The large and moderate deviations approach in geometric functional analysis
Joscha Prochno

TL;DR
This paper surveys the development of large and moderate deviations principles in geometric functional analysis, highlighting their connections to classical results and recent advances in high-dimensional probability.
Contribution
It introduces the fundamental ideas, reviews existing research, and aims to make the large and moderate deviations approach more accessible to researchers.
Findings
Connections to Kannan-Lovász-Simonovits conjecture
Insights into isotropic log-concave vectors
Integration of large deviations with geometric analysis
Abstract
The work of Gantert, Kim, and Ramanan [Large deviations for random projections of balls, Ann. Probab. 45 (6B), 2017] has initiated and inspired a new direction of research in the asymptotic theory of geometric functional analysis. The moderate deviations perspective, describing the asymptotic behavior between the scale of a central limit theorem and a large deviations principle, was later added by Kabluchko, Prochno, and Th\"ale in [High-dimensional limit theorems for random vectors in balls. II, Commun. Contemp. Math. 23(3), 2021]. These two approaches nicely complement the classical study of central limit phenomena or non-asymptotic concentration bounds for high-dimensional random geometric quantities. Beyond studying large and moderate deviations principles for random geometric quantities that appear in geometric functional analysis, other ideas emerged from the…
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Taxonomy
TopicsAnalytical Chemistry and Chromatography · Chemical Thermodynamics and Molecular Structure · Crystallization and Solubility Studies
