example, piecewise C^2-regular, so that the Gaussian curvature is defined at every point except maybe a \mathcal{H}^{d-1} -null set. Then the Affine Isoperimetric Inequality says that (with \Omega = \Omega_{\partial K} )

\displaystyle \boxed{ \Omega(\partial K)^{d+1} \lesssim |K|^{d-1}.  } \ \ \ \ \ \ \ (\dagger)


Notice that the inequality is invariant with respect to the action of SL(\mathbb{R}^d) indeed – thanks to the fact that d\Omega is. Observe also the curious fact that this inequality goes in the opposite direction with respect to the better known Isoperimetric Inequality of Geometric Measure Theory! Indeed, the latter says (let’s say in the usual \mathbb{R}^d ) that (a power of) the volume of a measurable set is controlled by (a power of) the perimeter of the set; more precisely, for any measurable E \subset \mathbb{R}^d

\displaystyle |E|^{d-1} \lesssim P(E)^d,

where P(E) denotes the perimeter1 of E – in case E = K a symmetric convex body as above we would have P(K) = \sigma(\partial K) . But in the affine context the “affine perimeter” is \Omega(\partial K) and is controlled by the volume instead of viceversa. This makes perfect sense: if K is taken to be a cube Q then \kappa_{\partial Q} = 0 and so the “affine perimeter” cannot control anything. Notice also that the power of the perimeter is d for the standard isoperimetric inequality and it is instead d+1 for the affine isoperimetric inequality. Informally speaking, this is related to the fact that the affine perimeter is measuring curvature too instead of just area.
So, the inequality should actually be called something like “Affine anti-Isoperimetric inequality” to better reflect this, but I don’t get to choose the names.

The inequality above is formulated for convex bodies since those are the most relevant objects for Affine Geometry. However, below we will see that Harmonic Analysis provides a sweeping generalisation of the inequality to arbitrary hypersurfaces that are not necessarily boundaries of convex bodies. Before showing this generalisation, we need to introduce Affine Fourier restriction estimates, which we do in the next section.

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Carbery's proof of the Stein-Tomas theorem

Writing the article on Bourgain’s proof of the spherical maximal function theorem I suddenly recalled another interesting proof that uses a trick very similar to that of Bourgain – and apparently directly inspired from it. Recall that the “trick” consists of the following fact: if we consider only characteristic functions as our inputs, then we can split the operator in two, estimate these parts each in a different Lebesgue space, and at the end we can combine the estimates into an estimate in a single L^p space by optimising in some parameter. The end result looks as if we had done “interpolation”, except that we are “interpolating” between distinct estimates for distinct operators!

The proof I am going to talk about today is a very simple proof given by Tony Carbery of the well-known Stein-Tomas restriction theorem. The reason I want to present it is that I think it is nice to see different incarnations of a single idea, especially if applied to very distinct situations. I will not spend much time discussing restriction because there is plenty of material available on the subject and I want to concentrate on the idea alone. If you are already familiar with the Stein-Tomas theorem you will certainly appreciate Carbery’s proof.

As you might recall, the Stein-Tomas theorem says that if R denotes the Fourier restriction operator of the sphere \mathbb{S}^{d-1} (but of course everything that follows extends trivially to arbitrary positively-curved compact hypersurfaces), that is

\displaystyle Rf = \widehat{f} \,\big|_{\mathbb{S}^{d-1}}

(defined initially on Schwartz functions), then

Stein-Tomas theorem: R satisfies the a-priori inequality

\displaystyle \|Rf\|_{L^2(\mathbb{S}^{d-1},d\sigma)} \lesssim_p \|f\|_{L^p(\mathbb{R}^d)} \ \ \ \ \ \ (1)


for all exponents {p} such that 1 \leq p \leq \frac{2(d+1)}{d+3} (and this is sharp, by the Knapp example).

There are a number of proofs of such statement; originally it was proven by Tomas for every exponent except the endpoint, and then Stein combined the proof of Tomas with his complex interpolation method to obtain the endpoint too (and this is still one of the finest examples of the power of the method around).
Carbery’s proof obtains the restricted endpoint inequality directly, and therefore obtains inequality (1) for all exponents 1 \leq p < \frac{2(d+1)}{d+3} by interpolation of Lorentz spaces with the p=1 case (which is a trivial consequence of the Hausdorff-Young inequality).

In other words, Carbery proves that for any (Borel) measurable set {E} one has

\displaystyle \|R \mathbf{1}_{E}\|_{L^2(\mathbb{S}^{d-1},d\sigma)} \lesssim |E|^{\frac{d+3}{2(d+1)}}, \ \ \ \ \ \ (2)

where the LHS is clearly the L^{2(d+1)/(d+3)} norm of the characteristic function \mathbf{1}_E . Notice that we could write the inequality equivalently as \|\widehat{\mathbf{1}_{E}}\|_{L^2(\mathbb{S}^{d-1},d\sigma)} \lesssim |E|^{\frac{d+3}{2(d+1)}} .

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Kovač’s solution of the maximal Fourier restriction problem

About 2 years ago, Müller Ricci and Wright published a paper that opened a new line of investigation in the field of Fourier restriction: that is, the study of the pointwise meaning of the Fourier restriction operators. Here is an account of a recent contribution to this problem that largely sorts it out.

1. Maximal Fourier Restriction
Recall that, given a smooth submanifold \Sigma of \mathbb{R}^d with surface measure d\sigma , the restriction operator {R} is defined (initially) for Schwartz functions as

\displaystyle f \mapsto Rf:= \widehat{f}\Big|_{\Sigma};

it is only after having proven an a-priori estimate such as \|Rf\|_{L^q(\Sigma,d\sigma)} \lesssim \|f\|_{L^p(\mathbb{R}^d)} that we can extend {R} to an operator over the whole of L^p(\mathbb{R}^d), by density of the Schwartz functions. However, it is no longer clear what the relationship is between this new operator that has been operator-theoretically extended and the original operator that had a clear pointwise definition. In particular, a non-trivial question to ask is whether for d\sigma  -a.e. point \xi \in \Sigma we have

\displaystyle \lim_{r \to 0} \frac{1}{|B(0,r)|} \int_{\eta \in B(0,r)} |\widehat{f}(\xi - \eta)| d\eta = \widehat{f}(\xi), \ \ \ \ \ (1)


where B(0,r) is the ball of radius {r} and center {0} . Observe that the Lebesgue differentiation theorem already tells us that for a.e. element of \mathbb{R}^d in the Lebesgue sense the above holds; but the submanifold \Sigma has Lebesgue measure zero, and therefore the differentiation theorem cannot give us any information. In this sense, the question above is about the structure of the set of the Lebesgue points of \widehat{f} and can be reformulated as:

Q: can the complement of the set of Lebesgue points of \widehat{f} contain a copy of the manifold \Sigma ?

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