An Efficient Linear Programming Algorithm to Generate the Densest Lattice Sphere Packings

TL;DR

This paper demonstrates an efficient linear programming-based algorithm for generating the densest lattice sphere packings in dimensions 2 through 19, significantly outperforming previous methods in speed and reliability.

Contribution

The paper introduces the Torquato-Jiao packing algorithm, a linear programming approach that efficiently finds densest lattice sphere packings in multiple dimensions, surpassing prior techniques in speed.

Findings

TJ algorithm is up to 1000 times faster in some dimensions.

Successfully reproduces known densest packings for dimensions 2-19.

Provides insights into the density landscape and local maxima.

Abstract

Finding the densest sphere packing in $d$-dimensional Euclidean space $\mathbb{R}^d$ is an outstanding fundamental problem with relevance in many fields, including the ground states of molecular systems, colloidal crystal structures, coding theory, discrete geometry, number theory, and biological systems. Numerically generating the densest sphere packings becomes very challenging in high dimensions due to an exponentially increasing number of possible sphere contacts and sphere configurations, even for the restricted problem of finding the densest lattice sphere packings. In this paper, we apply the Torquato-Jiao packing algorithm, which is a method based on solving a sequence of linear programs, to robustly reproduce the densest known lattice sphere packings for dimensions 2 through 19. We show that the TJ algorithm is appreciably more efficient at solving these problems than previously published methods. Indeed, in some dimensions, the former procedure can be as much as three orders of magnitude faster at finding the optimal solutions than earlier ones. We also study the suboptimal local density-maxima solutions (inherent structures or "extreme" lattices) to gain insight about the nature of the topography of the "density" landscape.