arXiv subjects

Apostolos Giannopoulos

Publications and source records attributed to Apostolos Giannopoulos.

At least 19 recordsLinked to original sources

arXiv2026

Geometry of the subgaussian body of an isotropic convex body

For a centered convex body $K\subset\mathbb{R}^n$, let $\Psi_2(K)$ denote the symmetric convex body whose support function is given by the $\psi_2$-norms of linear functionals on $K$. The recent solution of Milman's problem on the existence of subgaussian directions by Letwin and Mikulincer naturally motivates the study of the geometry of this body. We prove that $\Psi_2(K)$ has bounded volume ratio with respect to the $L_2$-centroid body $Z_2(K)$. In the isotropic case, we also obtain sharp estimates for its mean width and the volume radii of its orthogonal projections, and derive consequences for the existence of subgaussian orthonormal bases. In particular, we construct orthonormal bases with quantitatively controlled subgaussian constants for every isotropic convex body.

math.MG↗

arXiv2026

Functional perimeter and the dimensional Brunn-Minkowski inequality for log-concave measures

This paper is dedicated to two geometric problems associated to log-concave measures on $\mathbb{R}^n$. First, we study the dimensional Brunn-Minkowski inequality for even log-concave probability measures $\mu$ on $\mathbb{R}^n$ via an analytic approach based on diffusion operators and gradient estimates. We prove that for every pair of symmetric convex sets $K,L$ in $\mathbb{R}^n$ and every $\lambda\in(0,1)$, $$\mu(\lambda K+(1-\lambda)L)^{c_n} \geq \lambda \mu(K)^{c_n}+(1-\lambda)\mu(L)^{c_n},$$ where $c_n\geq c/n^3\ln n$ for some absolute constant $c>0$. Secondly, we study the maximal perimeter $\Gamma(\mu)$ of an isotropic log-concave measure $\mu$, without symmetry assumptions. We prove that $$\Gamma_n = \sup\{\Gamma(\mu): \ \mu \ \mbox{is an isotropic log-concave measure on } \mathbb{R}^n \} \approx n.$$ A key ingredient in both our proofs is a bound due to Eldan and Klartag (2008), which states that $$\int_{\mathbb{R}^n} |\nabla\psi|\,d\mu \leq Cn$$ for every isotropic log-concave probability measure $\mu$ on $\mathbb{R}^n$ with density $e^{-\psi}$. We also present further applications of this estimate to projections of log-concave functions projections, moment and surface area measures of isotropic log-concave functions, highlighting the central role of the gradient of the logarithmic potential in high-dimensional convexity.

math.MG↗

arXiv2026

Banach-Mazur distances and basis constants of isotropic log-concave random spaces

We study the Banach-Mazur distance between random normed spaces generated by centrally symmetric random polytopes associated with isotropic log-concave measures in $\mathbb{R}^n$. We show that, in a wide range of parameters, if $x_1,\dots,x_m$ and $y_1,\dots,y_m$ are independent samples from an isotropic log-concave probability measure on $\mathbb{R}^n$, then the corresponding normed spaces $X_{B_m}$ and $Y_{A_m}$ generated by their absolute convex hulls satisfy, with high probability, $$d_{{\rm BM}}(X_{B_m},Y_{A_m}) \geqslant \frac{cn}{\ln(1+m/n)},$$ which is sharp in both $n$ and $m$ and recovers the extremal order $n$ when $m \approx n$. Our results extend Gluskin's theorem from the Gaussian setting to general isotropic log-concave measures, providing evidence for a universality phenomenon in the extremal geometry of the Banach-Mazur compactum. In addition, we investigate operator-theoretic properties of the associated random spaces and, as consequences, we derive sharp estimates for their basis constant and show that these random spaces are far from the class of spaces with a $1$-unconditional basis. The proofs combine probabilistic and geometric methods with recent advances related to Bourgain's slicing problem.

math.FA↗

arXiv2026

On the maximal perimeter of isotropic log-concave probability measures

We study the maximal perimeter constant of isotropic log-concave probability measures on $\mathbb{R}^n$. For a measure $\mu$, this quantity, denoted by $\Gamma(\mu)$, is defined as the supremum of the $\mu$-perimeter over all convex bodies and measures the largest possible boundary contribution of convex sets with respect to $\mu$. Let $$\Gamma_n := \sup\{\Gamma(\mu) : \mu \text{ is an isotropic log-concave probability measure on } \mathbb{R}^n\}.$$ We prove that $\Gamma_n \leqslant Cn^{3/2}$, where $C>0$ is an absolute constant. This result improves the previously known $O(n^2)$ upper bound. Under additional structural assumptions, we obtain sharp linear bounds of order $O(n)$.

