# Diameters of Sections and Coverings of Convex alexandr/papers/ of Sections and Coverings

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Diameters of Sections and Coverings ofConvex Bodies

A. E. Litvak A. Pajor N. Tomczak-Jaegermann

Abstract

We study the diameters of sections of convex bodies in RN de-termined by a random N n matrix , either as kernels of or asimages of . Entries of are independent random variables satisfyingsome boundedness conditions, and typical examples are matrices withGaussian or Bernoulli random variables. We show that if a symmetricconvex body K in RN has one well bounded k-codimensional section,then for any m > ck random sections of K of codimension m are alsowell bounded, where c 1 is an absolute constant. It is noteworthythat in the Gaussian case, when determines randomness in sense ofthe Haar measure on the Grassmann manifold, we can take c = 1.

0 Introduction

Geometric Functional Analysis and the theory of finite dimensional normedspaces, traditionally study the structure of subspaces and quotient spacesof finite dimensional normed spaces, and operators acting on them. A par-allel study in the general setting of convex bodies and in the language ofAsymptotic Convex Geometry is concerned with the asymptotic propertiesof sections and projections of N -dimensional convex bodies, when N grows toinfinity. Then n-dimensional subspaces (sections) or quotients (projections)

Keywords: symmetric convex bodies, sections, random sections, diameters, coveringnumbers, Gelfand numbers.

This author holds the Canada Research Chair in Geometric Analysis.

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are often constructed using various random methods. Randomness may beunderstood in sense of the rotation invariant probability measure on theGrassmann manifold of n-dimensional subspaces of RN , or, for different pur-poses, it may carry some specific structure, such as in the case of subspacesgenerated by n vectors with random 1 coordinates in RN . These exam-ples can be seen as particular cases of a general setting when randomness isdetermined by rectangular matrices with random variable entries.

Consider a random N n matrix acting as a mapping : Rn RN(with N n). We can adopt two points of view. Subspaces of RN may bedefined by n linear forms, with say, 1 coefficients, in which case we look forker of a random 1 matrix . Alternatively, they may be generated byn vectors in RN , with say, 1 coordinates, and then we look for the imageof Rn under a 1 matrix . When the matrix is Gaussian, it is rotationinvariant from both sides, and then the induced measures on the linear sub-spaces, either ker or (Rn), are both the Haar measures on the Grassmannmanifolds, but in general these measures may be different. Studies of randomsubspaces defined by linear forms have been quite extensive in the Gaussiancase; and were developed in [MiP2] in the case of 1 coefficients and in amore general setting. The second approach, which is a dual point of view, istechnically very different and appeared recently in [LPRT]. (One importantdifference between the present paper and the work in [LPRT], though, isthat here we are more interested in properties of the subspaces (Rn) thanin properties of itself.)

Let us recall a well known example. In the studies of Euclidean subspacesof `N1 , Kashin ([Ka]) proved that for every proportion 0 < < 1, there existsubspaces of RN of dimension n = [N ] on which the `N1 norm, defined for x =(xi) RN by x1 =

N1 |xi|, and the Euclidean norm are equivalent (with

constants independent on the dimension). In fact, random n-dimensionalsubspaces of RN (in sense of the Haar measure on the Grassman manifoldGN,n) are nearly Euclidean in that sense. Kashins theorem was reprovedby Szarek [Sz] by a different argument, which also worked in a more generalcase of spaces with so-called bounded volume ratio [SzT] (cf. also [P]). If oneasks for an additional structure on the subspace, for instance for subspacesdefined by linear forms with random 1 coefficients, an analogous fact wasproved in [MiP2]. For spaces generated by vectors with 1 coordinates, theproblem was left open for some time. For `N1 , it was proved in [JS] (see [S])that one such a subspace exists, and in [LPRTV1], [LPRTV2] that subspacesgenerated by random vectors with 1 coordinates satisfy the conclusion of

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Kashins result.This example raises several questions. If a convex body K contains one

k-codimensional section with a well bounded (Euclidean) diameter, does arandom section of a slightly larger codimension k also has a well boundeddiameter? Randomness may be determined by the Haar measure on theGrassmann manifold, or by vectors with random 1 coordinates, or even byrandom matrices from a more general class. Can one take arbitrarily closeto 1, in any of the above cases? Let us note that, since subspaces determinedby random matrices from some class carry an additional structure associatedto the class, therefore it is not even clear that the existence of one sectionof K with good control of the diameter yields the existence of even just onesection of K determined by a matrix from the class and admitting a control ofthe diameter. In this paper we solve in positive the former series of questions,and we answer the latter question in the rotation invariant case.

