Euclidean Partitions Optimizing Noise Stability

TL;DR

This paper proves the Standard Simplex Conjecture for certain parameters, identifying optimal partitions of Euclidean space that maximize noise stability, with implications for computer science and geometric problems.

Contribution

The paper confirms the Standard Simplex Conjecture for specific cases, advancing understanding of optimal Gaussian partitions and their applications.

Findings

Proves the conjecture for k=3, n≥2, and small ρ

Identifies optimal Euclidean partitions for noise stability

Links conjecture to applications in computer science and geometry

Abstract

The Standard Simplex Conjecture of Isaksson and Mossel asks for the partition $\{A_{i}\}_{i=1}^{k}$ of $\mathbb{R}^{n}$ into $k\leq n+1$ pieces of equal Gaussian measure of optimal noise stability. That is, for $\rho>0$, we maximize $$ \sum_{i=1}^{k}\int_{\mathbb{R}^{n}}\int_{\mathbb{R}^{n}}1_{A_{i}}(x)1_{A_{i}}(x\rho+y\sqrt{1-\rho^{2}}) e^{-(x_{1}^{2}+\cdots+x_{n}^{2})/2}e^{-(y_{1}^{2}+\cdots+y_{n}^{2})/2}dxdy. $$ Isaksson and Mossel guessed the best partition for this problem and proved some applications of their conjecture. For example, the Standard Simplex Conjecture implies the Plurality is Stablest Conjecture. For $k=3,n\geq2$ and $0<\rho<\rho_{0}(k,n)$, we prove the Standard Simplex Conjecture. The full conjecture has applications to theoretical computer science, and to geometric multi-bubble problems (after Isaksson and Mossel).