math.MG↗

arXiv2025

Regular functional covering numbers

We establish the existence of a regular functional $M$-position, in the sense of Pisier, for geometric log-concave functions. This provides a functional analogue of Pisier's regular $M$-positions for convex bodies and yields uniform control of covering numbers at all scales. Specifically, we show that every isotropic geometric log-concave function $f:\mathbb{R}^n \to [0,\infty)$ satisfies, for all $t\geq 1$, $$\max \left\{N(f, t \cdot g),\,N(f^*, t \cdot g),\,N(g, t \cdot f),\,N(g, t \cdot f^*)\right\} \leq \exp\left( \frac{\gamma_n^2\, n}{t} \right),$$ where $f^*$ denotes the Legendre dual of $f$, $(t \cdot f)(x)=f(x/t)$ is the $t$-homothety of $f$, $g(x)=\exp \left(-\frac{1}{2}|x|^{2}\right)$ and $\gamma_n \leq c(\ln n)^2$. Our result shows that the isotropic position of a log-concave function already provides an almost $1$-regular functional $M$-position.

math.MG↗

arXiv2025

Moments of the Cram\'er transform of log-concave probability measures

Let $\mu$ be a centered log-concave probability measure on ${\mathbb R}^n$ and let $\Lambda_{\mu}^{\ast}$ denote the Cram\'{e}r transform of $\mu$, i.e. $\Lambda_{\mu}^{\ast}(x)=\sup\{\langle x,\xi\rangle-\Lambda_{\mu}(\xi):\xi\in\mathbb{R}^n\}$ where $\Lambda_{\mu}$ is the logarithmic Laplace transform of $\mu$. We show that $\mathbb{E}_{\mu}\left[\exp\left(\frac{c_1}{n}\Lambda_{\mu}^{\ast }\right)\right]<\infty $ where $c_1>0$ is an absolute constant. In, particular, $\Lambda_{\mu}^{\ast}$ has finite moments of all orders. The proof, which is based on the comparison of certain families of convex bodies associated with $\mu$, implies that $\|\Lambda_{\mu}^{\ast}\|_{L^2(\mu)}\leqslant c_2n\ln n$. The example of the uniform measure on the Euclidean ball shows that this estimate is optimal with respect to $n$ as the dimension $n$ grows to infinity.

math.MG↗

arXiv2023

Half-space depth of log-concave probability measures

Given a probability measure $μ$ on ${\mathbb R}^n$, Tukey's half-space depth is defined for any $x\in {\mathbb R}^n$ by $φ_{μ}(x)=\inf\{μ(H):H\in {\cal H}(x)\}$, where ${\cal H}(x)$ is the set of all half-spaces $H$ of ${\mathbb R}^n$ containing $x$. We show that if $μ$ is log-concave then $$e^{-c_1n}\leq \int_{\mathbb{R}^n}φ_{μ}(x)\,dμ(x) \leq e^{-c_2n/L_μ^2}$$ where $L_{μ}$ is the isotropic constant of $μ$ and $c_1,c_2>0$ are absolute constants. The proofs combine large deviations techniques with a number of facts from the theory of $L_q$-centroid bodies of log-concave probability measures. The same ideas lead to general estimates for the expected measure of random polytopes whose vertices have a log-concave distribution.

math.PR↗

arXiv2023

Threshold for the expected measure of random polytopes

Let $μ$ be a log-concave probability measure on ${\mathbb R}^n$ and for any $N>n$ consider the random polytope $K_N={\rm conv}\{X_1,\ldots ,X_N\}$, where $X_1,X_2,\ldots $ are independent random points in ${\mathbb R}^n$ distributed according to $μ$. We study the question if there exists a threshold for the expected measure of $K_N$. Our approach is based on the Cramer transform $Λ_μ^{\ast }$ of $μ$. We examine the existence of moments of all orders for $Λ_μ^{\ast }$ and establish, under some conditions, a sharp threshold for the expectation ${\mathbb E}_{μ^N}[μ(K_N)]$ of the measure of $K_N$: it is close to $0$ if $\ln N\ll {\mathbb E}_{μ}(Λ_μ^{\ast })$ and close to $1$ if $\ln N\gg {\mathbb E}_{μ}(Λ_μ^{\ast })$. The main condition is that the parameter $β(μ)={\rm Var}_{μ}(Λ_μ^{\ast })/({\mathbb E}_{μ}(Λ_{μ}^{\ast }))^2$ should be small.

