Generalized Dual Sudakov Minoration via Dimension Reduction - A Program
Shahar Mendelson, Emanuel Milman, Grigoris Paouris

TL;DR
This paper introduces a program to extend the dual Sudakov Minoration to a broader class of log-concave measures using dimension reduction techniques, establishing new bounds and conjectures.
Contribution
It proposes a novel dimension-reduction approach to generalize dual Sudakov Minoration for log-concave measures, including a new conjectural program and partial results for specific bodies.
Findings
Established Weak Generalized Dual Sudakov Minoration for various measures
Fully proved dimension-reduction for ellipsoids and cubes
Derived a new proof of Sudakov Minoration
Abstract
We propose a program for establishing a conjectural extension to the class of (origin-symmetric) log-concave probability measures , of the classical dual Sudakov Minoration on the expectation of the supremum of a Gaussian process: \begin{equation} \label{eq:abstract} M(Z_p(\mu), C \int ||x||_K d\mu \cdot K) \leq \exp(C p) \;\;\, \forall p \geq 1 . \end{equation} Here is an origin-symmetric convex body, is the -centroid body associated to , is the packing-number of in , and is a universal constant. The Program consists of first establishing a Weak Generalized Dual Sudakov Minoration, involving the dimension of the ambient space, which is then self-improved to a dimension-free estimate after applying a dimension-reduction step. The latter step may be thought of as a conjectural "small-ball one-sided" variant of the…
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Taxonomy
TopicsPoint processes and geometric inequalities · Morphological variations and asymmetry