This type of general phenomenon, of a deterministic information implyinga randomized one, has been first observed in [MiS] in the context of whatis called the global form of Dvoretzkys theorem. In a local contextsimilar questions on the Grassmann manifold were considered in [LT], [MT1],[MT2]. In particular, the affirmative answer to the first question of thepreceding paragraph immediately follows from one of the main results of[LT] (Theorem 3.2), and a version of this question for dimensions rather thancodimensions was proved in [MT2] (Proposition 2.3). Independently, thesequestions, again for the Grassmann manifold, were introduced and answeredin [GMT] and [V], however these proofs could not give close to 1. Theapproach in [GMT] and [V] is based on a recent Gromovs theorem on thesphere the isoperimetric inequality for waists. In contrast, our approachis completely different and allows for a natural generalization to randomstructures determined by random matrices described above.

The paper is organized as follows. In Section 1, we give our main newtechnical tool on covering numbers, Theorem 1.3 and its Corollary 1.6, whichwill be important for the next sections. In Section 2, we study subspaces de-fined by linear forms, and we begin by illustrating this approach by the rota-tion invariant case. We give an estimate of the diameter of random sections,with respect to the Haar measure on the Grassmann manifold, assuming aninformation on the diameter of one section of higher dimension. Our mainresult, Theorem 2.4, states the following:Let 1 k < m < N be integers and a > 0, and set := m/k. Let K RN be

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an arbitrary symmetric convex body such that there exists a k-codimensionalsubspace E0 with diam (E0 K) 2a. Then the subset of the Grassmannmanifold of all m-codimensional subspaces E RN satisfying

diam (E K) a(C

N/m)1+1/(1)

,

has Haar measure larger than 12 em/2 (here C > 1 is a universal constant).The fact that there is almost no loss in the dimension, that is, = m/k

can be chosen as close to 1 as we wish, resolves the question left open by allthe previous proofs.

At the end of Section 2 we observe that our proof also works for subspacesgiven as the kernels of random m N matrices from a class of matricesendowed with probability satisfying two abstract conditions. In particular,it gives a version of Theorem 2.4 for kernels of 1 random matrices. Thisgeneral approach is further elaborated in the subsequent section.

In Section 3, we study the problem for sections defined by images by arandom matrix , when carries some additional structure. We discuss gen-eral conditions on the set of random matrices that imply a similar statementas above; it turns out that, for example, the class of matrices with indepen-dent subgaussian entries satisfies these conditions. In Theorem 3.4 we proveestimates of the diameter of random sections given as images under andwith respect to our new probability measure on the set of matrices startingfrom an information on the diameter of one section of a higher dimension.

Let us finish by recalling basic notations used throughout the paper. If Xis a normed space, we denote its unit ball by BX . We denote the Euclideannorm on Rn by | | and the Euclidean unit ball by Bn2 . By a convex bodyK Rn we mean a convex compact set with the non-empty interior. Wecall K symmetric if it is centrally symmetric.

1 Results on covering numbers

In this section, we prove a result on covering numbers valid for operatorsbetween normed spaces. A setting from convex geometry is discussed atthe end of the section. We start by recalling a few classical notations foroperators.

If X and Y are normed spaces, an operator u : X Y always meansa bounded linear operator, with the operator norm denoted by u. For

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k = 1, 2, . . ., we denote by ak(u), ck(u) and dk(u) the approximation, Gelfand,and Kolmogorov numbers, respectively. Namely,

ak(u) = inf u v,

where the infimum runs over all operators v : X Y with rank v < k; then

ck(u) = inf u|E,

where the infimum runs over all subspaces E X of codim E < k; and

dk(u) = inf QF u,

where the infimum runs over all subspaces F Y of dim F < k and QF :Y Y/F denotes the quotient map.

Let X be a linear space and K, L be subsets of X. We recall that thecovering number N(K, L) is defined as the minimal number N such thatthere exist vectors x1, ..., xN in X satisfying

K N

i=1

(xi + L). (1.1)

Let > 0, a set of points x1, ..., xN in X satisfying K Ni=1(xi + L) iscalled an -net of K with