Isoperimetric inequality and OMT: Difference between revisions

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===Proof of the anisotropic isoperimetric inequality===
===Proof of the anisotropic isoperimetric inequality===
The proof of that inequality really follows with similar arguments as in the classical case. Indeed, as in the proof of the classical isoperimetric inequality, we get a transport map <math>T</math> which now pushes forward the density <math>\chi_E/|E|\lambda^N</math> to the density <math>\chi_K/|K|\lambda^N</math>.
The proof of that inequality really follows with similar arguments as in the classical case.
Indeed, as in the proof of the classical isoperimetric inequality, we get a transport map <math>T</math> which now pushes forward the density <math>\chi_E/|E|\lambda^N</math> to the density <math>\chi_K/|K|\lambda^N</math>.

Revision as of 04:18, 8 March 2022

The classic isoperimetric inequality

A very interesting application of optimal transport is a proof of the isoperimetric inequality. The first proof with an OMT argument is due to Gromov and the main tool is the Knothe's map. [1]. This proof is based on an idea by Knothe [2]. The classic isoperimetric inequality in states that the round ball has the minimal (n-1)-dimensional volume of the boundary among all the domains with a given fixed volume. This is equivalent to say that every set has a larger perimeter than the ball with the same volume. I will present this proof following the exposition given in chapter two in [3]. The usual way to state this is the following:

Here is the volume of the unit ball in . The idea of the proof is to construct a map T called Knothe transport and use it between the two densities: , the inequality will follow from some symmetries and consideration on the Jacobian determinant of this map.

The Knothe's transport

For this part I will follow the first chapter in [4]. In some sense, it can be seen as a multidimension generalization of monotone rearrangement. Take any two measures and define

,

This maps may not be well defined, since at some points the measures may have a delta. For the purpose of this exposition we will assume that those functions are well defined, for the precise definition and convention to include the mass of the deltas in the integral. It follow easily from the definition that those maps are not decreasing. We now define . We are now ready to define the following monotone rearrangement map :

.

Note that this map is also not decreasing. In the case that our first density has no deltas then it can be shown that is indeed a transport map (Theorem 1.4.7). To prove the isoperimetric inequality we only use the fact that this map is a transport map and its nondecreasing but we also know, from Benier's theorem, that is is also a optimal transport map. We now move to the two dimensional case: the key ingredient to the Knothe transport map is what is known as the disintegration theorem 1.4.10: Given and let where is the projection on the first component of : , Then there exist an uncountable family of probability measures such that for any continuous and bounded we have that:

. The disintegration of the measure is often also denoted as . Now we are ready to construct the Knothe map.

Fix any two absolutely continuous measures in : and , define and . Note that using these notation we can write:

and . Applying the monotone rearrangement we get a map that satisfies , We want to send the disintegration of at to the disintegration of $\nu$ at the point , in symbols let .

The Knothe map is now defined as:

.

It is not hard to check that this map is indeed transports to (Theorem 1.4.13) of . By monotonicity of the monotone rearrangement, assuming that the map T is differentiable, we can say that:

.

We can iterate the same construction and obtain a Knothe map on , the recursive nature of this definition is well described in [5], the formal construction can be found here [6].

Proof of the classic isoperimetric inequality

We now present three key properties of the Knothe map from to , this is Proposition 1.5.2 Let now be a bounded set with smooth boundary, its Lebesgue measure and the probability measures and , where is the characteristic function of the set : is identically one in and zero elsewhere, and is the unit ball in . Denote with be a Knothe map from to . First by just noticing that if then we can conclude:

Thank to a change of variable and using the fact that the jacobian map is upper triangular and its diagonal entries are non negative (similar to the two dimensional case) it can be shown that:

in

Now since the matrix is upper triangular, it's very easy to compute the determinant as the product of the diagonal entries, we then get an estimate on the divergence of :

.

We are now ready to prove the classic isoperimetric inequality, this is Theorem 1.5.1 of Denote by the outer unit normal of and by the surface element of . We can now write thanks to the first property of the Knothe map:

As a straightforward application of Stokes theorem together with our lower bound for the divergence we get:

We can now conclude with our explicit expression for the Jacobian of in ,

The isoperimetric inequality in spaces

There is a more general version of the classic isoperimetric inequality for Riemannian manifolds and even more in general for spaces. In the Riemannian setting this result is proved first by Simon Brendle in Corollary 1.3 of[7], the proof is based on PDE techniques, notably on the so called ABP-method (Aleksandrov-Bakelman-Pucci estimates, a standard reference for this would be [8]).

