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Galyna V. Livshyts

Publications and source records attributed to Galyna V. Livshyts.

20 records · Page 2Linked to original sources

An extension of Minkowski's theorem and its applications to questions about projections for measures

Minkowski's Theorem asserts that every centered measure on the sphere which is not concentrated on a great subsphere is the surface area measure of some convex body, and, moreover, the surface area measure determines a convex body uniquely. In this manuscript we prove an extension of Minkowski's theorem. Consider a measure $μ$ on $\mathbb{R}^n$ with positive degree of concavity and positive degree of homogeneity. We show that a surface area measure of a convex set $K$, weighted with respect to $μ$, determines a convex body uniquely up to $μ$-measure zero. We also establish an existence result under natural conditions including symmetry. We apply this result to extend the solution to classical Shephard's problem, which asks the following: if one convex body in $\mathbb{R}^n$ has larger projections than another convex body in every direction, does it mean that the volume of the first convex body is also greater? The answer to this question is affirmative when $n\leq 2$ and negative when $n\geq 3.$ In this paper we introduce a new notion which relates projections of convex bodies to a given measure $μ$, and is a direct generalization of the Lebesgue area of a projection. Using this notion we state a generalization of the Shephard problem to measures and prove that the answer is affirmative for $n\leq 2$ and negative for $n\geq 3$ for measures which have a positive degree of homogeneity and a positive degree of concavity. We also prove stability and separation results, and establish useful corollaries. Finally, we describe two types of uniqueness results which follow from the extension of Minkowski's theorem.

math.CA↗

Maximal surface area of polytopes with respect to log-concave rotation invariant measures

It was shown in \cite{GL} that the maximal surface area of a convex set in $\mathbb{R}^n$ with respect to a rotation invariant log-concave probability measure $γ$ is of order $\frac{\sqrt{n}}{\sqrt[4]{Var|X|} \sqrt{\mathbb{E}|X|}}$, where $X$ is a random vector in $\mathbb{R}^n$ distributed with respect to $γ$. In the present paper we discuss surface area of convex polytopes $P_K$ with $K$ facets. We find tight bounds on the maximal surface area of $P_K$ in terms of $K$. We show that $γ(\partial P_K)\lesssim \frac{\sqrt{n}}{\mathbb{E}|X|}\cdot\sqrt{\log K}\cdot\log n$ for all $K$. This bound is better then the general bound for all $K\in [2,e^{\frac{c}{\sqrt{Var|X|}}}]$. Moreover, for all $K$ in that range the bound is exact up to a factor of $\log n$: for each $K\in [2,e^{\frac{c}{\sqrt{Var|X|}}}]$ there exists a polytope $P_K$ with at most $K$ facets such that $γ(\partial P_K)\gtrsim \frac{\sqrt{n}}{\mathbb{E}|X|}\sqrt{\log K}.$ %For the measures $γ_p$ with densities $C_{n,p} e^{-\frac{|y|^p}{p}}$ (where $p>0$) we obtain: $γ_p(\partial P_K)\lesssim \frac{\sqrt{n}}{\mathbb{E}X}\sqrt{\log K},$ which was obtained for the standard Gaussian measure $γ_2$ by F. Nazarov.

math.CA↗