math.PR↗

arXiv2023

Inequalities for sections and projections of convex bodies

This article belongs to the area of geometric tomography, which is the study of geometric properties of solids based on data about their sections and projections. We describe a new direction in geometric tomography where different volumetric results are considered in a more general setting, with volume replaced by an arbitrary measure. Surprisingly, such a general approach works for a number of volumetric results. In particular, we discuss the Busemann-Petty problem on sections of convex bodies for arbitrary measures and the slicing problem for arbitrary measures. We present generalizations of these questions to the case of functions. A number of generalizations of questions related to projections, such as the problem of Shephard, are also discussed as well as some questions in discrete tomography.

math.FA↗

arXiv2021

Inequalities for the Radon transform on convex sets

Several years ago the authors started looking at some problems of convex geometry from a more general point of view, replacing volume by an arbitrary measure. This approach led to new general properties of the Radon transform on convex bodies including an extension of the Busemann-Petty problem and a slicing inequality for arbitrary functions. The latter means that the sup-norm of the Radon transform of any probability density on a convex body of volume one is bounded from below by a positive constant depending only on the dimension. In this note, we prove an inequality that serves as an umbrella for these results

math.MG↗

arXiv2019

Norms of weighted sums of log-concave random vectors

Let $C$ and $K$ be centrally symmetric convex bodies of volume $1$ in ${\mathbb R}^n$. We provide upper bounds for the multi-integral expression \begin{equation*}\|{\bf t}\|_{C^s,K}=\int_{C}\cdots\int_{C}\Big\|\sum_{j=1}^st_jx_j\Big\|_K\,dx_1\cdots dx_s\end{equation*} in the case where $C$ is isotropic. Our approach provides an alternative proof of the sharp lower bound, due to Gluskin and V. Milman, for this quantity. We also present some applications to "randomized" vector balancing problems.

math.MG↗

arXiv2016

Geometry of random sections of isotropic convex bodies

Let $K$ be an isotropic symmetric convex body in ${\mathbb R}^n$. We show that a subspace $F\in G_{n,n-k}$ of codimension $k=γn$, where $γ\in (1/\sqrt{n},1)$, satisfies $$K\cap F\subseteq \frac{c}{γ}\sqrt{n}L_K (B_2^n\cap F)$$ with probability greater than $1-\exp (-\sqrt{n})$. Using a different method we study the same question for the $L_q$-centroid bodies $Z_q(μ)$ of an isotropic log-concave probability measure $μ$ on ${\mathbb R}^n$. For every $1\leq q\leq n$ and $γ\in (0,1)$ we show that a random subspace $F\in G_{n,(1-γ)n}$ satisfies $Z_q(μ)\cap F\subseteq c_2(γ)\sqrt{q}\,B_2^n\cap F$. We also give bounds on the diameter of random projections of $Z_q(μ)$ and using them we deduce that if $K$ is an isotropic convex body in ${\mathbb R}^n$ then for a random subspace $F$ of dimension $(\log n)^4$ one has that all directions in $F$ are sub-Gaussian with constant $O(\log^2n)$.

math.MG↗

arXiv2016

Volume difference inequalities

We prove several inequalities estimating the distance between volumes of two bodies in terms of the maximal or minimal difference between areas of sections or projections of these bodies. We also provide extensions in which volume is replaced by an arbitrary measure.