Since OMT-theory has been worked very well in a much more general setting of metric measure spaces, we can construct the so called spaces where is a lower bound for the Ricci curvature and is an upper bound for the dimension (Wiki article on Ricci Curvature and OMT [1]). This has been worked out independently by Lott and Villani: [9] and Sturm in two very beautiful papers: [10] and [11]. The idea is to express the curvature in terms of the convexity of a specific entropy function defined on the space. It is also well known that even in the case of non negative curvature, i.e. without an additional condition, a general sharp isoperimetric inequality cannot hold! This can be found in two papers from Milman: [12] and [13]. To my knowledge, one of the most general result in this direction is Theorem 1.1 in [14]. In this case the condition is the so called Euclidean volume growth condition at infinity. If we denote with , by an generalization of the classical Bishop-Gromov volume growth inequality, theorem 2.3 of[15], it follows that the map is non increasing on for any . The Euclidean volume growth at infinity is a the positivity of the following limit:

. It can be shown that this is independent of the choice of . In the Riemannian contest this condition rises naturally thanks since the Bishop-Gromov relative volume comparison theorem assurers that the limit exists and that .

We are now ready to state Theorem 1.1 : Let be a metric measure space satisfying the condition for some together with the Euclidean volume growth assumptions, for any bounded Borel subsets the following holds:

.

Here is the exterior Minkowski content of that if we have more regularity can be interpreted just as the (n-1)-dimensional surface area of .

Anisotropic Isoperimetric inequality

Recall that the isoperimetric inequality is given by This can be generalized to the so-called as follows. For any convex and bounded subset define a weight function being

       

Then for any open and smooth set we define the anisotropic perimeter

       

We are now in the position to state the anisotropic isoperimetric inequality

       

Taking a look at this inequality and the isoperimetric inequality, one naturally expects that for , the unit ball in one recovers the classical isoperimetric inequality. This is indeed the case. Notice that if then for any we have that so that

      

as desired. Notice, though, that the perimeter is also defined for arbitrary measurable sets of finite volume. To define the anisotropic perimeter for such a large class of sets though, involves a little bit more background in geometric measure theory and functions of bounded variations which is why we commit the discussion here.

Proof of the anisotropic isoperimetric inequality

The proof of that inequality really follows with similar arguments as in the classical case. Indeed, as in the proof of the classical isoperimetric inequality, we get a transport map which now pushes forward the density to the density .

  1. V.D. Milman, G. Schechtman, Asymptotic Theory of Finite-Dimensional Normed Spaces, with an appendix by M. Gromov, Lecture notes in Mathematics, vol. 1200 (Springer, Berlin, 1986)
  2. Herbert Knothe. "Contributions to the theory of convex bodies.." Michigan Math. J. 4 (1) 39 - 52, 1957
  3. F. Santambrogio. Optimal Transport for Applied Mathematicians. Calculus of Variations, PDEs and Modeling (2015)
  4. A. Figalli, F. Glaudo An Invitation to Optimal Transport, Wasserstein Distances, and Gradient Flows
  5. Luigi Ambrosio, Elia Brué, Daniele Semola - Lectures on Optimal Transport Remark 5.10 pag. 52
  6. F. Santambrogio. Optimal Transport for Applied Mathematicians. Calculus of Variations, PDEs and Modeling (2015) pag. 67-72
  7. S. Brendle, Sobolev inequalities in manifolds with nonnegative curvature (2020) pag. 2
  8. X. Cabr´e, X. Ros-Oton, J. Serra, Sharp isoperimetric inequalities via the ABP method. J. Eur. Math. Soc. 18 (2016)
  9. J. Lott, C. Villani, Ricci curvature for metric measure spaces via optimal transport. Ann. of Math. (2) 169 (3) (2009)
  10. K.-T. Sturm, On the geometry of metric measure spaces. I, Acta Math. 196 (1) (2006), 65–131.
  11. K.-T. Sturm, On the geometry of metric measure spaces. II, Acta Math. 196 (1) (2006), 133–177.
  12. E. Milman, Sharp isoperimetric inequalities and model spaces for the curvature-dimension-diameter condition. J. Eur. Math. Soc. 17 (5) (2015)
  13. E. Milman, Beyond traditional curvature-dimension I: new model spaces for isoperimetric and concentration inequalities in negative dimension. Trans. Amer. Math. Soc. 369 (2017), no. 5, 3605–3637
  14. Z. M. Balogh, A.Kristály Sharp geometric inequalities in spaces with nonnegative Ricci curvature and Euclidean volume growth (2021)
  15. E. Milman, Beyond traditional curvature-dimension I: new model spaces for isoperimetric and concentration inequalities in negative dimension. Trans. Amer. Math. Soc. 369 (2017), no. 5, 3605–3637