Approximate Message Passing for sparse matrices with application to the equilibria of large ecological Lotka-Volterra systems

TL;DR

This paper develops an AMP algorithm for sparse matrices and applies it to analyze the distribution of stable equilibria in large ecological Lotka-Volterra systems, revealing a mixture of truncated Gaussians.

Contribution

It introduces a novel AMP approach for sparse matrices and applies it to characterize equilibrium distributions in large ecological models.

Findings

AMP algorithms adapted for sparse matrices with variance profiles.

Distribution of stable equilibria approximated by a mixture of truncated Gaussians.

Theoretical framework linking AMP and ecological equilibrium analysis.

Abstract

This paper is divided into two parts. The first part is devoted to the study of a class of Approximate Message Passing (AMP) algorithms which are widely used in the fields of statistical physics, machine learning, or communication theory. The AMP algorithms studied in this part are those where the measurement matrix has independent elements, up to the symmetry constraint when this matrix is symmetric, with a variance profile that can be sparse. The AMP problem is solved by adapting the approach of Bayati, Lelarge, and Montanari (2015) to this matrix model. \\ The Lotka-Volterra (LV) model is the standard model for studying the dynamical behavior of large dimensional ecological food chains. The second part of this paper is focused on the study of the statistical distribution of the globally stable equilibrium vector of a LV system in the situation where the random symmetric interaction matrix among the living species is sparse, and in the regime of large dimensions. This equilibrium vector is the solution of a Linear Complementarity Problem, which distribution is shown to be characterized through the AMP approach developed in the first part. In the large dimensional regime, this distribution is close to a mixture of a large number of truncated Gaussians.