math.MG↗

arXiv2016

On the average volume of sections of convex bodies

The average section functional ${\rm as}(K)$ of a centered convex body in ${\mathbb R}^n$ is the average volume of central hyperplane sections of $K$: \begin{equation*}{\rm as}(K)=\int_{S^{n-1}}|K\cap ξ^{\perp }|\,dσ(ξ).\end{equation*} We study the question if there exists an absolute constant $C>0$ such that for every $n$, for every centered convex body $K$ in ${\mathbb R}^n$ and for every 0<k<n, $${\rm as}(K)\ls C^k|K|^{\frac{k}{n}}\,\max_{E\in {\rm Gr}_{n-k}}{\rm as}(K\cap E).$$ We observe that the case $k=1$ is equivalent to the hyperplane conjecture. We show that this inequality holds true in full generality if one replaces $C$ by $CL_K$ or $Cd_{\rm ovr}(K,{\cal{BP}}_k^n)$, where $L_K$ is the isotropic constant of $K$ and $d_{\rm ovr}(K,{\cal{BP}}_k^n)$ is the outer volume ratio distance from $K$ to the class ${\cal{BP}}_k^n$ of generalized $k$-intersection bodies. We also compare ${\rm as}(K)$ to the average of ${\rm as}(K\cap E)$ over all $k$-codimensional sections of $K$. We examine separately the dependence of the constants on the dimension in the case where $K$ is in some of the classical positions as well as the natural lower dimensional analogue of the average section functional.

math.MG↗

arXiv2016

$M$-estimates for isotropic convex bodies and their $L_q$-centroid bodies

Let $K$ be a centrally-symmetric convex body in $\mathbb{R}^n$ and let $\|\cdot\|$ be its induced norm on ${\mathbb R}^n$. We show that if $K \supseteq r B_2^n$ then: \[ \sqrt{n} M(K) \leqslant C \sum_{k=1}^{n} \frac{1}{\sqrt{k}} \min\left(\frac{1}{r} , \frac{n}{k} \log\Big(e + \frac{n}{k}\Big) \frac{1}{v_{k}^{-}(K)}\right) . \] where $M(K)=\int_{S^{n-1}} \|x\|\, dσ(x)$ is the mean-norm, $C>0$ is a universal constant, and $v^{-}_k(K)$ denotes the minimal volume-radius of a $k$-dimensional orthogonal projection of $K$. We apply this result to the study of the mean-norm of an isotropic convex body $K$ in ${\mathbb R}^n$ and its $L_q$-centroid bodies. In particular, we show that if $K$ has isotropic constant $L_K$ then: \[ M(K) \leqslant \frac{C\log^{2/5}(e+ n)}{\sqrt[10]{n}L_K} . \]

math.FA↗

arXiv2016

Inequalities for the surface area of projections of convex bodies

We provide general inequalities that compare the surface area S(K) of a convex body K in ${\mathbb R}^n$ to the minimal, average or maximal surface area of its hyperplane or lower dimensional projections. We discuss the same questions for all the quermassintegrals of K. We examine separately the dependence of the constants on the dimension in the case where K is in some of the classical positions or K is a projection body. Our results are in the spirit of the hyperplane problem, with sections replaced by projections and volume by surface area.

math.MG↗

arXiv2016

Variants of the Busemann-Petty problem and of the Shephard problem

We provide an affirmative answer to a variant of the Busemann-Petty problem, proposed by V.~Milman: Let $K$ be a convex body in ${\mathbb R}^n$ and let $D$ be a compact subset of ${\mathbb R}^n$ such that, for some $1\ls k\ls n-1$, $$|P_F(K)|\ls |D\cap F|$$ for all $F\in G_{n,k}$, where $P_F(K)$ is the orthogonal projection of $K$ onto $F$ and $D\cap F$ is the intersection of $D$ with $F$. Then, $$|K|\ls |D|.$$ We also provide estimates for the lower dimensional Busemann-Petty and Shephard problems, and we prove separation in the original Busemann-Petty problem.

math.MG↗

arXiv2016

Asymptotic shape of the convex hull of isotropic log-concave random vectors

Let $x_1,\ldots ,x_N$ be independent random points distributed according to an isotropic log-concave measure $μ$ on ${\mathbb R}^n$, and consider the random polytope $$K_N:={\rm conv}\{ \pm x_1,\ldots ,\pm x_N\}.$$ We provide sharp estimates for the quermaßintegrals and other geometric parameters of $K_N$ in the range $cn\ls N\ls\exp (n)$; these complement previous results from \cite{DGT1} and \cite{DGT} that were given for the range $cn\ls N\ls\exp (\sqrt{n})$. One of the basic new ingredients in our work is a recent result of E.~Milman that determines the mean width of the centroid body $Z_q(μ)$ of $μ$ for all $1\ls q\ls n$.

math.MG↗